diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..2d102de --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +gEconpy/_version.py export-subst diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..814d1ea --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,47 @@ +name: release-pipeline + +on: + release: + types: + - created + +jobs: + release-job: + runs-on: ubuntu-latest + env: + PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} + steps: + - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: 3.12 + - name: Install release tooling + run: | + pip install twine wheel numpy setuptools versioneer + - name: Build package + run: | + python setup.py sdist bdist_wheel + - name: Check version number match + run: | + echo "GITHUB_REF: ${GITHUB_REF}" + # The GITHUB_REF should be something like "refs/tags/v1.2.3" + # Make sure the package version is the same as the tag + grep -Rq "^Version: ${GITHUB_REF:11}$" gEconpy.egg-info/PKG-INFO + - name: Publish to PyPI + run: | + twine check dist/* + twine upload --repository pypi --username __token__ --password ${PYPI_TOKEN} dist/* + test-install-job: + needs: release-job + runs-on: ubuntu-latest + steps: + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: 3.7 + - name: Give PyPI a chance to update the index + run: sleep 240 + - name: Install from PyPI + run: | + pip install gEconpy==${GITHUB_REF:11} diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml index a1ba0f9..88e98f4 100644 --- a/.github/workflows/run_tests.yml +++ b/.github/workflows/run_tests.yml @@ -5,6 +5,15 @@ on: push: branches: [main] + +# Cancels all previous workflow runs for pull requests that have not completed. +concurrency: + # The concurrency group contains the workflow name and the branch name for pull requests + # or the commit hash for any other events. + group: ${{ github.workflow }}-${{ github.event_name == 'pull_request' && github.head_ref || github.sha }} + cancel-in-progress: true + + jobs: unittest: strategy: @@ -30,7 +39,7 @@ jobs: - uses: actions/cache@v3 env: # Increase this value to reset cache if geconpy_test.yml has not changed - CACHE_NUMBER: 0 + CACHE_NUMBER: 2 with: path: ~/conda_pkgs_dir key: ${{ runner.os }}-py${{matrix.python-version}}-conda-${{ env.CACHE_NUMBER }}-${{ @@ -39,7 +48,7 @@ jobs: uses: actions/cache@v3 env: # Increase this value to reset cache if requirements.txt has not changed - CACHE_NUMBER: 0 + CACHE_NUMBER: 2 with: path: | ~/.cache/pip @@ -49,13 +58,13 @@ jobs: hashFiles('requirements.txt') }} - uses: conda-incubator/setup-miniconda@v2 with: - miniforge-variant: Mambaforge + miniforge-variant: Miniforge3 miniforge-version: latest mamba-version: "*" activate-environment: geconpy-test channel-priority: strict environment-file: conda_envs/geconpy_test.yml - python-version: 3.12 + python-version: ${{matrix.python-version}} use-mamba: true use-only-tar-bz2: false # IMPORTANT: This may break caching of conda packages! See https://github.com/conda-incubator/setup-miniconda/issues/267 diff --git a/.gitignore b/.gitignore index a81cdc8..7fd7b1b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,11 @@ # Created by https://www.toptal.com/developers/gitignore/ +# Default ignored files +/shelf/ +/workspace.xml +# GitHub Copilot persisted chat sessions +/copilot/chatSessions + # Jetbrains stuff .idea/ diff --git a/.idea/.gitignore b/.idea/.gitignore deleted file mode 100644 index 26d3352..0000000 --- a/.idea/.gitignore +++ /dev/null @@ -1,3 +0,0 @@ -# Default ignored files -/shelf/ -/workspace.xml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 186d77a..9bc24b0 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -11,17 +11,11 @@ repos: args: [--branch, main] - id: trailing-whitespace -- repo: https://github.com/asottile/pyupgrade - rev: v3.16.0 - hooks: - - id: pyupgrade - args: [--py312-plus] - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.4.8 + rev: v0.5.5 hooks: - id: ruff - args: [ --fix, --exit-non-zero-on-fix ] + args: [ --fix, --unsafe-fixes, --exit-non-zero-on-fix ] - id: ruff-format types_or: [ python, pyi, jupyter ] @@ -32,7 +26,7 @@ repos: types: [python] exclude: | (?x)^ - |gEconpy/classes/model.py + |gEconpy/_version.py - repo: https://github.com/MarcoGorelli/absolufy-imports rev: v0.3.1 @@ -57,7 +51,7 @@ repos: - id: no-references-as-links name: Check no references that should be sphinx cross-references are urls description: >- - 'A quick check to prevent urls pointing to pymc docs or other sphinx built docs like arviz, numpy, scipy...' + 'A quick check to prevent urls pointing other sphinx built docs like pymc, arviz, numpy, scipy...' files: ^examples/.+\.ipynb$ exclude: > (?x)(index.md| diff --git a/GCN Files/Baxter_King_1993.gcn b/GCN Files/Baxter_King_1993.gcn new file mode 100644 index 0000000..e43b283 --- /dev/null +++ b/GCN Files/Baxter_King_1993.gcn @@ -0,0 +1,143 @@ +block STEADY_STATE +{ + identities + { + tau[ss] = tau_bar; + G_B[ss] = G_B_bar; + I_G[ss] = I_G_bar; + K_G[ss] = I_G[ss] / delta; + + r_G[ss] = 1 / beta; + r[ss] = (1 / beta - (1 - delta)) / (1 - tau[ss]); + w[ss] = (1 - theta_K) * (A_bar * K_G[ss] ^ theta_G) ^ (1 / (1 - theta_K)) * + (theta_K / r[ss]) ^ (theta_K / (1 - theta_K)); + Y[ss] = ((1 - tau[ss]) * w[ss] / theta_L + G_B[ss] + I_G[ss]) / + (1 + (1 - theta_K) / theta_L * (1 - tau[ss]) - delta * theta_K / r[ss]); + K[ss] = theta_K * Y[ss] / r[ss]; + N[ss] = (1 - theta_K) * Y[ss] / w[ss]; + + I[ss] = delta * theta_K * Y[ss] / r[ss]; + C[ss] = (1 - tau[ss]) / theta_L * (w[ss] - (1 - theta_K) * Y[ss]); + L[ss] = 1 - N[ss]; + + U[ss] = (1 / (1 - beta)) * (log(C[ss]) + theta_L * log(L[ss])); + lambda[ss] = 1 / C[ss]; + lambda_L[ss] = w[ss] * (1 - tau[ss]) / C[ss]; + + TC[ss] = -(w[ss] * N[ss] + r[ss] * K[ss]); + Div[ss] = Y[ss] + TC[ss]; + TR[ss] = tau[ss] * (w[ss] * N[ss] + r[ss] * K[ss]) - G_B[ss] - I_G[ss]; + }; +}; + +block HOUSEHOLD +{ + definitions + { + u[] = log(C[]) + theta_L * log(L[]); + }; + + objective + { + U[] = u[] + beta * E[][U[1]]; + }; + + controls + { + C[], I[], K[], L[], N[], B[]; + }; + + constraints + { + C[] + I[] + B[] / r_G[] = (1 - tau[]) * (w[] * N[] + r[] * K[-1]) + + B[-1] + Div[] + TR[] : lambda[]; + N[] + L[] = 1 : lambda_L[]; + K[] = (1 - delta) * K[-1] + I[]; + }; + + calibration + { + # Real rate = 6.5% + r_G[ss] = 1.065 -> beta; + delta = 0.025; + N[ss] = 1/3 -> theta_L; + }; +}; + + +block FIRM +{ + objective + { + TC[] = -(w[] * N[] + r[] * K[-1]); + }; + + controls + { + N[], K[-1]; + }; + + constraints + { + Y[] = A_bar * K[-1] ^ theta_K * N[] ^ (1 - theta_K) * K_G[] ^ theta_G: mc[]; + }; + + identities + { + mc[] = 1; + Div[] = Y[] + TC[]; + }; + + calibration + { + Y[ss] = 1 -> A_bar; + w[ss] = 2 -> theta_K; + theta_G = 0.1; + }; +}; + +block FISCAL_AUTHORITY +{ + definitions + { + spending[] = G_B[] + I_G[] + B[-1]; + income[] = tau[] * (w[] * N[] + r[] * K[-1]) + B[] / r_G[]; + }; + identities + { + # Fiscal policy rules + G_B[] - G_B_bar = rho_G_B * (G_B[-1] - G_B_bar) + epsilon_GB[]; + I_G[] - I_G_bar = rho_I_G * (I_G[-1] - I_G_bar) + epsilon_IG[]; + log(tau[] / tau_bar) = rho_tau * log(tau[-1] / tau_bar) + epsilon_tau[]; + + # Government budget constraint + TR[] = income[] - spending[]; + + # # Law of motion of public capital + K_G[] = (1 - delta) * K_G[-1] + I_G[]; + + # Zero net supply of bonds + B[] = 0; + }; + + shocks + { + epsilon_GB[], + epsilon_IG[], + epsilon_tau[]; + }; + + calibration + { + rho_G_B = 0.75; + rho_tau = 0.75; + rho_I_G = 0.75; + + # Y_ss is normalized to 1, so govt spending is 0.2 + 0.22 = 0.22 + # and Y[] = -TC[] = w[] * N[] + r[] * K[-1] = 1, so 0.22 taxes balances the budget. + G_B_bar = 0.2; + I_G_bar = 0.02; + tau_bar = 0.22; + + }; +}; diff --git a/GCN Files/RBC_complete.gcn b/GCN Files/RBC_complete.gcn index 9b87d0a..6af6c23 100644 --- a/GCN Files/RBC_complete.gcn +++ b/GCN Files/RBC_complete.gcn @@ -1,10 +1,3 @@ -options -{ - output logfile = FALSE; - output LaTeX = FALSE; -}; - - tryreduce { U[], TC[]; @@ -13,28 +6,22 @@ tryreduce block STEADY_STATE { - definitions - { - }; - identities { A[ss] = 1; - P[ss] = 1; - r[ss] = P[ss] * (1 / beta - (1 - delta)); - w[ss] = (1 - alpha) * P[ss] ^ (1 / (1 - alpha)) * (alpha / r[ss]) ^ (alpha / (1 - alpha)); + mc[ss] = 1; + r[ss] = (1 / beta - (1 - delta)); + w[ss] = (1 - alpha) * (alpha / r[ss]) ^ (alpha / (1 - alpha)); Y[ss] = (r[ss] / (r[ss] - delta * alpha)) ^ (sigma_C / (sigma_C + sigma_L)) * - (w[ss] / P[ss] * (w[ss] / P[ss] / (1 - alpha)) ^ sigma_L) ^ (1 / (sigma_C + sigma_L)); + (w[ss] * (w[ss] / (1 - alpha)) ^ sigma_L) ^ (1 / (sigma_C + sigma_L)); I[ss] = (delta * alpha / r[ss]) * Y[ss]; - C[ss] = Y[ss] ^ (-sigma_L / sigma_C) * ((1 - alpha) ^ (-sigma_L) * (w[ss] / P[ss]) ^ (1 + sigma_L)) ^ (1 / sigma_C); - K[ss] = alpha * Y[ss] * P[ss] / r[ss]; - L[ss] = (1 - alpha) * Y[ss] * P[ss] / w[ss]; - + C[ss] = Y[ss] ^ (-sigma_L / sigma_C) * ((1 - alpha) ^ (-sigma_L) * w[ss] ^ (1 + sigma_L)) ^ (1 / sigma_C); + K[ss] = alpha * Y[ss] * mc[ss] / r[ss]; + L[ss] = (1 - alpha) * Y[ss] * mc[ss] / w[ss]; U[ss] = (1 / (1 - beta)) * (C[ss] ^ (1 - sigma_C) / (1 - sigma_C) - L[ss] ^ (1 + sigma_L) / (1 + sigma_L)); - lambda[ss] = C[ss] ^ (-sigma_C) / P[ss]; - q[ss] = lambda[ss]; + lambda[ss] = C[ss] ^ (-sigma_C); TC[ss] = -(r[ss] * K[ss] + w[ss] * L[ss]); }; }; @@ -65,11 +52,11 @@ block HOUSEHOLD calibration { - beta = 0.99; - delta = 0.02; + beta ~ Beta(alpha=70, beta=4) = 0.99; + delta ~ Beta(alpha=2, beta=42) = 0.02; - sigma_C ~ N(loc=2.0, scale=2.0, lower=1.0) = 1.5; - sigma_L ~ N(loc=2.0, scale=2.0, lower=1.0) = 2.0; + sigma_C ~ Gamma(alpha=7, beta=3) = 1.5; + sigma_L ~ Gamma(alpha=7, beta=3) = 2.0; }; }; @@ -111,12 +98,11 @@ block TECHNOLOGY_SHOCKS shocks { - epsilon_A[] ~ N(mean=0, sd=sigma_epsilon_A); + epsilon_A[]; }; calibration { rho_A ~ Beta(mean=0.95, sd=0.04) = 0.95; - sigma_epsilon_A ~ Gamma(alpha=2, beta=0.1) = 0.05; }; }; diff --git a/GCN Files/RBC_priors.gcn b/GCN Files/RBC_priors.gcn index 9190c07..5dd7deb 100644 --- a/GCN Files/RBC_priors.gcn +++ b/GCN Files/RBC_priors.gcn @@ -1,14 +1,27 @@ -options +tryreduce { - output logfile = FALSE; - output LaTeX = FALSE; + U[], TC[]; }; -tryreduce +block STEADY_STATE { - U[], TC[]; + identities + { + A[ss] = 1; + r[ss] = (1 / beta - (1 - delta)); + w[ss] = (1 - alpha) * (alpha / r[ss]) ^ (alpha / (1 - alpha)); + Y[ss] = (r[ss] / (r[ss] - delta * alpha)) ^ (sigma_C / (sigma_C + sigma_L)) * + (w[ss] * (w[ss] / (1 - alpha)) ^ sigma_L) ^ (1 / (sigma_C + sigma_L)); + + I[ss] = (delta * alpha / r[ss]) * Y[ss]; + C[ss] = Y[ss] ^ (-sigma_L / sigma_C) * ((1 - alpha) ^ (-sigma_L) * w[ss] ^ (1 + sigma_L)) ^ (1 / sigma_C); + K[ss] = alpha * Y[ss] / r[ss]; + L[ss] = (1 - alpha) * Y[ss] / w[ss]; + lambda[ss] = C[ss] ^ (-sigma_C); + }; }; + block HOUSEHOLD { definitions diff --git a/GCN Files/RBC_steady_state.gcn b/GCN Files/RBC_steady_state.gcn index 17962ec..994f6ba 100644 --- a/GCN Files/RBC_steady_state.gcn +++ b/GCN Files/RBC_steady_state.gcn @@ -11,10 +11,6 @@ tryreduce block STEADY_STATE { - definitions - { - }; - identities { A[ss] = 1; diff --git a/GCN Files/RBC_two_household.gcn b/GCN Files/RBC_two_household.gcn new file mode 100644 index 0000000..51bac29 --- /dev/null +++ b/GCN Files/RBC_two_household.gcn @@ -0,0 +1,177 @@ +assumptions +{ + positive + { + Y[], K[], C_NR[], C_R[], + w[], r[], mc_L[], + L[], L_NR[], L_R[], + TFP[], + alpha, alpha_L, beta, sigma_C, sigma_L, delta; + }; +}; + +tryreduce +{ + U_NR[], U_R[], TC[]; +}; + +block STEADY_STATE +{ + definitions + { + f1[ss] = (r[ss] / (r[ss] - alpha * delta)); + f2[ss] = (alpha_L * (1 - alpha_L) * (1 - alpha)) ^ (-sigma_L / sigma_C); + f3[ss] = alpha_L ^ (sigma_L / sigma_C) + + (1 - alpha_L) ^ (sigma_L / sigma_C); + + }; + identities + { + TFP[ss] = 1.0; + shock_beta_R[ss] = 1.0; + + r[ss] = 1 / beta - (1 - delta); + w[ss] = (1 - alpha) * alpha_L ^ alpha_L * (1 - alpha_L) ^ (1 - alpha_L) * + (r[ss] / alpha) ^ (alpha / (alpha - 1)); + mc_L[ss] = w[ss] / alpha_L ^ alpha_L / (1 - alpha_L) ^ (1 - alpha_L); + Y[ss] = (f1[ss] * f2[ss] * f3[ss] * w[ss] ^ ((1 + sigma_L) / sigma_C)) ^ + (sigma_C / (sigma_L + sigma_C)); + + L[ss] = (1 - alpha) * Y[ss] / mc_L[ss]; + + L_R[ss] = alpha_L * L[ss] * mc_L[ss] / w[ss]; + L_NR[ss] = (1 - alpha_L) * L[ss] * mc_L[ss] / w[ss]; + + C_R[ss] = w[ss] ^ (1/sigma_C) * L_R[ss] ^ (-sigma_L / sigma_C); + C_NR[ss] = w[ss] ^ (1/sigma_C) * L_NR[ss] ^ (-sigma_L / sigma_C); + + K[ss] = alpha * Y[ss] / r[ss]; + I[ss] = delta * K[ss]; + + lambda_R[ss] = C_R[ss] ^ -sigma_C; + q[ss] = lambda_R[ss]; + lambda_NR[ss] = C_NR[ss] ^ -sigma_C; + + }; +}; + +block RICARDIAN_HOUSEHOLD +{ + definitions + { + u_R[] = shock_beta_R[] * (C_R[] ^ (1 - sigma_C) / (1 - sigma_C) - + L_R[] ^ (1 + sigma_L) / (1 + sigma_L)); + }; + + controls + { + C_R[], L_R[], I[], K[]; + }; + + objective + { + U_R[] = u_R[] + beta * E[][U_R[1]]; + }; + + constraints + { + @exclude + C_R[] + I[] = r[] * K[-1] + w[] * L_R[] : lambda_R[]; + + K[] = (1 - delta) * K[-1] + I[]: q[]; + }; + + identities + { + log(shock_beta_R[]) = rho_beta_R * log(shock_beta_R[-1]) + epsilon_beta_R[]; + }; + + shocks + { + epsilon_beta_R[]; + }; + + calibration + { + beta = 0.99; + delta = 0.02; + sigma_C = 1.5; + sigma_L = 2.0; + rho_beta_R = 0.95; + }; +}; + +block NON_RICARDIAN_HOUSEHOLD +{ + definitions + { + u_NR[] = (C_NR[] ^ (1 - sigma_C) / (1 - sigma_C) - + L_NR[] ^ (1 + sigma_L) / (1 + sigma_L)); + }; + + controls + { + C_NR[], L_NR[]; + }; + + objective + { + U_NR[] = u_NR[] + beta * E[][U_NR[1]]; + }; + + constraints + { + @exclude + C_NR[] = w[] * L_NR[]: lambda_NR[]; + }; +}; + + +block FIRM +{ + controls + { + K[-1], L[], L_R[], L_NR[]; + }; + + objective + { + TC[] = -(r[] * K[-1] + w[] * L_R[] + w[] * L_NR[]); + }; + + constraints + { + L[] = L_R[] ^ alpha_L * L_NR[] ^ (1 - alpha_L) : mc_L[]; + Y[] = TFP[] * K[-1] ^ alpha * L[] ^ (1 - alpha) : mc[]; + }; + + identities + { + # Perfect competition + mc[] = 1; + + # Exogenous technology process + log(TFP[]) = rho_TFP * log(TFP[-1]) + epsilon_TFP[]; + }; + + shocks + { + epsilon_TFP[]; + }; + + calibration + { + alpha = 0.35; + alpha_L = 0.5; + + rho_TFP = 0.95; + }; +}; + +block EQULIBRIUM +{ + identities + { + Y[] = C_R[] + C_NR[] + I[]; + }; +}; diff --git a/GCN Files/RBC_two_household_additive.gcn b/GCN Files/RBC_two_household_additive.gcn new file mode 100644 index 0000000..a05cfa4 --- /dev/null +++ b/GCN Files/RBC_two_household_additive.gcn @@ -0,0 +1,178 @@ +assumptions +{ + positive + { + Y[], K[], C_NR[], C_R[], + w[], r[], mc_L[], + L[], L_NR[], L_R[], + TFP[], + alpha, alpha_L, beta, sigma_C, sigma_L, delta; + }; +}; + +tryreduce +{ + U_NR[], U_R[], TC[]; +}; + +block STEADY_STATE +{ + definitions + { + # Capital/Labor ratio, N = K/L + N[ss] = (alpha * TFP[ss] / r[ss]) ^ (1 / (1 - alpha)); + + }; + identities + { + TFP[ss] = 1.0; + shock_beta_R[ss] = 1.0; + + r[ss] = 1 / beta - (1 - delta); + w[ss] = (1 - alpha) * N[ss] ^ alpha; + + C_R[ss] = (w[ss] / Theta_R) ^ (1 / sigma_R); + C_NR[ss] = (w[ss] / Theta_N) ^ (1 / sigma_N); + + C[ss] = omega * C_R[ss] + (1 - omega) * C_NR[ss]; + L[ss] = C[ss] / (N[ss] ^ alpha - delta * N[ss]); + L_NR[ss] = C_NR[ss] / w[ss]; + L_R[ss] = (L[ss] - (1 - omega) * L_NR[ss]) / omega; + + K[ss] = N[ss] * L[ss]; + I[ss] = delta * K[ss]; + Y[ss] = C[ss] + I[ss]; + + lambda_R[ss] = C_R[ss] ^ -sigma_R; + lambda_NR[ss] = C_NR[ss] ^ -sigma_N; + q[ss] = lambda_R[ss]; + }; +}; + +block RICARDIAN_HOUSEHOLD +{ + definitions + { + u_R[] = shock_beta_R[] * (C_R[] ^ (1 - sigma_R) / (1 - sigma_R) - Theta_R * L_R[]); + }; + + controls + { + C_R[], L_R[], I[], K[]; + }; + + objective + { + U_R[] = u_R[] + beta * E[][U_R[1]]; + }; + + constraints + { + @exclude + C_R[] + I[] = r[] * K[-1] + w[] * L_R[] : lambda_R[]; + + K[] = (1 - delta) * K[-1] + I[]: q[]; + }; + + identities + { + log(shock_beta_R[]) = rho_beta_R * log(shock_beta_R[-1]) + epsilon_beta_R[]; + }; + + shocks + { + epsilon_beta_R[]; + }; + + calibration + { + beta ~ Beta(alpha=70, beta=4) = 0.99; + delta ~ Beta(alpha=2, beta=42) = 0.02; + sigma_R ~ Gamma(alpha=7, beta=3) = 1.5; + Theta_R ~ Gamma(alpha=7, beta=3) = 1.0; + rho_beta_R ~ Beta(mean=0.95, sd=0.04) = 0.95; + }; +}; + +block NON_RICARDIAN_HOUSEHOLD +{ + definitions + { + u_NR[] = C_NR[] ^ (1 - sigma_N) / (1 - sigma_N) - Theta_N * L_NR[]; + }; + + controls + { + C_NR[], L_NR[]; + }; + + objective + { + U_NR[] = u_NR[] + beta * E[][U_NR[1]]; + }; + + constraints + { + C_NR[] = w[] * L_NR[]: lambda_NR[]; + }; + + calibration + { + Theta_N ~ Gamma(alpha=7, beta=3) = 1.0; + sigma_N ~ Gamma(alpha=7, beta=3) = 1.5; + }; +}; + + +block FIRM +{ + controls + { + K[-1], L[]; + }; + + objective + { + TC[] = -(r[] * K[-1] + w[] * L[]); + }; + + constraints + { + Y[] = TFP[] * K[-1] ^ alpha * L[] ^ (1 - alpha) : mc[]; + }; + + identities + { + # Perfect competition + mc[] = 1; + + # Exogenous technology process + log(TFP[]) = rho_TFP * log(TFP[-1]) + epsilon_TFP[]; + }; + + shocks + { + epsilon_TFP[]; + }; + + calibration + { + alpha ~ Beta(alpha=2, beta=5) = 0.35; + rho_TFP ~ Beta(mean=0.95, sd=0.04) = 0.95; + }; +}; + +block EQULIBRIUM +{ + identities + { + Y[] = C[] + I[]; + L[] = omega * L_R[] + (1 - omega) * L_NR[]; + C[] = omega * C_R[] + (1 - omega) * C_NR[]; + }; + + calibration + { + omega ~ Beta(alpha=2, beta=2) = 0.5; + }; +}; diff --git a/GCN Files/RBC_with_CES.gcn b/GCN Files/RBC_with_CES.gcn new file mode 100644 index 0000000..9500ca9 --- /dev/null +++ b/GCN Files/RBC_with_CES.gcn @@ -0,0 +1,127 @@ +tryreduce +{ + U[], TC[]; +}; + +assumptions +{ + positive + { + A[], Y[], C[], K[], L[], w[], r[], mc[], beta, delta, sigma_C, sigma_L, alpha, psi; + }; +}; + +block STEADY_STATE +{ + definitions + { + f1[ss] = r[ss] ^ (psi - 1) * alpha ^ ((1 - psi) / psi) * (A[ss] * mc[ss]) ^ (1 - psi); + N[ss] = ((f1[ss] - alpha ^ (1 / psi)) / (1 - alpha) ^ (1 / psi)) ^ (psi / (1 - psi)); + f2[ss] = alpha ^ (1 / psi) * N[ss] ^ ((psi - 1) / psi) + (1 - alpha) ^ (1 / psi); + }; + + identities + { + A[ss] = 1.0; + r[ss] = 1 / beta - (1 - delta); + mc[ss] = 1.0; + + w[ss] = (1 - alpha) ^ (1 / psi) * A[ss] * mc[ss] * f2[ss] ^ (1 / (psi - 1)); + + L[ss] = (w[ss] / Theta) ^ (1 / (sigma_L + sigma_C)) * + (A[ss] * f2[ss] ^ (psi / (psi - 1)) - delta * N[ss]) ^ (-sigma_C / (sigma_L + sigma_C)); + + K[ss] = N[ss] * L[ss]; + I[ss] = delta * K[ss]; + Y[ss] = A[ss] * (alpha ^ (1 / psi) * K[ss] ^ ((psi - 1) / psi) + + (1 - alpha) ^ (1 / psi) * L[ss] ^ ((psi - 1) / psi)) ^ (psi / (psi - 1)); + C[ss] = Y[ss] - I[ss]; + + lambda[ss] = C[ss] ^ (-sigma_C); + }; + +}; + +block HOUSEHOLD +{ + definitions + { + u[] = C[] ^ (1 - sigma_C) / (1 - sigma_C) - Theta * L[] ^ (1 + sigma_L) / (1 + sigma_L); + }; + + controls + { + C[], L[], I[], K[]; + }; + + objective + { + U[] = u[] + beta * E[][U[1]]; + }; + + constraints + { + C[] + I[] = r[] * K[-1] + w[] * L[] : lambda[]; + + K[] = (1 - delta) * K[-1] + I[]; + }; + + calibration + { + beta = 0.99; + delta = 0.02; + sigma_C = 1.5; + sigma_L = 2.0; + Theta = 1.0; + }; +}; + +block FIRM +{ + controls + { + K[-1], L[]; + }; + + objective + { + TC[] = -(r[] * K[-1] + w[] * L[]); + }; + + constraints + { + Y[] = A[] * (alpha ^ (1 / psi) * K[-1] ^ ((psi - 1) / psi) + + (1 - alpha) ^ (1 / psi) * L[] ^ ((psi - 1) / psi) + ) ^ (psi / (psi - 1)): mc[]; + }; + + identities + { + # Perfect competition + mc[] = 1; + }; + + calibration + { + alpha = 0.35; + psi = 0.6; + }; +}; + +block TECHNOLOGY_SHOCKS +{ + identities + { + log(A[]) = rho_A * log(A[-1]) + epsilon_A[]; + }; + + shocks + { + epsilon_A[]; + }; + + calibration + { + rho_A = 0.95; + }; +}; diff --git a/GCN Files/RBC_with_assumptions.gcn b/GCN Files/RBC_with_assumptions.gcn deleted file mode 100644 index 4c8b141..0000000 --- a/GCN Files/RBC_with_assumptions.gcn +++ /dev/null @@ -1,139 +0,0 @@ -options -{ - output logfile = FALSE; - output LaTeX = FALSE; -}; - -tryreduce -{ - U[], TC[]; -}; - -assumptions -{ - positive - { - C[], K[], L[], A[], I[], Y[], lambda[], w[], r[], mc[], - beta, delta, sigma_C, sigma_L, alpha, rho_A; - }; - - negative - { - TC[]; - }; - - real - { - C[], K[], L[], A[], I[], Y[], lambda[], w[], r[], mc[], - U[], u[], - beta, delta, sigma_C, sigma_L, alpha, rho_A, epsilon_A; - }; -}; - -block STEADY_STATE -{ - definitions - { - }; - - identities - { - # A[ss] = 1; - # P[ss] = 1; - # r[ss] = P[ss] * (1 / beta - (1 - delta)); - # w[ss] = (1 - alpha) * P[ss] ^ (1 / (1 - alpha)) * (alpha / r[ss]) ^ (alpha / (1 - alpha)); - # Y[ss] = (r[ss] / (r[ss] - delta * alpha)) ^ (sigma_C / (sigma_C + sigma_L)) * - # (w[ss] / P[ss] * (w[ss] / P[ss] / (1 - alpha)) ^ sigma_L) ^ (1 / (sigma_C + sigma_L)); - - # I[ss] = (delta * alpha / r[ss]) * Y[ss]; - # C[ss] = Y[ss] ^ (-sigma_L / sigma_C) * ((1 - alpha) ^ (-sigma_L) * (w[ss] / P[ss]) ^ (1 + sigma_L)) ^ (1 / sigma_C); - # K[ss] = alpha * Y[ss] * P[ss] / r[ss]; - # L[ss] = (1 - alpha) * Y[ss] * P[ss] / w[ss]; - - - # U[ss] = (1 / (1 - beta)) * (C[ss] ^ (1 - sigma_C) / (1 - sigma_C) - L[ss] ^ (1 + sigma_L) / (1 + sigma_L)); - # lambda[ss] = C[ss] ^ (-sigma_C); - # q[ss] = lambda[ss]; - # TC[ss] = -(r[ss] * K[ss] + w[ss] * L[ss]); - }; -}; - -block HOUSEHOLD -{ - definitions - { - u[] = C[] ^ (1 - sigma_C) / (1 - sigma_C) - L[] ^ (1 + sigma_L) / (1 + sigma_L); - }; - - controls - { - C[], L[], I[], K[]; - }; - - objective - { - U[] = u[] + beta * E[][U[1]]; - }; - - constraints - { - C[] + I[] = r[] * K[-1] + w[] * L[] : lambda[]; - K[] = (1 - delta) * K[-1] + I[]; - }; - - calibration - { - beta = 0.99; - delta = 0.02; - sigma_C = 1.5; - sigma_L = 2.0; - }; -}; - -block FIRM -{ - controls - { - K[-1], L[]; - }; - - objective - { - TC[] = -(r[] * K[-1] + w[] * L[]); - }; - - constraints - { - Y[] = A[] * K[-1] ^ alpha * L[] ^ (1 - alpha) : mc[]; - }; - - identities - { - # Perfect competition - mc[] = 1; - }; - - calibration - { - # L[ss] / K[ss] = 0.36 -> alpha; - alpha = 0.35; - }; -}; - -block TECHNOLOGY_SHOCKS -{ - identities - { - log(A[]) = rho_A * log(A[-1]) + epsilon_A[]; - }; - - shocks - { - epsilon_A[]; - }; - - calibration - { - rho_A = 0.95; - }; -}; diff --git a/GCN Files/skilled_unskilled_rbc.gcn b/GCN Files/skilled_unskilled_rbc.gcn new file mode 100644 index 0000000..797f3ce --- /dev/null +++ b/GCN Files/skilled_unskilled_rbc.gcn @@ -0,0 +1,134 @@ +block STEADY_STATE +{ + identities + { + A[ss] = 1.0; + Div[ss] = 0.0; + r_u[ss] = 1 / beta_u - (1 - delta_u); + r_s[ss] = 1 / beta_s - (1 - delta_s); + }; +}; + +block SKILLED_HOUSEHOLD +{ + definitions + { + u_s[] = log(C_s[]) - Theta_s * L_s[]; + }; + + objective + { + U_s[] = u_s[] + beta_s * E[][U_s[1]]; + }; + + controls + { + C_s[], L_s[], K_s[], I_s[]; + }; + + constraints + { + C_s[] + I_s[] = w_s[] * L_s[] + r_s[] * K_s[-1] + s * Div[]: lambda_s[]; + K_s[] = (1 - delta_s) * K_s[-1] + I_s[]; + }; + + calibration + { + beta_s = 0.99; + delta_s = 0.035; + Theta_s = 1; + s = 0.5; # Share of dividend that the skilled household gets (could be alpha_L ?) + }; +}; + +block UNSKILLED_HOUSEHOLD +{ + definitions + { + u_u[] = log(C_u[]) - Theta_u * L_u[]; + }; + + objective + { + U_u[] = u_u[] + beta_u * E[][U_u[1]]; + }; + + controls + { + C_u[], L_u[], K_u[], I_u[]; + }; + + constraints + { + C_u[] + I_u[] = w_u[] * L_u[] + r_u[] * K_u[-1] + (1 - s) * Div[]: lambda_u[]; + K_u[] = (1 - delta_u) * K_u[-1] + I_u[]; + }; + + calibration + { + beta_u = 0.99; + delta_u = 0.035; + Theta_u = 1; + }; +}; + + +block FIRM +{ + objective + { + TC[] = -(r_u[] * K_u[] + r_s[] * K_s[] + w_u[] * L_u[] + w_s[] * L_s[]); + }; + + controls + { + K_u[-1], K_s[-1], L_u[], L_s[], K[], L[]; + }; + + constraints + { + # Bundle labor -- skilled/unskilled are imperfect substitutes + L[] = (alpha_L ^ (1 / psi_L) * L_u[] ^ ((psi_L - 1) / psi_L) + + (1 - alpha_L) ^ (1 / psi_L) * L_s[] ^ ((psi_L - 1) / psi_L)) ^ + (psi_L / (psi_L - 1)); + + # Bundle capital -- perfect substitutes + K[] = K_u[-1] ^ alpha_K * K_s[-1] ^ (1 - alpha_K); + + # Production function + Y[] = A[] * K[] ^ alpha * L[] ^ (1 - alpha) : P[]; + }; + + identities + { + # Perfect competition + P[] = 1; + Div[] = Y[] * P[] + TC[]; + }; + + calibration + { + alpha_L = 0.5; # share of unskilled labor in economy + alpha_K = 0.5; # share of capital stock owned by unskilled household + psi_L = 3.0; # Elasticity of substitution btwn skilled & unskilled, psi_L -> oo implies perfect substitutes + alpha = 0.66; # Share of capital in production + }; +}; + +block TECHNOLOGY +{ + identities + { + log(A[]) = rho * log(A[-1]) + epsilon_A[]; + }; + + shocks + { + epsilon_A[]; + }; + + calibration + { + rho = 0.95; + }; +}; diff --git a/README.md b/README.md index 9b7040e..26b27c1 100644 --- a/README.md +++ b/README.md @@ -200,7 +200,8 @@ To see how to do simulations, IRFs, and compute moments, see the example noteboo Since Dynare is still the gold standard in DSGE modeling, and this is a wacky open source package written by a literally who?, gEconpy has the ability to automatically convert a solved model into a Dynare mod file. This is done as follows: ```python -from gEconpy.shared.dynare_convert import make_mod_file +from gEconpy.dynare_convert import make_mod_file + print(make_mod_file(model)) ``` diff --git a/codecov.yml b/codecov.yml index 3ce1152..abdd455 100644 --- a/codecov.yml +++ b/codecov.yml @@ -21,8 +21,6 @@ coverage: ignore: - "gEconpy/tests/*" - - "gEconpy/examples/*" - - "gEconpy/GCN FIles/*" comment: layout: "reach, diff, flags, files" diff --git a/conda_envs/environment.yml b/conda_envs/environment.yml index 7f512bc..9c22ea8 100644 --- a/conda_envs/environment.yml +++ b/conda_envs/environment.yml @@ -6,7 +6,6 @@ dependencies: - python=3.12 - pymc - pytensor - - emcee - joblib - matplotlib - numba @@ -16,8 +15,10 @@ dependencies: - scipy - setuptools - statsmodels - - sympy + - preliz + - sympy<1.13 - pip: - sympytensor - gEconpy + - better_optimize diff --git a/conda_envs/environment_dev.yml b/conda_envs/environment_dev.yml index 6fbb981..2444d19 100644 --- a/conda_envs/environment_dev.yml +++ b/conda_envs/environment_dev.yml @@ -2,13 +2,13 @@ name: geconpy-dev channels: - conda-forge - nvidia + - nodefaults dependencies: # Core dependencies - python=3.12 - pymc - pytensor - - emcee - joblib - matplotlib - numba @@ -18,13 +18,10 @@ dependencies: - scipy - setuptools - statsmodels - - sympy + - sympy<1.13 + - preliz - pip - # GPU stuff, optional for now - - jaxlib=*=*cuda* - - cuda-nvcc - # JAX, optional for now - jax - numpyro @@ -54,4 +51,6 @@ dependencies: - pip: - sympytensor - - bumpver + - pymc-experimental + - better_optimize + - numdifftools diff --git a/conda_envs/environment_docs.yml b/conda_envs/environment_docs.yml index e9554e8..4d9e4bb 100644 --- a/conda_envs/environment_docs.yml +++ b/conda_envs/environment_docs.yml @@ -7,7 +7,6 @@ dependencies: - pip - pymc - pytensor - - emcee - joblib - matplotlib - numba @@ -17,7 +16,8 @@ dependencies: - scipy - setuptools - statsmodels - - sympy + - preliz + - sympy<1.13 # Extra dependencies for docs build - ipython diff --git a/conda_envs/geconpy_test.yml b/conda_envs/geconpy_test.yml index 89eb85f..970b1a8 100644 --- a/conda_envs/geconpy_test.yml +++ b/conda_envs/geconpy_test.yml @@ -1,23 +1,33 @@ -name: geconpy-test +name: gEconpy channels: - conda-forge -- defaults dependencies: # Base dependencies - numpy - numba - scipy - - sympy + - sympy<1.13 - pyparsing - pandas - xarray - matplotlib - joblib - - emcee - arviz - statsmodels + - pymc + - pytensor + - preliz + # Testing dependencies - pre-commit - pytest-cov>=2.5 - pytest>=3.0 - pytest-env + + - pip + - pip: + - sympytensor + - better-optimize + - pymc-experimental + - better_optimize + - numdifftools diff --git a/docs/source/examples/introductory/time_aware_symbol.ipynb b/docs/source/examples/introductory/time_aware_symbol.ipynb new file mode 100644 index 0000000..df0a595 --- /dev/null +++ b/docs/source/examples/introductory/time_aware_symbol.ipynb @@ -0,0 +1,1529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1b7c3ea5", + "metadata": {}, + "source": [ + "# Time Aware Symbols\n", + "\n", + "The `TimeAwareSymbol` object is an extension of `sympy.Symbol`. It is an important building block of DSGE models. This short tutorial shows what they are, and how they can be used. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e7da03af", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.append(\"/Users/jessegrabowski/Documents/Python/gEconpy/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3f7366c6", + "metadata": {}, + "outputs": [], + "source": [ + "from gEconpy.classes.time_aware_symbol import TimeAwareSymbol\n", + "import sympy as sp" + ] + }, + { + "cell_type": "markdown", + "id": "ab37d8e0", + "metadata": {}, + "source": [ + "## Basic Functionality\n", + "\n", + "A `TimeAwareSymbol` functions exactly like `sp.Symbol`, except that it accepts a `time_index` argument. `time_index` is an integer that gives an offset from time `t`. Here are three examples:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "01b55186", + "metadata": {}, + "outputs": [], + "source": [ + "x_t = TimeAwareSymbol(\"x\", time_index=0)\n", + "x_tm1 = TimeAwareSymbol(\"x\", time_index=-1)\n", + "x_tp1 = TimeAwareSymbol(\"x\", time_index=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9c64cb0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t}$" + ], + "text/plain": [ + "x_t" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "49b98d71", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t-1}$" + ], + "text/plain": [ + "x_t-1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_tm1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c4698744", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t+1}$" + ], + "text/plain": [ + "x_t+1" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_tp1" + ] + }, + { + "cell_type": "markdown", + "id": "4405c0b9", + "metadata": {}, + "source": [ + "The variable is build from the provided `base_name` (in this case `x`), and the `time_index`. The `name` is constructed by combining these two elements." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6e4a84b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'x_t'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.name" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2d8feb2f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'x'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.base_name" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "30eefe6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.time_index" + ] + }, + { + "cell_type": "markdown", + "id": "356b81e4", + "metadata": {}, + "source": [ + "There is also a `safe_name`, which can be used in contexts where the `+` or `-` in the name would be problematic. For the `safe_name`, `+` is replaced with `p`, and `-` with `m`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "282de615", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'x_t+1'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_tp1.name" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5a2d76d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'x_tp1'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_tp1.safe_name" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c52de32a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'x_tm1'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_tm1.safe_name" + ] + }, + { + "cell_type": "markdown", + "id": "a453fd69", + "metadata": {}, + "source": [ + "Otherwise, all other arguments to `sp.Symbol` can be specified. For example, assumptions are allowed:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ad0b0a50", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t}$" + ], + "text/plain": [ + "x_t" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t_positive = TimeAwareSymbol(\"x\", time_index=0, positive=True)\n", + "x_t_positive" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b9f43fec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'positive': True,\n", + " 'zero': False,\n", + " 'complex': True,\n", + " 'extended_negative': False,\n", + " 'extended_nonpositive': False,\n", + " 'extended_nonzero': True,\n", + " 'finite': True,\n", + " 'imaginary': False,\n", + " 'real': True,\n", + " 'commutative': True,\n", + " 'infinite': False,\n", + " 'extended_nonnegative': True,\n", + " 'extended_real': True,\n", + " 'negative': False,\n", + " 'nonzero': True,\n", + " 'nonnegative': True,\n", + " 'extended_positive': True,\n", + " 'nonpositive': False,\n", + " 'hermitian': True}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t_positive.assumptions0" + ] + }, + { + "cell_type": "markdown", + "id": "0f631adc", + "metadata": {}, + "source": [ + "## Time manipulations\n", + "\n", + "After creation, several methods for manipulating the time index of the variable are available:\n", + "\n", + "- `step_forward` increments the `time_index`\n", + "- `step_backward` decremetes the `time_index`\n", + "- `set_t` allows `time_index` to be set directly" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "586d93af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t+1}$" + ], + "text/plain": [ + "x_t+1" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.step_forward()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4035fa2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t-1}$" + ], + "text/plain": [ + "x_t-1" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.step_backward()" + ] + }, + { + "cell_type": "markdown", + "id": "ecfe78a0", + "metadata": {}, + "source": [ + "The most important feature of `TimeAwareSymbol`s is that when two `TimeAwareSymbol`s have the same `base_name` and `time_index`, they evaulate as equal" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9e4097ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.step_backward() == x_tm1" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "466bdba8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.step_forward() == x_tp1" + ] + }, + { + "cell_type": "markdown", + "id": "bd087fd9", + "metadata": {}, + "source": [ + "### Steady State\n", + "\n", + "Another important concept in analysis of dynamic systems is a \"steady state\". A steady state is an equlibrium such that $x_t = x_{t+1} = x_{t+1} = \\dots = x_{ss}$ \n", + "\n", + "Variables can be sent to the steady state using the `to_ss` method" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b29f782b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{ss}$" + ], + "text/plain": [ + "x_ss" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.to_ss()" + ] + }, + { + "cell_type": "markdown", + "id": "a7869fff", + "metadata": {}, + "source": [ + "Since 'ss' is a special `time_index`, variables of the same `base_name` sent to the steady state will evaluate to equal" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "bb60cdaa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_t.to_ss() == x_tp1.to_ss()" + ] + }, + { + "cell_type": "markdown", + "id": "e4b6827c", + "metadata": {}, + "source": [ + "# Working with Equations\n", + "\n", + "`TimeAwareSymbols` subclass `Symbol`, so anything you can do with a symbol can be done with a `TimeAwareSymbol`.\n", + "\n", + "For example, you can do algebraic manipulations" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "509ea9eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t+1} = \\frac{x_{t}}{2} + \\frac{x_{t-1}}{2}$" + ], + "text/plain": [ + "Eq(x_t+1, x_t/2 + x_t-1/2)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eq = sp.Eq(x_tp1, (x_t + x_tm1) / 2)\n", + "eq" + ] + }, + { + "cell_type": "markdown", + "id": "415fadc9", + "metadata": {}, + "source": [ + "Or call `sympy.solve`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "fc262e13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 2 x_{t+1} - x_{t-1}$" + ], + "text/plain": [ + "2*x_t+1 - x_t-1" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sp.solve(eq, x_t)[0]" + ] + }, + { + "cell_type": "markdown", + "id": "ea9708ac", + "metadata": {}, + "source": [ + "Usually, though, you are going to want to manipulate the time indices for entire expressions. Unfortunately, there is no `TimeAwareExpr`. Instead, `gEconpy` gives some helper functions for manipulation of equations that include `TimeAwareSymbols`. These are:\n", + "\n", + "- `step_equation_forward`\n", + "- `step_equation_backward`\n", + "- `eq_to_ss`\n", + "\n", + "The equations do what the names suggest" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "886f615a", + "metadata": {}, + "outputs": [], + "source": [ + "from gEconpy.shared.utilities import (\n", + " step_equation_backward,\n", + " step_equation_forward,\n", + " eq_to_ss,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "d5f266c3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t+2} = \\frac{x_{t}}{2} + \\frac{x_{t+1}}{2}$" + ], + "text/plain": [ + "Eq(x_t+2, x_t/2 + x_t+1/2)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "step_equation_forward(eq)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7346e163", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\frac{x_{t-1}}{2} + \\frac{x_{t-2}}{2}$" + ], + "text/plain": [ + "Eq(x_t, x_t-1/2 + x_t-2/2)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "step_equation_backward(eq)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4a86a257", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle 0$" + ], + "text/plain": [ + "0" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eq_to_ss(eq.lhs - eq.rhs)" + ] + }, + { + "cell_type": "markdown", + "id": "729de928", + "metadata": {}, + "source": [ + "# Example 1: $AR(1)$ to $MA(\\infty)$\n", + "\n", + "Using these tools, we can do powerful analysis on time series. \n", + "\n", + "Consider an AR(1) system:\n", + "\n", + "$$ x_t = \\rho x_{t-1} + \\epsilon_t$$\n", + "\n", + "We can use `step_equation_backward` together with repeated substitution to derive the $MA(\\infty)$ form of the $AR(1)$ system" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2952f2e5", + "metadata": {}, + "outputs": [], + "source": [ + "eps_t = TimeAwareSymbol(\"\\\\varepsilon\", 0)\n", + "rho = sp.Symbol(\"rho\", positive=True)\n", + "\n", + "# This is only the right-hand side, remember there's an x_t on the left\n", + "ar_1_rhs = rho * x_tm1 + eps_t" + ] + }, + { + "cell_type": "markdown", + "id": "4af732d4", + "metadata": {}, + "source": [ + "We will iterative shift the equation backwards and substitute" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "2bdbb2eb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho x_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho*x_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{2} x_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**2*x_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{3} x_{t-3} + \\rho^{2} \\varepsilon_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**3*x_t-3 + rho**2*\\varepsilon_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{4} x_{t-4} + \\rho^{3} \\varepsilon_{t-3} + \\rho^{2} \\varepsilon_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**4*x_t-4 + rho**3*\\varepsilon_t-3 + rho**2*\\varepsilon_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{5} x_{t-5} + \\rho^{4} \\varepsilon_{t-4} + \\rho^{3} \\varepsilon_{t-3} + \\rho^{2} \\varepsilon_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**5*x_t-5 + rho**4*\\varepsilon_t-4 + rho**3*\\varepsilon_t-3 + rho**2*\\varepsilon_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{6} x_{t-6} + \\rho^{5} \\varepsilon_{t-5} + \\rho^{4} \\varepsilon_{t-4} + \\rho^{3} \\varepsilon_{t-3} + \\rho^{2} \\varepsilon_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**6*x_t-6 + rho**5*\\varepsilon_t-5 + rho**4*\\varepsilon_t-4 + rho**3*\\varepsilon_t-3 + rho**2*\\varepsilon_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{7} x_{t-7} + \\rho^{6} \\varepsilon_{t-6} + \\rho^{5} \\varepsilon_{t-5} + \\rho^{4} \\varepsilon_{t-4} + \\rho^{3} \\varepsilon_{t-3} + \\rho^{2} \\varepsilon_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**7*x_t-7 + rho**6*\\varepsilon_t-6 + rho**5*\\varepsilon_t-5 + rho**4*\\varepsilon_t-4 + rho**3*\\varepsilon_t-3 + rho**2*\\varepsilon_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{8} x_{t-8} + \\rho^{7} \\varepsilon_{t-7} + \\rho^{6} \\varepsilon_{t-6} + \\rho^{5} \\varepsilon_{t-5} + \\rho^{4} \\varepsilon_{t-4} + \\rho^{3} \\varepsilon_{t-3} + \\rho^{2} \\varepsilon_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**8*x_t-8 + rho**7*\\varepsilon_t-7 + rho**6*\\varepsilon_t-6 + rho**5*\\varepsilon_t-5 + rho**4*\\varepsilon_t-4 + rho**3*\\varepsilon_t-3 + rho**2*\\varepsilon_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{9} x_{t-9} + \\rho^{8} \\varepsilon_{t-8} + \\rho^{7} \\varepsilon_{t-7} + \\rho^{6} \\varepsilon_{t-6} + \\rho^{5} \\varepsilon_{t-5} + \\rho^{4} \\varepsilon_{t-4} + \\rho^{3} \\varepsilon_{t-3} + \\rho^{2} \\varepsilon_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**9*x_t-9 + rho**8*\\varepsilon_t-8 + rho**7*\\varepsilon_t-7 + rho**6*\\varepsilon_t-6 + rho**5*\\varepsilon_t-5 + rho**4*\\varepsilon_t-4 + rho**3*\\varepsilon_t-3 + rho**2*\\varepsilon_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho^{10} x_{t-10} + \\rho^{9} \\varepsilon_{t-9} + \\rho^{8} \\varepsilon_{t-8} + \\rho^{7} \\varepsilon_{t-7} + \\rho^{6} \\varepsilon_{t-6} + \\rho^{5} \\varepsilon_{t-5} + \\rho^{4} \\varepsilon_{t-4} + \\rho^{3} \\varepsilon_{t-3} + \\rho^{2} \\varepsilon_{t-2} + \\rho \\varepsilon_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho**10*x_t-10 + rho**9*\\varepsilon_t-9 + rho**8*\\varepsilon_t-8 + rho**7*\\varepsilon_t-7 + rho**6*\\varepsilon_t-6 + rho**5*\\varepsilon_t-5 + rho**4*\\varepsilon_t-4 + rho**3*\\varepsilon_t-3 + rho**2*\\varepsilon_t-2 + rho*\\varepsilon_t-1 + \\varepsilon_t)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "curr_x = x_t\n", + "curr_rhs = ar_1_rhs.copy()\n", + "for _ in range(10):\n", + " display(sp.Eq(x_t, ar_1_rhs))\n", + " curr_x = curr_x.step_backward()\n", + " curr_rhs = step_equation_backward(curr_rhs)\n", + " ar_1_rhs = ar_1_rhs.subs({curr_x: curr_rhs}).expand()" + ] + }, + { + "cell_type": "markdown", + "id": "09fe5335", + "metadata": {}, + "source": [ + "Since $\\rho \\in (0, 1)$, the leading term will eventually go to zero, and we recover the well-known equation:\n", + "\n", + "$$ x_t = \\sum_{s=0}^t \\rho^s \\varepsilon_{t-s}$$\n", + "\n", + "I don't know any way for sympy to automatically detect the presence of this series and rewrite into summation notation -- if you do, open an issue so I can update this example! " + ] + }, + { + "cell_type": "markdown", + "id": "b9ca6207", + "metadata": {}, + "source": [ + "# Example 2: Analytical Steady State\n", + "\n", + "More useful, perhaps, is that we can use `TimeAwareSymbols` to derive the steady state of a dynamical system. Consider the AR(1) equation again:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "0330395e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\rho x_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, rho*x_t-1 + \\varepsilon_t)" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ar_1 = sp.Eq(x_t, rho * x_tm1 + eps_t)\n", + "ar_1" + ] + }, + { + "cell_type": "markdown", + "id": "25fd61be", + "metadata": {}, + "source": [ + "We can send this to the steady-state and compute the value of $x_{ss}$" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c78ba100", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{\\varepsilon_{ss}}{\\rho - 1}$" + ], + "text/plain": [ + "-\\varepsilon_ss/(rho - 1)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sp.solve(eq_to_ss(ar_1), x_t.to_ss())[0]" + ] + }, + { + "cell_type": "markdown", + "id": "2f5a8fc7", + "metadata": {}, + "source": [ + "Obviously we need to know something about $\\varepsilon_{ss}$. We typtically assume $\\varepsilon_t ~ N(0, \\sigma)$. In the (deterministic!) steady state, there are no shocks, so $\\varepsilon_{ss} = 0$. \n", + "\n", + "Let's generalize the equation to allow a drift in the shocks, so $\\varepsilon_t ~ N(\\mu, \\sigma)$. We can pull out the $\\mu$ using the properties of normal distributions to obtain:\n", + "\n", + "$$x_t = \\mu + \\rho x_{t-1} + \\varepsilon_t$$" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1c8ee016", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle x_{t} = \\mu + \\rho x_{t-1} + \\varepsilon_{t}$" + ], + "text/plain": [ + "Eq(x_t, mu + rho*x_t-1 + \\varepsilon_t)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mu = sp.Symbol(\"mu\")\n", + "ar_1 = sp.Eq(x_t, mu + rho * x_tm1 + eps_t)\n", + "ar_1" + ] + }, + { + "cell_type": "markdown", + "id": "71c140ff", + "metadata": {}, + "source": [ + "Solving for the steady state gives the well-known expression for the AR(1) steady state" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "403eced5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\frac{\\mu}{\\rho - 1}$" + ], + "text/plain": [ + "-mu/(rho - 1)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sp.solve(eq_to_ss(ar_1).subs({eps_t.to_ss(): 0}), x_t.to_ss())[0]" + ] + }, + { + "cell_type": "markdown", + "id": "8f4c3365", + "metadata": {}, + "source": [ + "# Example 3: Deterministic RBC\n", + "\n", + "Consider an RBC model defined (in reduced form) as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "b95f909d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\beta \\left(\\alpha K_{t+1}^{\\alpha - 1} e^{A_{t+1}} - \\delta + 1\\right) + \\frac{C_{t+1}}{C_{t}}$" + ], + "text/plain": [ + "-beta*(alpha*K_t+1**(alpha - 1)*exp(A_t+1) - delta + 1) + C_t+1/C_t" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle C_{t} - K_{t} \\left(1 - \\delta\\right) - K_{t}^{\\alpha} e^{A_{t}} + K_{t+1}$" + ], + "text/plain": [ + "C_t - K_t*(1 - delta) - K_t**alpha*exp(A_t) + K_t+1" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle - \\rho A_{t-1} + A_{t} - \\varepsilon_{t}$" + ], + "text/plain": [ + "-rho*A_t-1 + A_t - \\varepsilon_t" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "C, K, A, epsilon = [TimeAwareSymbol(x, 0) for x in [\"C\", \"K\", \"A\", \"\\\\varepsilon\"]]\n", + "alpha, beta, delta, rho = sp.symbols(\n", + " \"alpha beta delta rho\",\n", + ")\n", + "\n", + "euler = C.step_forward() / C - beta * (\n", + " alpha * sp.exp(A.step_forward()) * K.step_forward() ** (alpha - 1) + 1 - delta\n", + ")\n", + "transition = K.step_forward() - (sp.exp(A) * K**alpha + (1 - delta) * K - C)\n", + "shock = A - rho * A.step_backward() - epsilon\n", + "\n", + "system = [euler, transition, shock]\n", + "for eq in system:\n", + " display(eq)" + ] + }, + { + "cell_type": "markdown", + "id": "641d56b8", + "metadata": {}, + "source": [ + "We can use `TimeAwareSymbols` to solve for the deterministic steady state of this entire system in one fell swoop" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "53357a4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle A_{ss} = 0$" + ], + "text/plain": [ + "Eq(A_ss, 0)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle C_{ss} = - \\delta \\left(\\frac{\\beta \\left(\\delta - 1\\right) + 1}{\\alpha \\beta}\\right)^{\\frac{1}{\\alpha - 1}} + \\left(\\left(\\frac{\\beta \\left(\\delta - 1\\right) + 1}{\\alpha \\beta}\\right)^{\\frac{1}{\\alpha - 1}}\\right)^{\\alpha}$" + ], + "text/plain": [ + "Eq(C_ss, -delta*((beta*(delta - 1) + 1)/(alpha*beta))**(1/(alpha - 1)) + (((beta*(delta - 1) + 1)/(alpha*beta))**(1/(alpha - 1)))**alpha)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/latex": [ + "$\\displaystyle K_{ss} = \\left(\\frac{\\beta \\delta - \\beta + 1}{\\alpha \\beta}\\right)^{\\frac{1}{\\alpha - 1}}$" + ], + "text/plain": [ + "Eq(K_ss, ((beta*delta - beta + 1)/(alpha*beta))**(1/(alpha - 1)))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ss_system = [eq_to_ss(eq).simplify().subs({epsilon.to_ss(): 0.0}) for eq in system]\n", + "ss_dict = sp.solve(ss_system, [K.to_ss(), C.to_ss(), A.to_ss()], dict=True)[0]\n", + "\n", + "for var, eq in ss_dict.items():\n", + " display(sp.Eq(var, eq))" + ] + }, + { + "cell_type": "markdown", + "id": "0c70e2a6", + "metadata": {}, + "source": [ + "Using `sp.lambdify`, we can compile a function that computes the steady state of the system given input parameters. This is essentially what `gEconpy` does internally when solving for the steady state of a DSGE model." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "dd01f69d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1.9825902234443513, 19.50030034168597]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f_ss = sp.lambdify([alpha, beta, delta, rho], list(ss_dict.values()))\n", + "param_dict = {\"alpha\": 0.33, \"beta\": 0.99, \"delta\": 0.035, \"rho\": 0.95}\n", + "f_ss(**param_dict)" + ] + }, + { + "cell_type": "markdown", + "id": "194c01e7", + "metadata": {}, + "source": [ + "### Phase Diagram\n", + "\n", + "Since this system is deterministic, we can construct a phase diagram showing system dynamics for a given $(C_t, K_t)$ tuple. To do this, we first need to re-arrange the Euler equation and law of motion of capital to obtain rates of change, $\\Delta C_{t+1}$ and $\\Delta K_{t+1}$. For $\\Delta C_{t+1}$ we get:\n", + "\n", + "$$\\begin{align}\n", + "\\Delta C_{t+1} &= C_{t+1} - C_t \\\\\n", + "&= \\beta C_t \\left (\\alpha K_{t+1}^{\\alpha - 1} + (1 - \\delta) \\right ) - C_t \\\\\n", + "&= \\left (\\beta \\alpha K_{t+1}^{\\alpha - 1} + \\beta (1 - \\delta) - 1 \\right ) C_t\n", + "\\end{align}$$\n", + "\n", + "For the second line, the Euler equation was solved for $C_{t+1}$ and substituted.\n", + "\n", + "For $\\Delta K_{t+1}$:\n", + "\n", + "$$\\begin{align}\n", + "\\Delta K_{t+1} &= K_{t+1} - K_t \\\\\n", + "&= K_t^\\alpha + (1 - \\delta) K_t - C_t \\\\\n", + "\\end{align}$$\n", + "\n", + "Computing these $\\Delta$s over a grid of points will give us a phase diagram. Let's look at how these can be solved for with `sympy`:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "795a1847", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle \\beta C_{t} \\left(\\alpha K_{t+1}^{\\alpha - 1} e^{A_{t+1}} - \\delta + 1\\right)$" + ], + "text/plain": [ + "beta*C_t*(alpha*K_t+1**(alpha - 1)*exp(A_t+1) - delta + 1)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C_tp1 = sp.solve(euler, C.set_t(1))[0]\n", + "C_tp1" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "8eb5d492", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle C_{t} \\left(\\beta \\left(\\alpha K_{t+1}^{\\alpha - 1} - \\delta + 1\\right) - 1\\right)$" + ], + "text/plain": [ + "C_t*(beta*(alpha*K_t+1**(alpha - 1) - delta + 1) - 1)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Delta_C = (C_tp1 - C).collect(C).subs({A.set_t(1): 0})\n", + "Delta_C" + ] + }, + { + "cell_type": "markdown", + "id": "14b4090d", + "metadata": {}, + "source": [ + "This solution isn't exactly what we need, though, because we don't want the $K_{t+1}$. Use the transition equation to write it in terms of time $t$ variables only" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "a9deb7ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle C_{t} \\left(\\beta \\left(\\alpha \\left(- \\delta K_{t} - C_{t} + K_{t} + K_{t}^{\\alpha}\\right)^{\\alpha - 1} - \\delta + 1\\right) - 1\\right)$" + ], + "text/plain": [ + "C_t*(beta*(alpha*(-delta*K_t - C_t + K_t + K_t**alpha)**(alpha - 1) - delta + 1) - 1)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "K_tp1 = sp.solve(transition.subs({A: 0}), K.set_t(1))[0]\n", + "Delta_C = Delta_C.subs({K.set_t(1): K_tp1})\n", + "Delta_C" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "d9735240", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\delta K_{t} - C_{t} + K_{t} + K_{t}^{\\alpha}$" + ], + "text/plain": [ + "-delta*K_t - C_t + K_t + K_t**alpha" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "K_tp1 = sp.solve(transition, K.set_t(1))[0].subs({A: 0})\n", + "K_tp1" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "bfae4a64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\delta K_{t} - C_{t} + K_{t}^{\\alpha}$" + ], + "text/plain": [ + "-delta*K_t - C_t + K_t**alpha" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Delta_K = K_tp1 - K\n", + "Delta_K" + ] + }, + { + "cell_type": "markdown", + "id": "796b37fe", + "metadata": {}, + "source": [ + "Compile a function with `sp.lambdify`" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "7ed49878", + "metadata": {}, + "outputs": [], + "source": [ + "parameters = list(param_dict.keys())\n", + "f_Delta = sp.lambdify([C, K] + parameters, [Delta_C, Delta_K])" + ] + }, + { + "cell_type": "markdown", + "id": "02ff77d8", + "metadata": {}, + "source": [ + "We are also interested in when $\\Delta C_{t+1} = \\Delta K_{t+1} = 0$, because these equations will form boundaries in phase space. We can do this by using `sp.solve`. \n", + "\n", + "First, solve $\\Delta C_{t+1}$ for $C_t$. There will be two solutions, and one will be zero (since the whole expression is multiplied by $C_t$). We're only interested in the non-trivial solution." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "fe50f783", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\delta K_{t} + K_{t} + K_{t}^{\\alpha} - \\left(\\frac{\\beta \\delta - \\beta + 1}{\\alpha \\beta}\\right)^{\\frac{1}{\\alpha - 1}}$" + ], + "text/plain": [ + "-delta*K_t + K_t + K_t**alpha - ((beta*delta - beta + 1)/(alpha*beta))**(1/(alpha - 1))" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "boundary_1 = sp.solve(Delta_C, C)[1]\n", + "boundary_1" + ] + }, + { + "cell_type": "markdown", + "id": "1c56b009", + "metadata": {}, + "source": [ + "Next, solve $\\Delta K_{t+1}$ for $C_t$" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "13ef207b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$\\displaystyle - \\delta K_{t} + K_{t}^{\\alpha}$" + ], + "text/plain": [ + "-delta*K_t + K_t**alpha" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "boundary_2 = sp.solve(Delta_K, C)[0]\n", + "boundary_2" + ] + }, + { + "cell_type": "markdown", + "id": "05f3d5ea", + "metadata": {}, + "source": [ + "Compile a function" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "53ab0394", + "metadata": {}, + "outputs": [], + "source": [ + "f_boundaries = sp.lambdify([K] + parameters, [boundary_1, boundary_2])" + ] + }, + { + "cell_type": "markdown", + "id": "5430c224", + "metadata": {}, + "source": [ + "Functions created with `sp.lambdify` are inherently vectorized, so we can make a grid of capital values and compute the associated consumpions" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "1bdf3095", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "k_max = 120\n", + "c_max = (\n", + " k_max ** param_dict[\"alpha\"]\n", + ") # We can't consume more than exists in the economy!\n", + "\n", + "k_grid = np.linspace(1e-2, k_max, 100)\n", + "c_grid = np.linspace(1e-2, c_max, 100)\n", + "boundaries = f_boundaries(k_grid, **param_dict)\n", + "\n", + "kk, cc = np.meshgrid(k_grid, c_grid)\n", + "with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n", + " c_delta, k_delta = f_Delta(cc, kk, **param_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "2a689056", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(16, 4), dpi=77)\n", + "for axis, colorby, name in zip(fig.axes, [k_delta, c_delta], [\"ΔK\", \"ΔC\"]):\n", + " axis.plot(k_grid, boundaries[0], label=\"ΔK = 0\")\n", + " axis.plot(k_grid, boundaries[1], label=\"ΔC = 0\")\n", + " quiver_plot = axis.quiver(\n", + " kk, cc, k_delta, c_delta, colorby, cmap=plt.cm.RdBu, clim=(-0.05, 0.05)\n", + " )\n", + " axis.set(\n", + " xlim=(0, k_max),\n", + " ylim=(0, c_max),\n", + " xlabel=\"$K_t$\",\n", + " ylabel=\"$C_t$\",\n", + " title=f\"Phase Diagram (Colored by {name})\",\n", + " )\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/source/index.rst b/docs/source/index.rst index 2589080..db15fdb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,8 +1,3 @@ -.. gEconpy documentation master file, created by - sphinx-quickstart on Sun Jun 30 13:20:44 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - Introduction ============ A collection of tools for working with DSGE models in python, inspired by the fantastic R package gEcon, http://gecon.r-forge.r-project.org/. diff --git a/examples/Example Notebook.ipynb b/examples/Example Notebook.ipynb index 4298b90..73bc429 100644 --- a/examples/Example Notebook.ipynb +++ b/examples/Example Notebook.ipynb @@ -21,15 +21,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Running gEconpy version 1.2.1\n" + "Running gEconpy version 0+untagged.253.g6e388ed.dirty\n" ] } ], "source": [ - "import sys\n", - "\n", - "sys.path.append(\"..\")\n", - "\n", "import gEconpy as ge\n", "import gEconpy.plotting as gp\n", "\n", @@ -64,7 +60,7 @@ }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ "Model Building Complete.\n", @@ -79,16 +75,15 @@ "\t\t 0 / 1 has a defined prior. \n", "\t6 parameters\n", "\t\t 0 / 6 has a defined prior. \n", - "\t0 calibrating equations\n", - "\t0 parameters to calibrate\n", - " Model appears well defined and ready to proceed to solving.\n", + "\t0 parameters to calibrate.\n", + "Model appears well defined and ready to proceed to solving.\n", "\n" ] } ], "source": [ "file_path = \"../GCN Files/RBC_basic.gcn\"\n", - "model = ge.gEconModel(file_path, verbose=True)" + "model = ge.model_from_gcn(file_path, verbose=True)" ] }, { @@ -217,7 +212,7 @@ } ], "source": [ - "for eq in model.system_equations:\n", + "for eq in model.equations:\n", " display(eq)" ] }, @@ -236,7 +231,7 @@ "metadata": {}, "outputs": [], "source": [ - "for eq in model.calibrating_equations:\n", + "for eq in model.calibrated_params:\n", " display(eq)" ] }, @@ -259,17 +254,58 @@ }, "outputs": [ { - "name": "stdout", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a13ef548638745a7927bf1aaf3fccff5", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Steady state found! Sum of squared residuals is 6.695381126805323e-23\n",
-      "CPU times: user 511 ms, sys: 8.42 ms, total: 519 ms\n",
-      "Wall time: 521 ms\n"
+      "Steady state found\n",
+      "--------------------------------------------------------------------------------\n",
+      "Optimizer message             The solution converged.\n",
+      "Sum of squared residuals      6.694346901775185e-23\n",
+      "Maximum absoluate error       4.7553072590744705e-12\n",
+      "Gradient L2-norm at solution  3.2123187941480967e-10\n",
+      "Max abs gradient at solution  3.1686401419372956e-10\n"
      ]
     }
    ],
    "source": [
-    "%time model.steady_state()"
+    "ss_res = model.steady_state(how=\"root\")"
    ]
   },
   {
@@ -279,7 +315,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
       "A_ss               1.000\n",
@@ -295,34 +331,7 @@
     }
    ],
    "source": [
-    "model.print_steady_state()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "28e96b98",
-   "metadata": {},
-   "source": [
-    "The function to solve a new steady state is called `f_ss`, and it takes a dictionary of free parameters as an input, and returns a dictionary summarizing the results of the steady state fitting. Notice now that the function is also instantaneous."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0440548b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 460 μs, sys: 19 μs, total: 479 μs\n",
-      "Wall time: 476 μs\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time model.f_ss(model.free_param_dict);"
+    "ge.print_steady_state(ss_res)"
    ]
   },
   {
@@ -337,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "df2c5665",
    "metadata": {},
    "outputs": [
@@ -440,10 +449,10 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\left(\\rho_{A} - 1\\right) \\log{\\left(A_{ss} \\right)}$"
+       "$\\displaystyle \\rho_{A} \\log{\\left(A_{ss} \\right)} + \\epsilon_{A ss} - \\log{\\left(A_{ss} \\right)}$"
       ],
       "text/plain": [
-       "(rho_A - 1)*log(A_ss)"
+       "rho_A*log(A_ss) + epsilon_A_ss - log(A_ss)"
       ]
      },
      "metadata": {},
@@ -451,143 +460,34 @@
     }
    ],
    "source": [
-    "for eq in model.steady_state_system:\n",
-    "    display(eq)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dbdf23d1",
-   "metadata": {},
-   "source": [
-    "# Perturbation Solution\n",
-    "\n",
-    "Like the steady state solution, the perturbation solution constructs a function to solve linearized system via perturbation. The first time you run the function will be slower. \n",
-    "\n",
-    "Following Dynare, the default pertubation solver is Cycle Reduction, implemented in Numba for faster execution. You can also ask for Gensys if you wish. The original gEcon used Gensys."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b2ceed67",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Solution found, sum of squared residuals:  3.980959555625145e-31\n",
-      "Norm of deterministic part: 0.000000000\n",
-      "Norm of stochastic part:    0.000000000\n",
-      "CPU times: user 472 ms, sys: 6.83 ms, total: 479 ms\n",
-      "Wall time: 481 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time model.solve_model()"
+    "for eq in model.equations:\n",
+    "    display(ge.utilities.eq_to_ss(eq).simplify())"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "91289bd8",
-   "metadata": {},
-   "source": [
-    "The second run is much faster"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "607bb2d2",
+   "id": "b34c3d69",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Solution found, sum of squared residuals:  3.980959555625145e-31\n",
-      "Norm of deterministic part: 0.000000000\n",
-      "Norm of stochastic part:    0.000000000\n",
-      "CPU times: user 574 μs, sys: 310 μs, total: 884 μs\n",
-      "Wall time: 637 μs\n"
-     ]
-    }
-   ],
    "source": [
-    "%time model.solve_model()"
+    "# Linearization"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "03fc723c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "====================    T     ====================\n",
-      "               A    C    I             K    L    Y  lambda    r    w\n",
-      "A       0.950000 -0.0 -0.0 -5.308123e-17 -0.0 -0.0    -0.0 -0.0 -0.0\n",
-      "C       0.309657  0.0  0.0  4.787472e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "I       3.640697 -0.0 -0.0 -5.127277e-01 -0.0 -0.0    -0.0 -0.0 -0.0\n",
-      "K       0.072814 -0.0 -0.0  9.697454e-01 -0.0 -0.0    -0.0 -0.0 -0.0\n",
-      "L       0.206602  0.0  0.0 -1.566471e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "Y       1.084291  0.0  0.0  2.481794e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "lambda -0.464485  0.0  0.0 -7.181208e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "r       1.084291  0.0  0.0 -7.518206e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "w       0.877689  0.0  0.0  4.048265e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "====================    R     ====================\n",
-      "        epsilon_A\n",
-      "A        1.000000\n",
-      "C        0.325955\n",
-      "I        3.832313\n",
-      "K        0.076646\n",
-      "L        0.217476\n",
-      "Y        1.141359\n",
-      "lambda  -0.488932\n",
-      "r        1.141359\n",
-      "w        0.923883\n"
-     ]
-    }
-   ],
-   "source": [
-    "for name, policy_matrix in zip([\"T\", \"R\"], [model.T, model.R]):\n",
-    "    print(name.center(10).center(50, \"=\"))\n",
-    "    print(policy_matrix.to_string())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61d3785b",
+   "execution_count": 8,
+   "id": "23c54d06",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Blanchard-Kahn Conditions\n",
-    "\n",
-    "After you have a perturbation solution, you can check the Eigenvalues of the system to make sure the BK conditions are satisfied.\n",
-    "\n",
-    "The output shows the eigenvalues computed by gensys: the modulus, real part, and imaginary part."
+    "A, B, C, D = model.linearize_model(steady_state=ss_res)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "3dd1bea1",
+   "execution_count": 9,
+   "id": "d1c6e44b",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model solution has 2 eigenvalues greater than one in modulus and 2 forward-looking variables.\n",
-      "Blanchard-Kahn condition is satisfied.\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -609,52 +509,124 @@
        "  \n",
        "    \n",
        "      \n",
-       "      Modulus\n",
-       "      Real\n",
-       "      Imaginary\n",
+       "      A\n",
+       "      C\n",
+       "      I\n",
+       "      K\n",
+       "      L\n",
+       "      Y\n",
+       "      lambda\n",
+       "      r\n",
+       "      w\n",
        "    \n",
        "  \n",
        "  \n",
        "    \n",
-       "      0\n",
-       "      1.951878e-18\n",
-       "      1.951878e-18\n",
+       "      Equation 0\n",
+       "      0.00\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      1.076\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "    \n",
+       "    \n",
+       "      Equation 1\n",
+       "      0.00\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      35.018\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "    \n",
+       "    \n",
+       "      Equation 2\n",
+       "      0.00\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.000\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
        "      0.0\n",
        "    \n",
        "    \n",
-       "      1\n",
-       "      1.096047e-17\n",
-       "      1.096047e-17\n",
+       "      Equation 3\n",
+       "      0.00\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.000\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
        "      0.0\n",
        "    \n",
        "    \n",
-       "      2\n",
-       "      9.429945e-17\n",
-       "      9.429945e-17\n",
+       "      Equation 4\n",
+       "      0.00\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.000\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
        "      0.0\n",
        "    \n",
        "    \n",
-       "      3\n",
-       "      9.500000e-01\n",
-       "      9.500000e-01\n",
+       "      Equation 5\n",
+       "      0.00\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      1.076\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
        "      0.0\n",
        "    \n",
        "    \n",
-       "      4\n",
-       "      9.697454e-01\n",
-       "      9.697454e-01\n",
+       "      Equation 6\n",
+       "      0.00\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      -0.020\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
        "      0.0\n",
        "    \n",
        "    \n",
-       "      5\n",
-       "      1.041615e+00\n",
-       "      1.041615e+00\n",
+       "      Equation 7\n",
+       "      0.00\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.853\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
        "      0.0\n",
        "    \n",
        "    \n",
-       "      6\n",
-       "      5.077961e+06\n",
-       "      5.077961e+06\n",
+       "      Equation 8\n",
+       "      0.95\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.000\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
+       "      0.0\n",
        "      0.0\n",
        "    \n",
        "  \n",
@@ -662,87 +634,851 @@
        ""
       ],
       "text/plain": [
-       "        Modulus          Real  Imaginary\n",
-       "0  1.951878e-18  1.951878e-18        0.0\n",
-       "1  1.096047e-17  1.096047e-17        0.0\n",
-       "2  9.429945e-17  9.429945e-17        0.0\n",
-       "3  9.500000e-01  9.500000e-01        0.0\n",
-       "4  9.697454e-01  9.697454e-01        0.0\n",
-       "5  1.041615e+00  1.041615e+00        0.0\n",
-       "6  5.077961e+06  5.077961e+06        0.0"
+       "               A    C    I       K    L    Y  lambda    r    w\n",
+       "Equation 0  0.00  0.0  0.0   1.076  0.0  0.0     0.0  0.0  0.0\n",
+       "Equation 1  0.00  0.0  0.0  35.018  0.0  0.0     0.0  0.0  0.0\n",
+       "Equation 2  0.00  0.0  0.0   0.000  0.0  0.0     0.0  0.0  0.0\n",
+       "Equation 3  0.00  0.0  0.0   0.000  0.0  0.0     0.0  0.0  0.0\n",
+       "Equation 4  0.00  0.0  0.0   0.000  0.0  0.0     0.0  0.0  0.0\n",
+       "Equation 5  0.00  0.0  0.0   1.076  0.0  0.0     0.0  0.0  0.0\n",
+       "Equation 6  0.00  0.0  0.0  -0.020  0.0  0.0     0.0  0.0  0.0\n",
+       "Equation 7  0.00  0.0  0.0   0.853  0.0  0.0     0.0  0.0  0.0\n",
+       "Equation 8  0.95  0.0  0.0   0.000  0.0  0.0     0.0  0.0  0.0"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model.check_bk_condition()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "162cd3aa",
-   "metadata": {},
-   "source": [
-    "You can also visualize the Eigenvalues using `plot_eigenvalues` in the plotting functions."
+    "ge.matrix_to_dataframe(A, model, dim1=\"equation\", round=3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "137a797b",
+   "execution_count": 10,
+   "id": "a63cab46",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp.plot_eigenvalues(model);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c2b289b",
-   "metadata": {},
-   "source": [
-    "## Model Statistics\n",
-    "\n",
-    "Functions to compute the stationary covariance matrix, as well as autocovariances for each variable, are also available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "c03d28cb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/jessegrabowski/Documents/Python/gEconpy/examples/../gEconpy/classes/model.py:860: UserWarning: No standard deviation provided for shocks epsilon_A. Using default of std = 0.01. Explicitypass variance information for these shocks or set their priors to silence this warning.\n",
-      "  warn(\n"
-     ]
-    }
-   ],
-   "source": [
-    "sigma = model.compute_stationary_covariance_matrix()\n",
-    "acorr_matrix = model.compute_autocorrelation_matrix(n_lags=30)"
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Equation 00.000-2.358-0.7150.0001.9980.0000.0001.0761.998
Equation 10.0000.0000.715-35.7320.0000.0000.0000.0000.000
Equation 20.000-0.4140.0000.0000.0000.000-0.2760.0000.000
Equation 30.0000.0000.0000.000-1.3450.0000.6730.0000.673
Equation 40.0000.0000.0000.0000.0000.000-0.2760.0000.000
Equation 53.0730.0000.0000.0001.998-3.0730.0000.0000.000
Equation 60.0300.0000.0000.0000.0200.0000.000-0.0300.000
Equation 72.4360.0000.0000.000-0.8530.0000.0000.000-2.436
Equation 8-1.0000.0000.0000.0000.0000.0000.0000.0000.000
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                A      C      I       K      L      Y  lambda      r      w\n",
+       "Equation 0  0.000 -2.358 -0.715   0.000  1.998  0.000   0.000  1.076  1.998\n",
+       "Equation 1  0.000  0.000  0.715 -35.732  0.000  0.000   0.000  0.000  0.000\n",
+       "Equation 2  0.000 -0.414  0.000   0.000  0.000  0.000  -0.276  0.000  0.000\n",
+       "Equation 3  0.000  0.000  0.000   0.000 -1.345  0.000   0.673  0.000  0.673\n",
+       "Equation 4  0.000  0.000  0.000   0.000  0.000  0.000  -0.276  0.000  0.000\n",
+       "Equation 5  3.073  0.000  0.000   0.000  1.998 -3.073   0.000  0.000  0.000\n",
+       "Equation 6  0.030  0.000  0.000   0.000  0.020  0.000   0.000 -0.030  0.000\n",
+       "Equation 7  2.436  0.000  0.000   0.000 -0.853  0.000   0.000  0.000 -2.436\n",
+       "Equation 8 -1.000  0.000  0.000   0.000  0.000  0.000   0.000  0.000  0.000"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ge.matrix_to_dataframe(B, model, dim1=\"equation\", round=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d7d852e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "    \n",
+       "  \n",
+       "
ACIKLYlambdarw
Equation 00.00.00.00.00.00.00.0000.0000.0
Equation 10.00.00.00.00.00.00.0000.0000.0
Equation 20.00.00.00.00.00.00.0000.0000.0
Equation 30.00.00.00.00.00.00.0000.0000.0
Equation 40.00.00.00.00.00.00.2760.0080.0
Equation 50.00.00.00.00.00.00.0000.0000.0
Equation 60.00.00.00.00.00.00.0000.0000.0
Equation 70.00.00.00.00.00.00.0000.0000.0
Equation 80.00.00.00.00.00.00.0000.0000.0
\n",
+       "
"
+      ],
+      "text/plain": [
+       "              A    C    I    K    L    Y  lambda      r    w\n",
+       "Equation 0  0.0  0.0  0.0  0.0  0.0  0.0   0.000  0.000  0.0\n",
+       "Equation 1  0.0  0.0  0.0  0.0  0.0  0.0   0.000  0.000  0.0\n",
+       "Equation 2  0.0  0.0  0.0  0.0  0.0  0.0   0.000  0.000  0.0\n",
+       "Equation 3  0.0  0.0  0.0  0.0  0.0  0.0   0.000  0.000  0.0\n",
+       "Equation 4  0.0  0.0  0.0  0.0  0.0  0.0   0.276  0.008  0.0\n",
+       "Equation 5  0.0  0.0  0.0  0.0  0.0  0.0   0.000  0.000  0.0\n",
+       "Equation 6  0.0  0.0  0.0  0.0  0.0  0.0   0.000  0.000  0.0\n",
+       "Equation 7  0.0  0.0  0.0  0.0  0.0  0.0   0.000  0.000  0.0\n",
+       "Equation 8  0.0  0.0  0.0  0.0  0.0  0.0   0.000  0.000  0.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ge.matrix_to_dataframe(C, model, dim1=\"equation\", round=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "22df936c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
+       "  \n",
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+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
epsilon_A
Equation 00
Equation 10
Equation 20
Equation 30
Equation 40
Equation 50
Equation 60
Equation 70
Equation 81
\n",
+       "
"
+      ],
+      "text/plain": [
+       "            epsilon_A\n",
+       "Equation 0          0\n",
+       "Equation 1          0\n",
+       "Equation 2          0\n",
+       "Equation 3          0\n",
+       "Equation 4          0\n",
+       "Equation 5          0\n",
+       "Equation 6          0\n",
+       "Equation 7          0\n",
+       "Equation 8          1"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ge.matrix_to_dataframe(D, model, dim1=\"equation\", round=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbdf23d1",
+   "metadata": {},
+   "source": [
+    "# Perturbation Solution\n",
+    "\n",
+    "Like the steady state solution, the perturbation solution constructs a function to solve linearized system via perturbation. The first time you run the function will be slower. \n",
+    "\n",
+    "Following Dynare, the default pertubation solver is Cycle Reduction, implemented in Numba for faster execution. You can also ask for Gensys if you wish. The original gEcon used Gensys."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b2ceed67",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Solution found, sum of squared residuals: 0.000000000\n",
+      "Norm of deterministic part: 0.000000000\n",
+      "Norm of stochastic part:    0.000000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "T, R = model.solve_model(steady_state=ss_res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "03fc723c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "    \n",
+       "  \n",
+       "
ACIKLYlambdarw
A0.950-0.0-0.0-0.000-0.0-0.0-0.0-0.0-0.0
C0.3100.00.00.4790.00.00.00.00.0
I3.641-0.0-0.0-0.513-0.0-0.0-0.0-0.0-0.0
K0.073-0.0-0.00.970-0.0-0.0-0.0-0.0-0.0
L0.2070.00.0-0.1570.00.00.00.00.0
Y1.0840.00.00.2480.00.00.00.00.0
lambda-0.4640.00.0-0.7180.00.00.00.00.0
r1.0840.00.0-0.7520.00.00.00.00.0
w0.8780.00.00.4050.00.00.00.00.0
\n",
+       "
"
+      ],
+      "text/plain": [
+       "            A    C    I      K    L    Y  lambda    r    w\n",
+       "A       0.950 -0.0 -0.0 -0.000 -0.0 -0.0    -0.0 -0.0 -0.0\n",
+       "C       0.310  0.0  0.0  0.479  0.0  0.0     0.0  0.0  0.0\n",
+       "I       3.641 -0.0 -0.0 -0.513 -0.0 -0.0    -0.0 -0.0 -0.0\n",
+       "K       0.073 -0.0 -0.0  0.970 -0.0 -0.0    -0.0 -0.0 -0.0\n",
+       "L       0.207  0.0  0.0 -0.157  0.0  0.0     0.0  0.0  0.0\n",
+       "Y       1.084  0.0  0.0  0.248  0.0  0.0     0.0  0.0  0.0\n",
+       "lambda -0.464  0.0  0.0 -0.718  0.0  0.0     0.0  0.0  0.0\n",
+       "r       1.084  0.0  0.0 -0.752  0.0  0.0     0.0  0.0  0.0\n",
+       "w       0.878  0.0  0.0  0.405  0.0  0.0     0.0  0.0  0.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ge.matrix_to_dataframe(T, model).round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fdcea8ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
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+       "    \n",
+       "    \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
epsilon_A
A1.000
C0.326
I3.832
K0.077
L0.217
Y1.141
lambda-0.489
r1.141
w0.924
\n",
+       "
"
+      ],
+      "text/plain": [
+       "        epsilon_A\n",
+       "A           1.000\n",
+       "C           0.326\n",
+       "I           3.832\n",
+       "K           0.077\n",
+       "L           0.217\n",
+       "Y           1.141\n",
+       "lambda     -0.489\n",
+       "r           1.141\n",
+       "w           0.924"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ge.matrix_to_dataframe(R, model).round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61d3785b",
+   "metadata": {},
+   "source": [
+    "## Blanchard-Kahn Conditions\n",
+    "\n",
+    "After you have a perturbation solution, you can check the Eigenvalues of the system to make sure the BK conditions are satisfied.\n",
+    "\n",
+    "The output shows the eigenvalues computed by gensys: the modulus, real part, and imaginary part."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "3dd1bea1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Model solution has 2 eigenvalues greater than one in modulus and 2 forward-looking variables. \n",
+      "Blanchard-Kahn condition is satisfied.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ge.bk_condition(model, steady_state=ss_res);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "162cd3aa",
+   "metadata": {},
+   "source": [
+    "You can also visualize the Eigenvalues using `plot_eigenvalues` in the plotting functions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
+   "id": "137a797b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gp.plot_eigenvalues(model, linearize_model_kwargs={\"steady_state\": ss_res});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c2b289b",
+   "metadata": {},
+   "source": [
+    "## Model Statistics\n",
+    "\n",
+    "Functions to compute the stationary covariance matrix, as well as autocovariances for each variable, are also available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c03d28cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cov = np.eye(1) * 0.1\n",
+    "sigma = ge.stationary_covariance_matrix(model, T=T, R=R, shock_cov_matrix=cov)\n",
+    "acorr = ge.autocovariance_matrix(model, T=T, R=R, shock_cov_matrix=np.eye(1), n_lags=30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "id": "d4ec6cbf",
    "metadata": {},
    "outputs": [
@@ -781,141 +1517,141 @@
        "  \n",
        "    \n",
        "      A\n",
-       "      0.102564\n",
-       "      0.078837\n",
-       "      0.344429\n",
-       "      0.099835\n",
-       "      0.007448\n",
-       "      0.140601\n",
-       "      -0.118255\n",
-       "      0.045758\n",
-       "      0.133152\n",
+       "      1.025641\n",
+       "      0.788370\n",
+       "      3.444292\n",
+       "      0.998345\n",
+       "      0.074484\n",
+       "      1.406005\n",
+       "      -1.182554\n",
+       "      0.457577\n",
+       "      1.331522\n",
        "    \n",
        "    \n",
        "      C\n",
-       "      0.078837\n",
-       "      0.097039\n",
-       "      0.225722\n",
-       "      0.150552\n",
-       "      -0.006198\n",
-       "      0.126964\n",
-       "      -0.145559\n",
-       "      -0.022053\n",
-       "      0.133163\n",
+       "      0.788370\n",
+       "      0.970392\n",
+       "      2.257223\n",
+       "      1.505521\n",
+       "      -0.061981\n",
+       "      1.269645\n",
+       "      -1.455588\n",
+       "      -0.220535\n",
+       "      1.331626\n",
        "    \n",
        "    \n",
        "      I\n",
-       "      0.344429\n",
-       "      0.225722\n",
-       "      1.198454\n",
-       "      0.256210\n",
-       "      0.037783\n",
-       "      0.451931\n",
-       "      -0.338583\n",
-       "      0.214950\n",
-       "      0.414149\n",
+       "      3.444292\n",
+       "      2.257223\n",
+       "      11.984536\n",
+       "      2.562105\n",
+       "      0.377826\n",
+       "      4.519312\n",
+       "      -3.385834\n",
+       "      2.149502\n",
+       "      4.141486\n",
        "    \n",
        "    \n",
        "      K\n",
-       "      0.099835\n",
-       "      0.150552\n",
-       "      0.256210\n",
-       "      0.246693\n",
-       "      -0.016902\n",
-       "      0.175123\n",
-       "      -0.225828\n",
-       "      -0.071376\n",
-       "      0.192025\n",
+       "      0.998345\n",
+       "      1.505521\n",
+       "      2.562105\n",
+       "      2.466929\n",
+       "      -0.169017\n",
+       "      1.751230\n",
+       "      -2.258281\n",
+       "      -0.713757\n",
+       "      1.920247\n",
        "    \n",
        "    \n",
        "      L\n",
-       "      0.007448\n",
-       "      -0.006198\n",
-       "      0.037783\n",
-       "      -0.016902\n",
-       "      0.004442\n",
-       "      0.004030\n",
-       "      0.009297\n",
-       "      0.022047\n",
-       "      -0.000413\n",
+       "      0.074484\n",
+       "      -0.061981\n",
+       "      0.377826\n",
+       "      -0.169017\n",
+       "      0.044423\n",
+       "      0.040296\n",
+       "      0.092972\n",
+       "      0.220473\n",
+       "      -0.004126\n",
        "    \n",
        "    \n",
        "      Y\n",
-       "      0.140601\n",
-       "      0.126964\n",
-       "      0.451931\n",
-       "      0.175123\n",
-       "      0.004030\n",
-       "      0.202536\n",
-       "      -0.190447\n",
-       "      0.033062\n",
-       "      0.198506\n",
+       "      1.406005\n",
+       "      1.269645\n",
+       "      4.519312\n",
+       "      1.751230\n",
+       "      0.040296\n",
+       "      2.025356\n",
+       "      -1.904467\n",
+       "      0.330618\n",
+       "      1.985060\n",
        "    \n",
        "    \n",
        "      lambda\n",
-       "      -0.118255\n",
-       "      -0.145559\n",
-       "      -0.338583\n",
-       "      -0.225828\n",
-       "      0.009297\n",
-       "      -0.190447\n",
-       "      0.218338\n",
-       "      0.033080\n",
-       "      -0.199744\n",
+       "      -1.182554\n",
+       "      -1.455588\n",
+       "      -3.385834\n",
+       "      -2.258281\n",
+       "      0.092972\n",
+       "      -1.904467\n",
+       "      2.183382\n",
+       "      0.330802\n",
+       "      -1.997439\n",
        "    \n",
        "    \n",
        "      r\n",
-       "      0.045758\n",
-       "      -0.022053\n",
-       "      0.214950\n",
-       "      -0.071376\n",
-       "      0.022047\n",
-       "      0.033062\n",
-       "      0.033080\n",
-       "      0.110281\n",
-       "      0.011014\n",
+       "      0.457577\n",
+       "      -0.220535\n",
+       "      2.149502\n",
+       "      -0.713757\n",
+       "      0.220473\n",
+       "      0.330618\n",
+       "      0.330802\n",
+       "      1.102809\n",
+       "      0.110145\n",
        "    \n",
        "    \n",
        "      w\n",
-       "      0.133152\n",
-       "      0.133163\n",
-       "      0.414149\n",
-       "      0.192025\n",
-       "      -0.000413\n",
-       "      0.198506\n",
-       "      -0.199744\n",
-       "      0.011014\n",
-       "      0.198919\n",
+       "      1.331522\n",
+       "      1.331626\n",
+       "      4.141486\n",
+       "      1.920247\n",
+       "      -0.004126\n",
+       "      1.985060\n",
+       "      -1.997439\n",
+       "      0.110145\n",
+       "      1.989186\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "               A         C         I         K         L         Y    lambda  \\\n",
-       "A       0.102564  0.078837  0.344429  0.099835  0.007448  0.140601 -0.118255   \n",
-       "C       0.078837  0.097039  0.225722  0.150552 -0.006198  0.126964 -0.145559   \n",
-       "I       0.344429  0.225722  1.198454  0.256210  0.037783  0.451931 -0.338583   \n",
-       "K       0.099835  0.150552  0.256210  0.246693 -0.016902  0.175123 -0.225828   \n",
-       "L       0.007448 -0.006198  0.037783 -0.016902  0.004442  0.004030  0.009297   \n",
-       "Y       0.140601  0.126964  0.451931  0.175123  0.004030  0.202536 -0.190447   \n",
-       "lambda -0.118255 -0.145559 -0.338583 -0.225828  0.009297 -0.190447  0.218338   \n",
-       "r       0.045758 -0.022053  0.214950 -0.071376  0.022047  0.033062  0.033080   \n",
-       "w       0.133152  0.133163  0.414149  0.192025 -0.000413  0.198506 -0.199744   \n",
+       "               A         C          I         K         L         Y    lambda  \\\n",
+       "A       1.025641  0.788370   3.444292  0.998345  0.074484  1.406005 -1.182554   \n",
+       "C       0.788370  0.970392   2.257223  1.505521 -0.061981  1.269645 -1.455588   \n",
+       "I       3.444292  2.257223  11.984536  2.562105  0.377826  4.519312 -3.385834   \n",
+       "K       0.998345  1.505521   2.562105  2.466929 -0.169017  1.751230 -2.258281   \n",
+       "L       0.074484 -0.061981   0.377826 -0.169017  0.044423  0.040296  0.092972   \n",
+       "Y       1.406005  1.269645   4.519312  1.751230  0.040296  2.025356 -1.904467   \n",
+       "lambda -1.182554 -1.455588  -3.385834 -2.258281  0.092972 -1.904467  2.183382   \n",
+       "r       0.457577 -0.220535   2.149502 -0.713757  0.220473  0.330618  0.330802   \n",
+       "w       1.331522  1.331626   4.141486  1.920247 -0.004126  1.985060 -1.997439   \n",
        "\n",
        "               r         w  \n",
-       "A       0.045758  0.133152  \n",
-       "C      -0.022053  0.133163  \n",
-       "I       0.214950  0.414149  \n",
-       "K      -0.071376  0.192025  \n",
-       "L       0.022047 -0.000413  \n",
-       "Y       0.033062  0.198506  \n",
-       "lambda  0.033080 -0.199744  \n",
-       "r       0.110281  0.011014  \n",
-       "w       0.011014  0.198919  "
+       "A       0.457577  1.331522  \n",
+       "C      -0.220535  1.331626  \n",
+       "I       2.149502  4.141486  \n",
+       "K      -0.713757  1.920247  \n",
+       "L       0.220473 -0.004126  \n",
+       "Y       0.330618  1.985060  \n",
+       "lambda  0.330802 -1.997439  \n",
+       "r       1.102809  0.110145  \n",
+       "w       0.110145  1.989186  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -926,373 +1662,583 @@
   },
   {
    "cell_type": "markdown",
-   "id": "018fa592",
+   "id": "6eeafa22",
    "metadata": {},
    "source": [
-    "You can also plot the covaraince matrix as a heatmap using `gp.plot_covariance_heatmap`"
+    "Unlike the stationary covariance, the computed autocovariances will be returned as an `xarray` with a `lag` dimension. This lets you inspect correlations between all combinations of variables and timesteps."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "c29abc39",
+   "execution_count": 20,
+   "id": "8e0db787",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+       "         -1.99743884e+01,  1.10144584e+00,  1.98918619e+01]],\n",
+       "\n",
+       "       [[ 9.74358974e+00,  7.48951154e+00,  3.27207716e+01, ...,\n",
+       "         -1.12342673e+01,  4.34698199e+00,  1.26494555e+01],\n",
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+       "         -1.44733161e+01, -2.00017271e+00,  1.33162741e+01],\n",
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+       "         -3.14743935e+01,  2.03186241e+01,  3.86310319e+01],\n",
+       "...\n",
+       "        [-9.37039422e+00, -9.51519871e+00, -2.89908242e+01, ...,\n",
+       "          1.42727981e+01, -5.48879399e-01, -1.41204407e+01],\n",
+       "        [-5.77747158e+00, -6.86198427e+00, -1.68089017e+01, ...,\n",
+       "          1.02929764e+01,  1.22448203e+00, -9.54775430e+00],\n",
+       "        [ 6.86363871e+00,  6.57946068e+00,  2.16531806e+01, ...,\n",
+       "         -9.86919102e+00,  1.01488083e+00,  1.00129699e+01]],\n",
+       "\n",
+       "       [[ 2.31728760e+00,  1.78120720e+00,  7.78187919e+00, ...,\n",
+       "         -2.67181080e+00,  1.03382919e+00,  3.00838059e+00],\n",
+       "        [ 6.13173839e+00,  6.20827957e+00,  1.89903373e+01, ...,\n",
+       "         -9.31241936e+00,  3.87776936e-01,  9.22464071e+00],\n",
+       "        [ 3.12255571e+00,  7.99013659e-01,  1.22009373e+01, ...,\n",
+       "         -1.19852049e+00,  3.90755574e+00,  2.69986339e+00],\n",
+       "        ...,\n",
+       "        [-9.19760758e+00, -9.31241936e+00, -2.84855059e+01, ...,\n",
+       "          1.39686290e+01, -5.81665404e-01, -1.38369611e+01],\n",
+       "        [-5.79820920e+00, -6.80467604e+00, -1.69569898e+01, ...,\n",
+       "          1.02070141e+01,  1.10020149e+00, -9.51273803e+00],\n",
+       "        [ 6.68717049e+00,  6.40437510e+00,  2.11028087e+01, ...,\n",
+       "         -9.60656265e+00,  9.98090254e-01,  9.75052139e+00]]])\n",
+       "Coordinates:\n",
+       "  * lag           (lag) int64 240B 0 1 2 3 4 5 6 7 8 ... 22 23 24 25 26 27 28 29\n",
+       "  * variable      (variable) <U6 216B 'A' 'C' 'I' 'K' 'L' 'Y' 'lambda' 'r' 'w'\n",
+       "  * variable_aux  (variable_aux) <U6 216B 'A' 'C' 'I' 'K' ... 'lambda' 'r' 'w'
"
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+       "\n",
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+       "...\n",
+       "        [-9.37039422e+00, -9.51519871e+00, -2.89908242e+01, ...,\n",
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+       "\n",
+       "       [[ 2.31728760e+00,  1.78120720e+00,  7.78187919e+00, ...,\n",
+       "         -2.67181080e+00,  1.03382919e+00,  3.00838059e+00],\n",
+       "        [ 6.13173839e+00,  6.20827957e+00,  1.89903373e+01, ...,\n",
+       "         -9.31241936e+00,  3.87776936e-01,  9.22464071e+00],\n",
+       "        [ 3.12255571e+00,  7.99013659e-01,  1.22009373e+01, ...,\n",
+       "         -1.19852049e+00,  3.90755574e+00,  2.69986339e+00],\n",
+       "        ...,\n",
+       "        [-9.19760758e+00, -9.31241936e+00, -2.84855059e+01, ...,\n",
+       "          1.39686290e+01, -5.81665404e-01, -1.38369611e+01],\n",
+       "        [-5.79820920e+00, -6.80467604e+00, -1.69569898e+01, ...,\n",
+       "          1.02070141e+01,  1.10020149e+00, -9.51273803e+00],\n",
+       "        [ 6.68717049e+00,  6.40437510e+00,  2.11028087e+01, ...,\n",
+       "         -9.60656265e+00,  9.98090254e-01,  9.75052139e+00]]])\n",
+       "Coordinates:\n",
+       "  * lag           (lag) int64 240B 0 1 2 3 4 5 6 7 8 ... 22 23 24 25 26 27 28 29\n",
+       "  * variable      (variable) \n",
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0123456789...20212223242526272829
A1.00.9500000.9025000.8573750.8145060.7737810.7350920.6983370.6634200.630249...0.3584860.3405620.3235340.3073570.2919890.2773900.2635200.2503440.2378270.225936
C1.00.9943280.9875980.9799040.9713340.9619690.9518870.9411580.9298510.918027...0.7681050.7536790.7392490.7248360.7104620.6961450.6819030.6677500.6537010.639770
I1.00.9367040.8769750.8206220.7674660.7173340.6700640.6255030.5835050.543930...0.2356200.2166470.1988400.1821340.1664680.1517840.1380260.1251430.1130850.101806
K1.00.9992130.9969760.9934070.9886160.9827070.9757770.9679160.9592110.949739...0.8132440.7992040.7850590.7708410.7565770.7422910.7280070.7137470.6995300.685374
L1.00.9424140.8879370.8364070.7876680.7415760.6979920.6567830.6178260.581002...0.2883880.2698650.2523920.2359140.2203760.2057280.1919230.1789140.1666580.155116
Y1.00.9673060.9357220.9052100.8757320.8472500.8197290.7931350.7674350.742598...0.5184010.5018430.4858290.4703410.4553610.4408720.4268570.4132990.4001840.387497
lambda1.00.9943280.9875980.9799040.9713340.9619690.9518870.9411580.9298510.918027...0.7681050.7536790.7392490.7248360.7104620.6961450.6819030.6677500.6537010.639770
r1.00.9364850.8765550.8200180.7666920.7164050.6689940.6243050.5821900.542510...0.2335990.2146080.1967880.1800740.1644030.1497170.1359620.1230830.1110330.099764
w1.00.9783040.9568370.9356120.9146440.8939430.8735190.8533830.8335410.814000...0.6200350.6043440.5889750.5739250.5591920.5447720.5306650.5168650.5033700.490176
\n",
-       "
9 rows × 30 columns
\n",
-       ""
-      ],
+      "image/png": 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4yn7h1q9fldu3HhMTI/+HnMfGg7Tv0CCFxPr7bdt0Fr9X/oyb0umPvm90dAx37jzF0VE2uTk42uHtlfJ5tsnzGK9efWDKtN5y1x89cgV7+1IMH7aYfHmaUr5sV+a7bCIuLvmPpz/pvs9Lbng/oWoK31ESiYSL5+/x4tkHqla3+cPRCSlJ6+dZinbt2kWxYsUoVqwYXbt2ZdiwYUydOhUVFRW59aOiooiKipIp09LSQktLK6MhJBMUEIyZmWz3k5m5AbGxcXz5HIZFbiMAQkMjKFqgG1FRMaipqbJ05ZBkSe5PCAwMxtzcQDZeM0NiY+P4/DmM3LmNCAwIxixpHXMDAgO+Zuq9P38OJS4uPtnxMjczJDAwOM3t/f2/cOrkDTy3yrYEHepXZMWyfVSvUZqChfLwz7m7HD3sRVxcfKbiBXjl+4GhLRYRHRVDDh0tZqzvT4GiueXWff8qiA0uB1m23wk1dTW5dR7efMnxndeStWYz6/PnYOLi4jAzN5IpNzc3JjDgs9xtAgM+Y24u28thZm5EbGwsnz+HkDu3bNfkjRsPePTwBW7rnZUau6JevvjIrKmbOHpuHuopHN/f5fPnkITja5bk+JoZEhD4Re42z5+/Y8okV85dWIO6uvyvPT+/j1z45w6dOtfn0JFFvHjxnhHDFhMbG8fkqfIT8u9kU6gXXz6FEhsbz/gpHeneu77M+rDQCGwK9pJ+jy1cPpA6DuVT2Jvwp2W45eru7k7XrgndDw0bNiQ8PJxz51IeGOTi4oK+vr7M4uLiktG3T1HS3P6z1/vXcl3dHFy7uYpL15bhPLMHE8eu59LF+0qPRSFJApYXb9IfLBKJJMUfMel/+4zte+um0xgY5KJ5km7vRUsHU6iwJWVt+qCXoxGjRqyie8/6qKllfuxcvkLmrD81kdWHx9K8Ww3mj9rM62fJr2HGxcUzZ9hGeoxuQr6C5nL2BN/DI5k73JPRC7qgb5Qr07HJk95jK6++vHJIaLXalCqMnV1pJUSaPnFxcfTvsYjxUztTuIjlH3//n5IfL/nHKi4ujh7dpjPVuQ9Fi6Z86So+XoKZmSFr1o6jgm1x2ndwYPzEHritO6js0BVy/KwL568tYfHKQaxddYR9uy7KrM+lm4OLN5Zx7upiJs/oypTxG7lyMeUBicKflaGW69OnT7lx4wb79+9P2Im6Oh06dGDjxo04ODjI3WbixIk4OcleL1BmqxXAzCJ5q+tTUCjq6moYGetJy1RVVSlUOA8AZcoV4umTtyxesJuatcooNZ60mJsbEhiQJN5PIairq2H8b7zmFnLqBIViZp65ASImJvqoqakSGCjbAg76FIKZmUGq20okEjZ5nqRTFwc0NTVk1pmaGrBn/wwiI6P58iWMPHmMmTJxAwWsLTIVL4CGpjqW1mYAFCubn6f33rDf/R+c5neWqfcjPJKn997y/OF7VkzZnRBzvASJRIJD/qEs2D4MPYOcBLz7wuRerol/V3xCMnPIP5RNF52xLJCxgSwmJoaoqakRGCDbigoK+oqZubHcbcwtTAhI0qr9FBSMuro6xsb6MuXfv/9g965TOM9QbKCfsoV/+4HP7Rc88HnF+JEJ4xvi/z2+Zjot2Xt0htwBTspiYmKQcHyTtFKDPgVjnqQ1C/Dt23du33qCz93njBy+9N9445FIJOTUqsmxE0upU9eW3BbGqGuoo6aW2BIvXjw/AQFfiI6OSXau/275//3MlCxVgE9BIcyfvZM2HRLHKKiqqlKwUML3WOmyBXn25D1LF+6leq0//4NLSC5DydXd3Z3Y2FgsLRN/tUokEjQ0NAgODsbQMPkXv7K7gOWpXLkEx49dlyk7d/YOFWyLpDpAQSKBqKjUByj9Dnb2xTmRNN4zsvFWti/B+XN3GDayVWKds3eoXKVEpt5bU1OD8hWKcv7sHVq0rC4tP3/2Dk2bpTwIC+Dyxfu8fPGRnr1THiWura2JpaUJMTGxHDxwhTZta6ZYN6MkEoiJTn4tPaeuNu5np8iUHdp8kbtXnzF9XT8srIxRU1VNVmfjwsN8D49i6Ix2mOXJ+I8XTU0NKtiW4OxZL1q2qistP3f2Os2a15a7jb19GY4dvSRTduaMF7YVS6KhIfulvmf3aaKiounSpUmGY8wMXb2cXLm9UqbMfd1xLl+4j+eOCVgVkN9boCyamhpUqFCMs2dv0qJlYrI5d/YmzZpVT1ZfT0+HOz5bZMrWrt3PhX9us3PXHApYJ1xaqFK1NLt2niE+Ph5V1YSelufP35E7t/EfT6xJSSSSNL+jJBIJ0VnwPSbIl+7kGhsby+bNm1m8eDH168teA2jTpg3btm1j6NDMz7sDCA//wasXiVMt3rwO5L7PSwyNdMlnZYbzZA8+fvzCeo8xAPTp35h1rkeYMNaNnr0bcuP6EzZ7nMZjyzjpPhbN30UF2yJYF8xNdHQsp0/eZPvWcyxbNUQp8b78Jd7XfoHc83mJ0b/xTpvswccPX9jgmRBv3/5NWLfmCOPHuNGrT0Oue/uyyeM0nlvHS/cxeGgL6tcdy+KFu2narApHj3jxz7m7nL2wKNPxDh/Vhj495lPBtiiV7Uvgvv44794G0XdAUwCmTnLn48fPuHuOl9nO0+MEleyKY1PKOtk+b1z35ePHz5QtW5gPHz4zZ+Zm4uPjcRrbIVOxbph3CLs6NpjlMeR7eCT/HL7FPa9nzNuacK6tdznI54AQJi7viaqqKtbF88hsb2Csi6aWhkx50jq59HLKLc+IkSO70bPHZGxtbbCvUoYN6/fx9q0//Qe0BWDypBV8+BCE56bZAPQf0I41q3cyZvQi+vRtjbfXfTw2HmDrtnnJ9u3hcZAWLepgbGyQ6Th/Cg//gd/LxC72t68DeXDvFYaGuuS1MmXmlE34f/yK68ZRqKqqUsImv8z2pqb6aGtrypRHR8fw1Pddwv/HxOL/8SsP7r1CJ5e2tMWVUSNGdaBXj1nY2hansn0p3Ncf4t3bQPoNSPgROmWSKx8/fmaj51RUVVWxKSU78M3M1BBtbU2Z8v4DW7Fm9V6cRi1j8JC2vHj+ngXzNjNkaLtMxZr02L5J5dgCbFh7jLz5TClSLC+QMO911bKD9B/UVLqPpQv2UK5C4YTvsZhYzpy8xa5t/7BoRdb0ZgjJpTu5Hj16lODgYPr06YO+vmx3Vdu2bXF3d1dacr1z+zmNHROnb0wYux6ALt0cWOfuREBAMO/efZKuL2Btwb7DM5kwxg0316PkzmPMwqUDaNk68ddsREQko4av4cP7z+TIoUnRYvnY4DmGtu1lp4RkNN5GDomJaMJYN2m8bhtHE+D/lffvEueQFrC2YP+RmYwf7Yab6xFy5zFm0dKBMvHaVy3Jpm0TmOm8mVnOWyhYKDebt0+kUuXimY63XfvafP0SxtzZWwnw/4pNqQIcPDJHOvo3IOAL75LMeQ0NjeDg/issWip/Lm9UZDQzpnni98qfXLly0KCRHe6bxmNgkLnrmsGfwnAZ4cnXoDB0dLUpWMKSeVuHUrFmQgv+a1AYQR/SHoj1p7Tv0IAvX0OYM3sd/v6fsSlVmCNHV5E/f0JS8ff/xLt3iV+41taWHDm6itGjF+G6Zhd58piydNl4WreRvczy7Nkbrl65y4mTriiTz+0XtGiQOG1oyjh3ADp2rcvqDSMJDAjmwy+fNUUEfPxK7cojpa9XLT3AqqUHqFajFIfPpHxDD0W0a+/Aly9hzJ3tgb//F2xKFeTQkUXkz5/QlZpw7gama5/58plz7MQyxo5eTsXyPchjacLQYe0YMy751Jb08Ln9guZyjm2nX47t+1+ObXy8hJlTN/P2dSBq6mpYF7TAeXZ3evZN7Cn6HhHF2BFr+fjhC9o5NClSLC9rPZxo3a5GpmIVlCfd81ybNWtGfHw8x44lv5PQnTt3sLW15fbt21SooPjoW0XmuWadjM1zzTqKz3PNepmb5/qnpWeea/aQsXmuWSG981yzXsbmuWYVMc/1z0t3y/XIkSMprqtQoQKZuCeFIAiCIPwnZOt7CwuCIAjC30gkV0EQBEFQMpFcBUEQBEHJRHIVBEEQBCUTyVUQBEEQlEwkV0EQBEFQMpFcBUEQBEHJRHIVBEEQBCUTyVUQBEEQlEwkV0EQBEFQMpFcBUEQBEHJRHIVBEEQBCXL0MPSlS9rH0ScOnmxiXiVI3lc6qpaWRCHYuTHpvbH41Bc8thUsum5ID+ubPL1JFfy2LLrsYXsHdt/VbofOScIgiAIQupEt7AgCIIgKJlIroIgCIKgZCK5CoIgCIKSZYsRA99jLwExWR1GCjTIqV5TpuRV2CHiJVFZFE/qVFW0KKjXQqbsa+Q5JERnUUQpU0ETI+16MmUvQg8Sl02PrZqKFoX1W8qUxUleAXFZEk/a1FBTKShTEvT9dLY9F8xy1k9SGghk1yEhKoC5TElAxAnis+GxBVBFEwudRlkdxv+VbJFcExJrdk2uycVLorJtApBHQnS2/EKVJ+4vO7YJiTW7JtfkJERnywQgvwtNAsT/2UAUljzi+Gx6bIWsIbqFBUEQBEHJRHIVBEEQBCUTyVUQBEEQlEwkV0EQBEFQMpFcBUEQBEHJRHIVBEEQBCUTyVUQBEEQlEwkV0EQBEFQMpFcBUEQBEHJsskdmgRBEIS/WWRkJNHRit2hSlNTE21t7d8cUdYSyVUQBEHIlMjISHLo54JoxW4FamFhgZ+f3386wYrkKgiCIGRKdHR0QmKtVQTU07jaGBtPwMXnREdH/6eTa7a+5nrl8gPatnSmkFUXdDQaceTQtTS3uXzpPtXshmGUqzk2RXuxYd2xZHUO7r+CbZn+GOo0w7ZMfw4fvJrpWNctOULbus5UyNefqkWGMKTLMl4991d4+zvez7Ax6UnLGlNkyrs1nUtxw+7JlgHtF2cq3mtXHtGlzWxKWffCNEdLjh/2TrX+1UsPMM3RMtny/Ol7aZ0nj9/Ss+M8KhTrh2mOlqxdeThTMf5q/dIjtK83nUpWA6hRdCjDui7HL43je+OKLzZGPZItr559lKkXFhrBrLGbqVViOOVz96VZ5QlcOnNPabH/FBwcSo/u4zAyqISRQSV6dB9HSEhYqtsc2H+aRg37Ym5aBXXVEvj4+Mqsf/36A+qqJeQue/ecUDg27yuP6N52LuUL9SWPThtOHLmeav2R/VeSR6dNsqV2xRHSOru2nJdbJzJS+Te3Dw4OoVu3YejrF0dfvzjdug0jJCQ01W1UVCzlLgsXukrruLltpXbttujpFUNFxTLNfcrjfeUxvdrNw7bwAPLlas/JIzfS3MZz3UnqVBhFYZMu1Co/gr3bL8qs3+5xltaO0yiVtxel8vaiU9NZ3L31It2xKZ2GKmiopbFk67SjNNm65RoREUnpMgXp1qM+ndvPTrP+a78AWjebRs8+DXHfNBbva48ZOWw1Jqb6tGxdHYDrXr507+zC1Bndad6iKocPXaNbJxfOXlhEpcrFMxzrzWtP6NzXgdLlrYmLjWfp7D30bb2Ao97zyKmjleq230K/M36QG/a1SvIlSPbLduWW4cREx0pfh3wNp2WNKTRoaZfhWAG+R0RiU9qaTt3q0avTfIW3876/mly6OaWvTUz1pP//43sUBawtaNG6GlPGb8xUfEndvPqUTn3qUbq8NbFx8ayYvZd+bRZy2MslzeN77MZ8dHQTfyEbmSTGHB0dS9/WCzE20WOpx1AsLI3w//AVnVzK/0XdtctYPrwP4NgJNwAGDXCmR/fxHDrsmuI2ERE/qFq1PG3bNmBA/2nJ1ufLZ8H7j5dkyta77WbRwo00bFQzWf2UfI+IwqZ0ATp2q0vfzgvTrD9zYW8mzewqfR0bF4+jvRNNW1WVqaerl5PLd1fIlGlrayocl6I6dx7K+/f+nDy5FYD+/cfTrdtwjhzZlOI2/v53ZV6fOPEPffqMpk2bxtKy799/0LBhbRo2rM3EiS4Ziu3H9yhKlCpA+6516N8l7R/Fm9efZv70HcxfOYCytoXwufWC8cPWoW+gg2PjigB4XX5Mi3bVqGhfDC0tDVyXHaJri9mcvbmE3HmMMhSncqiAikradf4PZDi59uzZk5CQEA4ePCgt27t3L127dmXmzJmMGzcu08E1aFiJBg0rKVx/g9sx8lmZsXDJQACKl7Dizu3nLF+yT5pcV688SF2HCowd3wGAscU7cOXSA1atPMimyhMyHOuGvWNlXrus7kfVIkN55ONHpWqpJ23nUR40bWuPqpoq547dkVlnYJhL5vXx/d5o59SkYYvMJVeHBrY4NLBN93YmpvroG+SSu658xSKUr1gEgFlTN2cqvqTc9o6ReT17VV9qFB3G43t+VKya+vE1MtVFT19H7roD2y4RFhzOtpNT0NBI+DjkyWeinKB/4ev7klMnL3PVayeVK5cFYK3bTKpX7cTTp34UK2Ytd7uu3RKezfv69Qe569XU1LCwMJUpO3TwHO3bNyRXLvl/szx1G1SgboMKCtfX09eROaYnjlwnJDiCjt3qyNRTUQEzC0OF95sRvr7POXnyH7y9j1C5csLfsH79AqpUac7Tpy8oVqyw3O0sLMxkXh86dIo6dapSsGB+adnIkf0AuHAh7V6zlNSpX5469csrXH//zkt06e1A87YJP1TyW5tz9+ZzXJcekibXlRuHy2yzYNVAjh+8ztULD2jbuVaGY800FdLOnf8fuVV53cIbNmygS5curFq1SimJNSNueD+hroPsF4RD/Qrcuf2cmJiE1t91b1/qJa3jaMt1L9nutsz6FvYDAH1D+Ynop33bLvH2dRBDxrdSaL97t1yicWv7NFtrv0tdeydsrHvRutFUrlx8kCUxwC/HN4VE/6u2taZRq8Rwerecz/XLsv/O/5y4S9lKhZk9djM1iw2jRdVJuC05Qlyccp8j6u3lg76+rjSxAtjbl0NfXxeva3dT2TJ9bt9+hI+PL736tFXaPhWxY9M5atQpQ14r2YQVER5JpeIDsC3Sj+5t5vLA55XS39vL6zb6+nrSxApgb2+Lvr4e167dVmgfgYGfOHbsHH36dFJ6fOkVHRWDlpaGTJl2Dk18br2Qfo8l9eN7FDExscl+jP9xqiqKLf8HlJJcFyxYwNChQ9m+fTt9+/ZVxi4zJDAwGHNzA5kyMzNDYmPj+Pw5obs1MCAYs6R1zA0IDPiqtDgkEgnzJm/H1r4oRUvmTbHe65cBLJmxm4VuA1FXV0tzv/dvv+S573vadfvzv0zNLYxYsnowG3eMx3PHeAoXtaR1o2lcu/Loj8cikUhYMGU7FeyLUiSV42tqbsD0pb1YtmkYyzcNp0BhC/q0nM+ta0+kdd6/+cTpw7eIj5PgusuJAaOb47n6BOsWK+96MUBAwGfMzJJ315mZGREQ8Flp7+PhvpcSJQpRtariLaXMCvQP5p/Td+ncs55MeeFieVm2biieuyeyxnMUWtoatHCYzKsXH1PYU8YEBARhZmacrNzMzJiAgCCF9rFp0x50dXPRunUjpcaWETUdyrJz03nu332FRCLh3p2X7Nr8DzExcXz98k3uNi7TtmGRx4jqdUr/4WiTUFFw+T+Q6WuuEyZMYPXq1Rw9ehQHB4cU60VFRREVFSVTpqWlhZaWkltgSfr7JRJJsmIVOXWSlmXGrLGbefroHdtPTEmxTlxcPGP6uTJsQmusC+dWaL97t1yiSIm8lLEtpKxQFVa4qCWFi1pKX1eyL86H959Zs+wgVavb/NFYZo/bwrNH79lyfHKq9ayL5Ma6SOKxLWdXmIAPX/FYdULalRwfH4+RiS7Tl/VCTU0Vm3LWBAWE4LHqOIPHtUwzlhnTVzBr5qpU63jf2AMkP+9Auefejx+R7NhxjMlTBillf4ravfUf9Ax0aNhM9lKFrV1RbO2KSl9XqlKc+lXHsnHtCWYv6pPmfqdPX8SMGalfo7x58ziQ+WO7ceNOunRplS1Gr44Y35ZPgSG0qDMZiUSCiZk+7brWwnXpYdRUk7eHXJce4tDeq+w5Mf23XM9OFxUFrrkq8bs2O8tUcj1x4gSHDh3i3Llz1K1bN9W6Li4uzJgxQ6bM2dmZ6dOnZyYEGebmhgQGBMuUffoUgrq6GsbGCYNYzC3k1AkKxcxcOdeFZo3bzPkTd9l6fDIWlikPLIgI/8HDu3743n/DrHEJ1yfj4yVIJBJsTHrivn8c9jVLSuv/+B7F8f3eDJ/UWilxKoOtXTH27riYdkUlmjN+CxdO3GXTsUmpHt+UlK1YiKO7E6+fmZoboK6hhppa4pdWoaK5+RwYSnR0LJqaqX9EhgztSoeOqbd2ChSw5MH9pwQGfkm27tOnYMzNk7e6MmLf3lN8/x5Jt+4tlLI/RUgkEnZuOUfbjrXQ1NRIta6qqirlbAvj90KxUfRDh/aiY8fmqdYpUCAv9+/7EhiYvPX/6dNXzM1N5Wwl6/Ll6zx9+pJdu1IeWPYn5cihyWLXwcxb0Z/PQaGYWRiybeNZcunmwMhEV6bu2uWHWbXoANuPTKVEqfwp7PEPEtdcpTKVXMuUKcPnz5+ZNm0alSpVQldXN8W6EydOxMnJSaZM2a1WO/vinDgmO4Xg3Jk7VLAtIh2sUtm+BOfP3WHYyMRrnOfO3qFylRKZem+JRMKscVs4e+w2m49MJG/+1D/UuXRzcPjqXJmyHe5n8b7sy3LPYcm2P3HwBtHRsTRrLzsaMys98HmF+W8erPKTRCJhzvgtnDt2G8/DaR/flPjef4OJhYH0dfnKRTi215v4+HhU/20VvH4ZiKmFQZqJFcDExAgTE/0069lXKUdo6Ddu3LiPnV0ZAK5fv0do6DeqKKkLd+PGfTRrXgdT0z83WtTr8iP8XgbQqUe9NOtKJBIe3fejuI1iScDExBgTk7TPrypVbAkNDePGjbvY2SUcy+vX7xAaGkbVqmkP2nN334GtbRnKlv2zPTBp0dBQJ7dlwg+vw3uvUq9hBek5CrB22WFWLNjH1oOTKVvhz/dmySWSq1SmkqulpSX79u2jTp06NGzYkJMnT6aYYDPSBRwe/oOXv1yfee0XyD2flxgZ6ZLPyoxpkz34+OELGzwTRpL27d+EdWuOMH6MG736NOS6ty+bPE7juXW8dB+Dh7agft2xLF64m6bNqnD0iBf/nLvL2QuLMnAEEs0cs4mje71ZvX0kOrm0+RQYAiRMRdDOkdBVs3jGboL8g5m/dgCqqqrJrscameqhpaUh9zrtvi0XcWhcAUOjlH/ApEd4+A/8Xia2IN6+DuLBvVcYGuqS18qUWVO3EPDxC6vdRwKwduVhrPKbUaykFTHRsezZcZGjB73w2JF4bKOjY3jq++7f/48l4ONXHtx7hU6uHBQspFjXd0pmjd3M8b3erNw2gpwpHN+lMxOOr4vrAAA2u57C0sqEwsUtiYmO5ciea5w5cotlm4ZJ99uhV122rT+Ly8RtdOnnyJtXAaxfeoQu/R0zFW9SJUoUokHDGgzsP401a6cDCVNxmjStLTNS2KZEY+bMHUXLVgnv//VrCG/f+vPxY8K1w2dP/QCwsDCRGSX84sUbLl+6xZFj6zIUX0T4D/xeBkhfv3sdxMN7fhgY5SJvPlPmTttKwMevrNggO0p1x6ZzVKhUhOI2Vsn2uXjubmwrFcG6cG6+hf3A3fU4j+6/Zu7SfhmKMSUlShShYcM69Os3lnXrEqaV9e8/nqZNHWRGChcvXhMXl4m0apXY0xAW9o09e46yeHHyaU6QcD03ICCIFy9eA/DgwRN0dXWwssqHkZGFQvFFhEfy+tUvx/ZNEI/uv8bAMBeW+UyY57ydgI9fWbZ+KACvnn/E5/YLylcsQkhIBOtXHuWp7zuWug2R7sN16SEWzdrFyo3DyZvfjKB/Pw86Otq/ZRqZwkS3sFSmr7laWVlx8eJF6tSpQ/369Tl16hR6enppb6iAO7ef08gh8ct7wtiE+YFdujngtnE0Af5fef8uccBCAWsL9h+ZyfjRbri5HiF3HmMWLR0onYYDYF+1JJu2TWCm82ZmOW+hYKHcbN4+MVNzXAF2bDwPQPemsq3Ruav70bpzDQA+BYbw8X3yrsG0+L3w57b3M9z3K28U9r07L2jZYKr09dR/56V26FqHVetHEBjwlffvPknXx0TH4jzRk4CPX9HOoUmxEvnYfmAKjg0rSusE+H+lrn1i78TqZQdZvewgVWvYcOj0nEzFu+vf49uzmexcw9mr+tJKenxD8X+fODAtJiaWhdN2EuQfjJa2JoWLW+K6y4majokjdnPnNWb93rHMn7ydVjWmYJ7bgK4D6tNnRJNMxSvPlq0LGDliLo0aJAz6a9a8LitWyl6Xf/rUj9DQcOnrI4f/oU/vSdLXnTuNBmDqtCE4Tx8qLffYuB9LS3Pq16+Wodju3XlJ20bO0tfTJ3gC0L5LbZa5DSMoIJgP72W7XsNCIzh2yJtZC3vL3WdYSARjh63lU2AIuno5KVXWmv2nZ0mnaynTtm0rGT58GvXrdwagefP6rFolOzf+6dOXhIbKziPfufMQEomETp1ayt3v2rVbmDFjifR1zZoJl2U8PJbRs6divV3377ykfePES2IzJyRcBmrbpRZL1w0hMCCYD+8Sj21cXDxuK47y8vlHNDTUqFLThoNnZ5Mvf+JI7M3rTxMdHcuAromxAYya2Banye0Viuu3EC1XKRXJzxE/6ZR0nuuHDx+oU6cORkZGnDp1Cn39tLvKfvoeew6IyUgYf4AGOdVlu7xehO4mThKVQv2spaaiRWF92Q/Xl8gTSFD+XXEySwVNjLVlr1c+DdmVrY9tMYMOMmVxkueAYvdT/fPUUFORTWSB348Snw3PBVU0Mc/ZNElpAKDcKVHKowrItlw/RhzKlscWEo5vHp3fdy0+LCws4Tu/eamEuzClJiYODj8kNDRUaQ2x7Ehp81wtLS25ePEiISEhODo6EhISoqxdC4IgCH8DMc9VKsPdwp6ensnKcufOzZMnT5JXFgRBEP77VEm7yfb/cWvh7H1vYUEQBOEvIq65SonkKgiCICiHGC0sJZKrIAiCoByi5SolkqsgCIKgHKLlKiWSqyAIgqA8/x+5M00iuQqCIAjKIVquUiK5CoIgCMohrrlKieQqCIIgKIdouUqJ5CoIgiAoh2i5SonkKgiCICiHmkrCklad/wMiuQqCIAjKIVquUiK5CoIgCMohrrlKZZPkqpHVAaQieWyqKul76PufJC82FTSzIJK0yYtLLRsfW/mxpfF4rSyVPDYVNLPlfdPln6MqZN+7vCdPEKrZ9HMGfzA20XKVyvDzXAVBEAQBfnmeay9b0EyjzRYdCx63//PPc80mLVdBEAThrydarlIiuQqCIAjKocAlV4lIroIgCIKgOBUVFVQUGND0/3AtMlsk18i4a0BMVoeRAg201arKlHyNPIeE6CyKJ3UqaGKkXU+m7HvsZbLn8dUgp3oNmZK34UeJl2TPY6uqoolVrqYyZbHxz4C4rAkoTWqoqxaVKfkWfQFJNjwXVNBAV7O2TFmc5BUQnyXxpE0VNZWCMiWhUeez5bGFhOOrr1X397+PAi1XVEh3cr106RILFy7k9u3b+Pv7c+DAAVq2bCldL5FImDFjBm5ubgQHB1O5cmVWr16NjY1Nev8EpckWyTXhiz97npTySIjOtslVvr/n+MZLoomXRGV1GOkQR/ZNrslJiPmLzt14xLH9u6gq0HKVqKik+ydTREQEZcuWpVevXrRp0ybZ+gULFrBkyRI8PT0pWrQos2fPxtHRkadPn6Krq5vOd1OObJJcBUEQhL+doi3X9GrUqBGNGjWSu04ikbBs2TImT55M69atAdi0aRPm5uZs376dAQMGpP8NlSC7TiITBEEQ/jKqqKCqksbyb3YNCwuTWaKiMtZj5efnR0BAAPXr15eWaWlpUatWLa5du6aUvysjRHIVBEEQlOJnyzWtBSBfvnzo6+tLFxcXlwy9Z0BAAADm5uYy5ebm5tJ1WUF0CwuCIAhKkZ5u4Xfv3sncREJLK3N3Z0t6rVcikaQ9cvk3EslVEARBUApFp+IA6OnpKeUOTRYWFkBCCzZ37tzS8qCgoGSt2T9JdAsLgiAISpGebmFlsba2xsLCgjNnzkjLoqOjuXjxIlWrVk1ly99LtFwFQRAEpUhPyzU9wsPDefHihfS1n58fPj4+GBkZYWVlxciRI5k7dy5FihShSJEizJ07l5w5c9K5c+d0v5eyiOQqCIIgKMXvmopz69Yt6tSpI33t5OQEQI8ePfD09GTcuHH8+PGDwYMHS28icfr06Syb4woiuQqCIAhKokrauTMjtz6sXbs2qT3ATUVFhenTpzN9+vQM7P33EMlVEARBUIrf1S38NxLJVRAEQVCK39Ut/DcSyVUQBEFQDvHIOalsPxVnnethihfuhoFOY6raDebK5Qcp1u3XewE51B2TLRXK9JXWiYmJZe6sLZQs2h0DncbYVRjA6ZM3lRLrtSuP6NJmNqWse2GaoyXHD3unWv/qpQeY5miZbHn+9L20zpPHb+nZcR4VivXDNEdL1q48rJRYf3JzPULJIj0wytWManZDuXrlYar11605TIXS/TDWbU45mz5s23JWZn3DemPR0WiYbGndfGqm4nRdfIiWtadQxrI3lQoNZEDnxbx6/jHVbU4dvkH3FnOpVHAAZfP2oa3DNC6dvZesXlhIBM6jPbAvOpgSZj2oX2kM/5y+m6l45QkODqVH94kYG1bB2LAKPbpPJCQkLNVtJBIJM2eswSpvXXR1KlKvbi8ePXqRrJ6Xlw+ODn3Q17XDxKgq9er24sePSIVju3r5IR1azaB4ge4YaDXl6CGvVOsfPniNlo2mUMiyM/lM2uFYczTnTt+WqdPEcQIGWk2TLe1bTFc4LkUlHNtxGBlUxMigIj26j0v12MbExDBh/CLKlWmGXq7y5LOsQc8e4/n4MVBa5+vXEEYMm0XJ4g3R1SmHdf46jBw+m9DQb+mK7drlh3RqPYuS1j0x0m7OsTS+F7yvPqZh7XEUytOFPAZtqVxmEGtWHJKpc+TgNepWdaKAeSfyGrWjpt0Idm37J11x/Q6qqioKLf8PsnXLdc/uC4x1cmX5qmFUqWrDhvXHaNl0EnceuGNlZZas/qKlQ5g1NzGRxsbGUbnCAFq3qSktmz7Vgx3bz7Fm7SiKFbfizOlbdGg7nX8uL6dc+cKZivd7RCQ2pa3p1K0evTrNV3g77/uryaWbU/raxDRxYvWP71EUsLagRetqTBm/MVPxJbV390XGjV7HspVDsK9qg/v647RqOoXb993IJ+f4rl97FOcpnqxaOwLbikW5dfMpQwcux9AwF42b2gOwfc80oqMTn8Dz9UsY9raDadWmRrL9pcf1q7507edImQqFiIuNY/Gs3fRoNY9T1xeQU0db7jY3rj2hWp3SjJnWAV2DnOzbepH+HRex79wsbMoWACA6OpbuLV0wNtVj1eYR5M5jhP+HL+jkypGpeOXp1mU87z8EcvS4KwCDBs6gZ/dJHDy8KsVtFi3cyLKlm3HfOJsiRfPjMseNRg3688j3CLq6OkBCYm3aeBDjJ/Rh2fKJaGpqcP/eU1RVFf/t/D0iktJlCtKlhyPdO8xNs/61yw+pU68c02Z1R19fh22bz9Kx9SzOXllM2XKFANi6azLR0bHSbb5+DaN6xWG0aFNd4bgU1bXLGD68D+DYifUADBowjR7dx3Ho8Fq59b9/j+Tu3cdMnjKYMmWLERwchtMoF1q1GMz1m/sA+PgxiI/+QcxfOI6SJQvz5s1Hhgxy5qN/ELv3rFY4tojvUZQqbU3n7vXo0XFemvVz6mjRb1ATSpa2RienFt7XHuM0dA05c2rRs29DAAwNdXEa344ixfKiqaHOqRM3Gdp/OSZm+tRzrKBwbMqmyDXXrLxr0p+kIkltCFYqAgICmDNnDseOHePDhw+YmZlRrlw5Ro4cSb169dLewS8i4y4i75FoNaoMo3yFwqxYPUJaVq5Ub5o1r8asuX3S3O/hQ1fp2HYGvi+2kD9/wp06rPN1YPzEzgwc3EJar11rZ3LlyoHH5gly9qKBtlotmZIvkSfSfLSUaY6WbNo1gcbN7VOsc/XSA1o2mMoL/63oG+RK8++pUKwf/Yc2Y+Cw5inWUUETY23Zp0d8jz2PvONbq+oIypUvzPLVwxLfo3Q/mjavwsw5vZPVr1tjFPZVSzJ3fj9p2Vintdy9/YyzF5fIjWfV8gPMnrGFl++2o5MsCWqQU132GZOvv+1X6JFzXz6HYVdoIDuOT8WuWok06//UsPJYmrSuwrDxCU/P2O5+lvUrjnL61iI0NFL/ramqokUB3dYyZbHxvijyWDRf31eUKdWCK9e2UblyGQC8ve9Ro1pXHj4+TLFi1sm2kUgkWOWty/ARXRk7LuF8j4qKxjJ3bea6jKT/gPYAVKvaBQcHe2bMHJZkD2qoq8oem7DoM2meuwZaTdm6ezJNW1RJ8+/6lX25wbRqV4PxkzvJXb9mxSFcZm7lyZstyc4FFTTR03SUKYuTvECxY/uS0jZNuOq1i8qVywLg7e1D9aodeeR7nGLFCqaxhwQ3bz6gSuV2vHp9HiurPHLr7N1zku7dxhIWfh8tjeIy60KiTqV5bI20m7Nl9ySapPK9IE/3DnPJmVObtR5OKdapbT8Sx4YVmTy9a7J1KmhioNUgXe+ZHmFhYejr62M6phqqWql/juKjYvm06CqhoaFKuUNTdpWhbuHXr19ja2vL+fPnWbBgAQ8ePODkyZPUqVOHIUOGKCWw6OgY7t55Rj1HW5nyeo62eHs9UmgfmzaepG698tLEChAdFYO2tqZMvRw5NLl2NfXu0N+prr0TNta9aN1oKlcuptztrUwJx/d5sl+5dR0qcN3LV+42USkcu1s3nxETEyt3m00ep2jbvpacxJo530K/A6BvmPaPkp/i4+MJD49E31BHWnb2xG3K2xXBebQHdoUH0tB+HGsWHSQuTrkP6fb2uoe+vq40sQLY25dFX18Xr2s+crfx83tPQMBnHBwT7zKjpaVJzZq2eHkldG8HBX3hxvX7mJoZUaN6Vyxz16JunZ5cuXJHqfGnJeHY/sAwlX+PrZ6nad2uptLPBW8vn3+PbVlpmb19uX+PreLd+6Gh31BRUcHAIOUv/NDQb+jp5UJd/c91+t33eckN7ydUrVFK7nqJRMLF8/d48ewDVatn3cPBIWGskiLL/4MMnSGDBw9GRUWFGzduoKOT+EVlY2ND797JWzwZ8flzKHFx8ZiZGcqUm5sZEhgYnOb2/v5fOHXyBp5bJ8mUO9SvyIpl+6heozQFC+Xhn3N3OXrYS+lfpoowtzBiyerBlClfiOioGPbsuEDrRtM4eHr2b/+QfPkcJv/4mhtyNvCr3G0c6tviufEkzZpXpVyFwty9/ZzNnqeJiYnl8+dQcuc2lql/68ZTHj96javbKKXGLpFImDt5KxWrFKNYyXwKb7dh5TF+RETRuFViq+Hd6yC8Lj2mRbtquO8Zx+uXAUwf40lcXLy0dasMgYGfMTMzSlZuZmZEYOAXudsEBCSUm5vLHlczc2PevvEH4NWrhOvzs2a4Mn/BaMqWK87WLYdp4NgXn/uHKFFM8VZ9ZqxaeoCIiEhatZXf/X/75lMeP3rDynXDlf7eAQGfUjy2AQGfFdpHZGQUkycuplPnpujpyf+B8OVLMHNmu9Kvf4dMxasom0K9+PIplNjYeMZP6Uj33vVl1oeFRmBTsBdRUTGoqamycPlA6jiU/yOxpUR0CydKd3L9+vUrJ0+eZM6cOTKJ9ScDAwO520VFRSV7Xp+WllaaT0LI6JMOtm46jYFBLpq3kL235KKlgxk8YCllbfqgogIFC+Whe8/6bPY8neY+la1wUUsKF7WUvq5kX5wP7z+zZtnBP/YLNOmhTO34TpjcmcCAYGpXH4lEIsHM3JCu3R1ZumgPampqyepv8jhJSZsCVLQrptSYp4/x5Mmjt+w66azwNof3XmPFvP2s2+6Eiam+tDw+XoKxqR5zVvRFTU2V0uULEhQQzPoVxxRKrjNnrGLWzDWp1vG6vhOQ/6WScLxTf4/kn4HEsvj4hKs6/fq3o2evVgCUL1+C8+ev4+mxn/nz5D9gWpn27rrIvNnb2b53KqZmBnLrbPE4Q0mb/NhWUvxcmDF9BbNmrky1jveNPUBKx1axL/KYmBg6d3IiPl7CqtXyz6mwsHCaNR1IiZKFmOasnN65tBw/60JERCQ3rz9l5tTNFCyUmzYdEi9R5dLNwcUby4gIj+TiP/eYMn4jBawtqF6r9B+JTx5FpuL8n+TW9CfXFy9eIJFIKF68eNqVf+Hi4sKMGTNkypydnVO8o4aJiT5qaqoEJmlFBX0KwSyFD/BPEomETZ4n6dTFAU1NDZl1pqYG7Nk/g8jIaL58CSNPHmOmTNxAAWuLdP09v4utXTH27rj429/H2ETv3+Mr2wsQFBSSrDX7U44cWqzd4MRK1+EEBQZjkduIjetPoKubExMT2a60798j2bv7IlOcuys17uljPTl74jY7j08jt6Vx2hsAR/d5MXGoG6s2jaBaHdkvHjMLA9TV1VBTS7xCUqiYJZ8CQ4iOjkVTM/WPyOAhXWjfIfVrWQUKWPLg/jO5LdRPn4IxM5P/d1hYJJQHBHwmd27TxG2CvmD2b2s2d24TAEqUkL2uWKJ4Qd6+9U81LmXYv+cSwwaswHP7BGrXKye3zvfvkezfc4mJ07qka99DhnalQ8fUfxykfmy/Jmv1JxUTE0PHDqN47feeM+c85bZav30Lp3GjvuTKlZN9+1ehoaEhZ0/Kl//f76SSpQrwKSiE+bN3yiRXVVVVChZKuDZcumxBnj15z9KFe7M0uaqqJCypEslVvp/jn9LbtJ84caL0fpA/pdZq1dTUoHyFopw/e4cWLRNHF54/e4emzVJ/0sHli/d5+eIjPXs3TLGOtrYmlpYmxMTEcvDAFdq0rZli3T/pgc8rzC3kJzdlSji+RTh/9i7NW1aTlv9z7i5NmqU+2EJDQx3LvAlf9nt3X6RhE7tkI1P37blEVFQMHbvUlbeLdJNIJMwY68npo7fYdmwK+QokH80sz+G915gwZB3L3IdSp0HyLjPbykU5vPca8fHx0r/B74U/ZhYGaSZWABMTw2Q/LOSxr1KW0NBv3LjxADu7hC+/69fvExr6jSpVy8ndxto6LxYWJpw760X58gndu9HRMVy6dJu5LiOBhOSSJ48Zz569ltn22fM3NGyYuRHaadm76yJD+y9nw+axNGhcKcV6B/ZeISoqhg6d66RYRx4TEyNMTPTTrGdfpdy/x/Y+dnYJ17SvX7/377FNuZv0Z2J98fwNZ89vwtg4+ecuLCycRg37oKWlycFDa9DWztwzRzNKIpEQFZV8UGLSOtFp1PndRLdwonQn1yJFiqCiooKvry8tW7ZUeDtFuoCTGj6qDX16zKeCbVEq25fAff1x3r0Nou+ApgBMneTOx4+fcfccL7Odp8cJKtkVx6ZU8hGYN6778vHjZ8qWLcyHD5+ZM3Mz8fHxOI3N/HWU8PAf+L1MbC28fR3Eg3uvMDTUJa+VKbOmbiHg4xdWu48EYO3Kw1jlN6NYSStiomPZs+MiRw964bEj8e+Jjo7hqe+7f/8/loCPX3lw7xU6uXJQsFBuMmPYyNb07bmQ8rZFqGxfgo0bTiQc3/5NAJg2eSMfP3xhg+dYAJ4/e8+tm0+pZFeckOBwVi7fz+NHr3HbODrZvjd7nKJZi6oYGytnNKDzaA8O773Guu2jyZUrB58CQwDQ1cuJdo6EQVYLp+8kwP8ri9cNBhIS69gBrkyd153ylYpIt9HW1kRXP2HqU+c+jmx2O83M8ZvpMaABr18G4Lr4ED0GpPzDLCNKlChIgwbVGDhgOmtcpwEJU3GaNKklM1K4VMlmzJ4zkpat6qGiosLwEV2Z57KBwoXzU7iIFfNd1pMzpzadOif8G6moqOA0piczp6+hTJlilC1XnC2bD/H0iR+7di9TOL7w8B+8+uXcffM6kPv3XmFomIt8VmbMmOLJx49fWPfvv/XeXRcZ2HsJ8xb3p1Ll4gQGJPSAaOfQRF9f9nLRVs/TNGluj5GSzoWkSpQoRIOGNRjYfypr1ib0jg0aMI0mTWvLjBS2KdGIOXOdaNnKkdjYWNq3G8HdO485dGQtcXFxBAR8AsDISB9NTU2+fQunYYM+/Pj+g81bFhIWFk5YWDgApqamqCn47Zn0e+HN60CZ74WZUzbh//ErrhsTxiZsWHuMvPlMKVIsL5Aw73XVsoP0H9RUuo+lC/ZQrkJhrAvmJjomljMnb7Fr2z8sWjEo4wdSCVRQoFv4j0SS9dKdXI2MjGjQoAGrV69m+PDhya67hoSEpHjdNb3ata/N1y9hzJ29lQD/r9iUKsDBI3Oko38DAr7w7m2QzDahoREc3H+FRUsHy91nVGQ0M6Z54vfKn1y5ctCgkR3um8ZjoMBUmLTcu/OClg0Sb5Yw9d95qR261mHV+hEEBnzl/btP0vUx0bE4T/Qk4ONXtHNoUqxEPrYfmIJjw4rSOgH+X6lrn9jiX73sIKuXHaRqDRsOnZ6TqXjbtq/F1y9hzJuzjQD/YEra5Gf/kVlY/Ty+/l95/y7x+MbFxbNi6X6eP3uPhoYaNWuX5dylJeQvINul/vzZe65dfcThE2nPl1TUNveEm1V0bjJLpnz+mgG07ZLQVRYUGIL/+8TuwZ0e54iNjcN5jAfOYzyk5a0712Sh60AA8uQ1xvPABOZM3ErjqhOwyG1Iz4ENGTAq5elOGbV563xGjnChccMBADRtVpsVK2UH3D19+lrmJgVjxvbmx48ohg2dTXBwGHaVS3P85DrpHFeAESO6ERUZxZjRC/j6NYwyZYty4pQbhQpZKRzb3dvPaVY/MZbJ4zYA0KlbPVw3jCIgIFjm3PXYcILY2DjGjHBlzAhXafnP+j+9ePYBr6uPOXBM9t9N2bZsXcjIEXNo1CBhylKz5nVZsVL2xiVPn/pJj+379wEcOXweANvyLWXqnT2/idq1K3P79iNuXE8YlV2siOxgohev/qGQtWLXj31uv6B5g8nS11PGuQPQqWtdVm8YSWCSYxsfL2Hm1M28fR2Imroa1gUtcJ7dXTrHFeB7RBRjR6zl44cvaOfQpEixvKz1cKJ1u9/bW5EW0XJNlKF5rn5+flStWhUjIyNmzpxJmTJliI2N5cyZM7i6uuLrK38qR0pSmueaPWRsnmtWSc8816yX8XmuWSEz81yzRsbmuWaFzMxzzRpqqKnI3nRGkXmuWeVPzXO1mlQTVe005rlGxvJ27qX//DzXDE3Fsba25s6dO8yZM4fRo0fj7++Pqakptra2uLq6pr0DQRAE4T9HVUUFVTFcGMjE7Q9z587NqlWrWLUq5Vu3CYIgCP8/xFScRNn63sKCIAjC30NFVQWVNObipLX+v0IkV0EQBEE5xMPSpURyFQRBEJRCjBZOJJKrIAiCoBTimmsikVwFQRAEpRAt10QiuQqCIAhKIQY0JRLJVRAEQVCKhG7htFqufyiYLCaSqyAIgqAUols4kUiugiAIglKI5JpIJFdBEARBKVRVVVBVSz15Sv5Prrmqpl1FEARBENL2s+Wa1pIesbGxTJkyBWtra3LkyEHBggWZOXMm8fHxv+mvUI5s0nLVyOoAUpE8NhU0syAOxciPLbse3+Rxqapk32MrPza1Px6H4pLHppJNzwX5cWXn3/7JY8uuxxb+YGy/YaLr/PnzWbt2LZs2bcLGxoZbt27Rq1cv9PX1GTFiRCaC/b2yRXLVVqua1SGki5F2vawOIV1yqmftMx7TwypX07QrZSPqqkWzOoR00dWsndUhKExNpWDalbIRfa26aVf6j/sd11y9vLxo0aIFTZo0AaBAgQLs2LGDW7duZTjOPyE7/zQUBEEQ/iIqqootkPAM2F+XqCj5z3GuXr06586d49mzZwDcu3ePK1eu0Lhx4z/1Z2VItmi5CoIgCH+/9LRc8+XLJ1Pu7OzM9OnTk9UfP348oaGhFC9eHDU1NeLi4pgzZw6dOnVSWty/g0iugiAIglKkJ7m+e/cOPT09abmWlpbc+rt27WLr1q1s374dGxsbfHx8GDlyJHny5KFHjx7KC17JskVy9Q7cTmy8/C6BrKauqoW9eWeZshehB4mTZM941VS0KKzfUqbsbfhR4iXRWRNQKlRVNJNdYy3epwlfwkKyJqA0GOsZ8MT9mExZdNxdIDZrAkqTOppq5WVK9r10IyruRxbFkzIttRy0KdRfpux9+DHiyX7nLYAqmuTN1USm7MQbD6LjI7MootRpqmrTKH+v3/4+6Umuenp6Msk1JWPHjmXChAl07NgRgNKlS/PmzRtcXFxEck1LbHwUMdn0pJQnThKVbZOrPPGSaOL/kni/hIXwOTQ4q8NIh1iyb3JNLiruB5HZMLnKE082Pm/l5I/o+Eii4/+OY/u7/I57C3///h1VVdnhQWpqamIqjiAIgvD/4XeMFm7WrBlz5szBysoKGxsb7t69y5IlS+jdu3dmQv3tRHIVBEEQlEOBaa7yWv2pWblyJVOnTmXw4MEEBQWRJ08eBgwYwLRp0zIc5p8gkqsgCIKgFL+j5aqrq8uyZctYtmxZJiJLv+DgYJ4/f46KigqFCxfG0NAwXduLea6CIAiCUqiqqSq0ZGevX7+mcePGmJiYYG9vT+XKlTExMaFx48a8efNG4f2IlqsgCIKgFH/7U3ECAwOpVq0aampqzJ07lxIlSiCRSHj69CmrVq2iatWq3LlzB3Nz8zT3JZKrIAiCoBQqKHBr4T8SScbMmTMHIyMjbty4QY4cOWTWDR8+nEqVKjF79mxWrlyZ5r6yd/tcEARB+Gv8jqfi/ElHjx5lxowZyRIrgLa2NjNnzuT48eMK7Uu0XAVBEASl+B3zXP8kf39/ypQpk+L60qVL8+HDB4X2JZKrIAiCoBR/+zVXU1NTYmNTvilMTEyMQtdbQXQLC4IgCMry86JrqktWB5kyW1tbTp8+neL6kydPUrZsWYX2JZKrIAiCoBRp5lVFbjKRhZycnFi3bh2hoaHJ1oWFhbF+/XpGjhyp0L5Et7AgCIKgFKoqKqimkT3TWp+VatSowaNHjzh79iyPHz9GRUWF4sWL4+joiJ6eHo8fP1Z4XyK5CoIgCErxt19zff/+PS1atODBgwdYWVkhkUh49+4dpUqV4tChQ8meQZuabN0tfGjzJfo6zKZpcSeaFndiaPOFXD//SKFtH958iUP+ofSrPzfFOucP3aJu3sFM7bM207GuX3qE9vWmU8lqADWKDmVY1+X4PfdPdZsbV3yxMeqRbHn17KNMvbDQCGaN3UytEsMpn7svzSpP4NKZe5mK13XxIVrWnkIZy95UKjSQAZ0X8+r5x1S3OXX4Bt1bzKVSwQGUzduHtg7TuHQ2eRxhIRE4j/bAvuhgSpj1oH6lMfxz+m664qtR2pbDM1fzYccFJKcf06JqPZn1rao5cHKuG5/2XEVy+jFlCxZPc5/qaupM7TKIF54n+XH0Lj6u+2lQsbpMHTVVNWb1HM6rzaf5fuQOLzedYmqXQRn6Qljruo+ihdugp1Mbe7teXLnsk2Ldq1fuUbvGAHKbNUQ/V21K23Rk+bKdyeqtWL6LUiU7op+rNoUKtGSM03IiI5X75JhjbhfoU3IiO1yOpFjnyY1X9Ck5Mdni/ypIWufinhvM67qOYfYzGGY/g0W9N/Dq/rtMx7dm8UFa1JpMqTy9qFhwAP07LeZlGuduUEAwI3qvpG4FJwrqd2bm+E3J6uzddhFrvU7JlqhI5T327oDrOdoXHo3n7IMp1gkOCmP5qK2McJxHhyJjUq0LcPXoXdoXHs2CgRuVFmdG/Wy5prVkV8OGDUNXVxc/Pz9evHjBy5cvef36Nbq6ugwdOjRd+1Jqy7Vnz56EhIRw8OBBpezPNLcBfSe2xNLaFIDTe7yZ2mct605OxLpYnhS3Cw/7gcvITVSoXozgT9/k1gl4/4W1s/ZTunJhpcR68+pTOvWpR+ny1sTGxbNi9l76tVnIYS8XcurIfwjwT8duzEdHV1v62sgk8RmH0dGx9G29EGMTPZZ6DMXC0gj/D1/RyaUtb1cKu37Vl679HClToRBxsXEsnrWbHq3mcer6AnLqyN/3jWtPqFanNGOmdUDXICf7tl6kf8dF7Ds3C5uyBaTxdm/pgrGpHqs2jyB3HiP8P3xBJ1fyeWOp0dHOyb1XT/E4dYD9zivkrM/B1Ud32XPpFBucZim0z9k9h9O1XjP6LXXmybtXNKhYjQPOK6g6sgs+L30BGN+hLwObdKDHwok8evOCikVL4TF6DqER39j+z7E03iHRnt1nGeO0nBWrxlClahk2rD9I86aj8XmwDSsri+R/j442g4a0oXTpwuTUycG1q/cYMmgBOjra9O3XEoAd208xZZIrbhsmYV+lNM+fvaVfnzkALFoyQuHYUuP34B2X9twgb7HkMcoz57gTOX45X3SNdKT///TGK+yalKFwuWZoaKlzwv0SS/ptZNbhkRia62c4xutXfOnWvz5lKhQkNjaexTN30b2lC2duLEzx3I2OisXIRI8hY1qycU3K8xR19XJw7vYSmTItbc0Mx/qrF/ffcnaXN/mL5061Xkx0LHpGuWg92IFjGy+mWvfTh69scTlCiUoFlRJjZqmpqqKumnqbTZLG+qx07tw5Ll68iKWlpbQsT548LF++nBo1aqRrX9m6W7iqo+x8oz7jW3B482V87/ilmlyXTthOvZaVUFVV4eqp+8nWx8XFM3eYJz1HN+HBjZeEh33PdKxue8fIvJ69qi81ig7j8T0/KlZNvVVlZKqLnr6O3HUHtl0iLDicbSenoKGR8M+VJ59JpuP13D9B5vX8NQOwKzSQhz5+2FUrIXebqfO6y7we49yRs8dvc/7kHWly3bvlAqHB4ew5M10ar6WVabrjO3nzMidvXk5x/dZzCa2q/OYpnwdJdXNozpzt6zhx8xIAa4/uokHF6oxu25Nu88cDUKVEWQ55nef4jYQ6bwI/0ql2YyoWLZWu5Lp86U569m5G7z7NAVi8ZCRnTl/Hbe0BZs8dlKx+ufLFKFe+mPR1gQK5OXjgIlev3JMmV2/vh1SpWpqOnepL67Tv4MCtm74Kx5WayIgo1o/bRY8ZrTm67rxC2+gZ5SKnnvwfTv0XdpR53XNma26ffoiv90uqtqiQ4Tg3HZgo83qB60AqFhzAAx8/Kqdw7ubNb4rzgoQHa+/ZeiHlnauoYGpukOHYUhIZEcVKp20MmNOO/avPplrXLK8Rvaa2BOCfPTdSrBcfF88Kp220H9EA31uviAjL+mfJ/u3dwqqqqsTExCQrj4mJSfZM2TT3paygfre4uHjOH7pF5I9oStqm/CvtxC4vPr75RI9RjVOss2XpcfSNctG4U7XfESoA3/490fUNcqVZt22tadQqMZzeLedz/bLsF+U/J+5StlJhZo/dTM1iw2hRdRJuS44QF6fcBwV/C034gaFvmHa8P8XHxxMeHom+YeIPg7MnblPergjOoz2wKzyQhvbjWLPooNLjzQgtDU0iY2S7UH9ERVLdJvGL/sqjO9QrZ08Ry/wAlClYjOqlKkiTrSKio2O4c+cpjo52MuUOjnZ4ez1QaB8+d5/i7fWAGjXLS8uqVSvD3TtPuXkjYVDFq1cfOHnSi0aNqygcW2q2zT5EmVrFKVlV8d6cGW1W4lRzLgt7beDJ9Zep1o2KjCEuNg4d/fT1YqTl57lrkI5zNyXfwyOpZjOMKsWH0KfdAh7d88v0PgE2TN9P+dolKVOtqFL2B7B35Wn0jHJRt31lpe0zs1RRUWjJrho1asSQIUNkBi75+voyaNAgGjZsmK59/bGWa1RUFFFRsl9sWlpaaGml3mX6yvcDQ1ssIjoqhhw6WsxY358CReV3q7x/FcQGl4Ms2++Emrqa3DoPb77k+M5rrD89KWN/iAIkEgkLpmyngn1RipTMm2I9U3MDpi/thU25AkRHxXJ491X6tJyP55EJ0tbu+zefuH7Zl6Ztq+C6y4k3LwOZPW4zsbFxDB7XUmnxzp28lYpVilGspOIX7DesPMaPiCgat7KXlr17HYTXpce0aFcN9z3jeP0ygOljPImLi2fY+NZKiTejTt26glPrnly6f5uX/m+pV96eFlXqoqaaeK7M37UBfR1dnrgfIy4+DjVVNSZ7LmfnheOY6Cv2yKnPn0OIi4vDzMxIptzczIiAwK+pblswfws+fQohNjaOqdP6SFu+AO07OPLpUwh1ag1EIpEQGxtH/4GtGDu+eyp7VMz14/d48/gjU3cPUai+gaku3We0ooCNJTHRsXgdvsui3u6M3dSPYhWt5W6zb8lJDM30KFlFOZdiIOHcnT1pS7rPXXkKFcnDQteBFLOxIvzbDzxcT9C2/nSOX52HdeHUu3JTc/XoXfwevcflwMhMxferJ7f9OL/nBguOOCltn8qgqpKwpFUnu1qxYgUdO3akVKlS6Ovro6KiQkhICLVr11bofsK/+mPJ1cXFhRkzZsiUOTs7M3369FS3y1fInPWnJhIe9oNLx+8yf9Rmlu4dlSzBxsXFM2fYRnqMbkK+gvLvoPE9PJK5wz0ZvaAL+kaZ/5WbktnjtvDs0Xu2HJ+caj3rIrmxLpL4d5SzK0zAh694rDohTa7x8fEYmegyfVkv1NRUsSlnTVBACB6rjistuU4f48mTR2/ZddJZ4W0O773Ginn7WbfdCRPTxOtn8fESjE31mLOiL2pqqpQuX5CggGDWrziW5cl1hKsL60fN5In7USRIePnxHR6nD9CrfitpnQ61G9G1XlM6zxvLo9cvKFeoOMsGTeTjl6B0tV4h+Xw+iUSS5hy/cxdciQj/wfXrD5kyyZVChS3p0DGhG/jihTvMd9nEilVjsLOz4eXL94wetYy5Fh5MmtIrXbH96qt/CDtdjuK0vjcaWhoKbWNhbYqFdWJ3f+Fy+QkOCOWUx2W5yfWE+0WuH7vHuE39FH4PRUwb7cGTR2/Zc2p6pvdV3q4I5e2KSF9XtC9K0xqT2LTuFNMX9szQPj9/DMZz1kEmew5AU0l/94/wSFaO3s6Aue3Q+43fYxnxt3cLm5qacu7cOe7cuYOvry8SiYQSJUpga2ub7n39seQ6ceJEnJxkf2Wl1WoF0NBUx9LaDIBiZfPz9N4b9rv/g9P8zjL1foRH8vTeW54/fM+KKbsBkMRLkEgkOOQfyoLtw9AzyEnAuy9M7uUq3U4SLwHAIf9QNl10xrJA+q8P/mrO+C1cOHGXTccmYWFplPYGSZStWIiju69JX5uaG6CuoYbaL89ALFQ0N58DQ4mOjkVTM3P/hNPHenL2xG12Hp9GbktjhbY5us+LiUPdWLVpBNXqlJZZZ2ZhgLp6kniLWfIpMEQp8WbG59BgWk0fhpaGJsZ6Bnz8EsS8Pk74BSTeK3RhvzHM27mBXRdOAPDw9XPym+dhYsd+CidXExMD1NTUCEzSSg36FIy5WernhLV1wjXkUqULERT4lVkzN0qT6wzn9XTu0lDami1VuhARET8YPHA+Eyb1SPc1oZ9eP/pA2JdwZrZbJS2Lj4vn2a3XnN/uzTqfWQo9g7NgGSu8jyYfFX5y4yWOuV1gjHsf8hXLeAswKecxHpw7cZtdJ5wVPnfTQ1VVlTIVCvL6ZUCG9/Hq0XtCv4QzoeVSaVl8XDy+N19xcstVtj+en+7nmwa+/cKn91+Z3z9xdPDP77GOxcay7PR4LPJnflxGRvzt81x/qlChAhUqyI4LePbsGUWLKt6t/8e+6RTpAlaERJIwmi6pnLrauJ+dIlN2aPNF7l59xvR1/bCwMkZNVTVZnY0LD/M9PIqhM9phlid9T5qXjUvCnPFbOHfsNp6HJ5I3f8aStO/9N5hYGEhfl69chGN7vYmPj5d+eb5+GYiphUGmEpVEImHGWE9OH73FtmNTyFfATKHtDu+9xoQh61jmPpQ6DconW29buSiH916TidfvhT9mmYxXmaJiovn4JQh1NXXaVK/P7ksnpetyauUgXiJ7fTguPh5VFcW/ADU1NahQoRhnz96gRcta0vJzZ2/SrJniIw4lEoiOSpwG8v1HJKpJ+tTU1FSRSBJ+RGZUiSqFmXFIdrSxx+S9WFib0qhvLYW//N8++Yi+qa5M2Un3Sxxdd55R63tToFTKl0jSQyKR4DzGk9NHb7Lj2FSFz92MvI/v/TcUs8l4d3PpKkVYdFx2sKPr+F3kKWhGiwF1MvTg8DyFzJLtc+eSE0RGRNFzaktMchtkON7M+q8k158CAgLYuXMn27dv59atW8THKz52JHt826Vgw7xD2NWxwSyPId/DI/nn8C3ueT1j3taE+UbrXQ7yOSCEict7oqqqinVx2ZGjBsa6aGppyJQnrZNLL6fc8vSaNXYzx/d6s3LbCHLm0uZTYAgAuno50c6RMJR/6czdBPkH4+I6AIDNrqewtDKhcPGE61ZH9lzjzJFbLNs0TLrfDr3qsm39WVwmbqNLP0fevApg/dIjdOnvmKl4nUd7cHjvNdZtH02uXDnkxrtw+k4C/L+yeN1gICGxjh3gytR53SlfqYh0G21tTXT1E45j5z6ObHY7zczxm+kxoAGvXwbguvgQPQakbzCAjnZOCuexkr62trCkbMHifP0WyrtP/hjq6mNlmps8xv/2auQrAEBA8GcCgz8DsGmsCx++BDFpY0Krwa54GSyNzfB5+QRLE3OmdxuCqqoKC3a7S9/niPc/TO40gLdB/jx684LyhUvg1LoHG0/tT1f8I0Z1pFePmdjalqCyfSnc1x/i3dtA+g1oCcCUSa58/PiJjZ7TAHBds498+cwpVjxhINW1q/dYumQ7g4e0le6zSZNqLF+2k3Lli1LJzoaXL94z3Xk9TZvVQE1N/hgDReTQ0SJvEdmpN1o5NMllkFNavm/JSYKDwug7rz0AZzZfwTiPIZaFzYmNicP7yF1un37I4OVdpPs44X6RgyvO0G9hR0zyGBL677Q4rZyaaKcxPS0105w2cmjvNdx2jCaXrvxzd8H0HQR8DGaJ22Dpdo/vvwbge0QkXz9/4/H912hoqlOkeELSX+6yl3KVimBdyILwbz/wXHuSxw/eMGNxxrvcc+TSxirJJSytHJroGuaUlm9feIyvgaEMXZTYG/f6cUJvSuT3KMK+hvP68QfUNdTIW8QCTS2NZPvU+XfEdtLyP+2/kFy/ffvG/v372bZtG//88w8FChSgc+fObN26NV37ydbJNfhTGC4jPPkaFIaOrjYFS1gyb+tQKtZMGG7/NSiMoA/BWRxlgl0bE6Yu9GzmIlM+e1VfWnVOaK18CgzF/31iV2FMTCwLp+0kyD8YLW1NChe3xHWXEzUdE28MnTuvMev3jmX+5O20qjEF89wGdB1Qnz4jmmQq3m3uCdMBOjeRnSM6f80A2nZJaG0FBYbg//6LdN1Oj3PExsbhPMYD5zEe0vLWnWuy0HUgAHnyGuN5YAJzJm6lcdUJWOQ2pOfAhgwY1Zz0qFjUhguLEif6Lx2YMHXI8/QBei2aTHP7OniOTbxByK7JCXMTp29ZzYwtqwGwMsst0wrV1tBkds8RFMydl/Af3zl+4xLd5o8nNCJxLvSw1XOY1WM4a4ZNw8zAiI9fglh3fDczt7qir6P49a127R348iWUubM34u//BZtSBTl0ZBH58yd8+QUEfOHd20Bp/fj4eKZOceW1nz/q6moULGTJ7LmD6Ne/pbTOxMk9UVFRwXmaGx8/fMLU1JDGTasxc9YAhePKqJDP3/jqHyJ9HRsTx56FxwkOCkNDSwPLwuaMcO1BmVqJ087+2eFNbEwcriO3yeyr+eB6tBjqkOFYtv577nZqLHvuLnQdmHjuBoTw8f1nmfVNqidO4Xlw149De65iaWXClYcJA1XCQr8zacQGPgeGoKuXk5JlCrDrxDTKVVTeACx5gj+F8fljiEzZuOaJc21fPXzPlcN3MbU0ZPXFKWRnKv/+l1ad7KpDhw4cOXIEfX19OnTowOzZs7Gzs0t7QzlUJJnpT0oiozeRuOLvQUx8pLLCUCoNVW2q55b95fo0ZBdxEuXeFUdZ1FS0KGbQQabs9bf9xGfDeFVVtCigKzvIybRdNT6HZo8fTEmZ6Bvyac9VmbLouJtAyo+oylrqaKpVkinZ/mw5kXFZPx8yKW21HHQuKts1/Tb8QLY8byHh3LXK1Uqm7JCfK9Hx2e/YAmiq5qCFdfL51coSFhaGvr4+bfb2QSNn6jfdiPkezb627oSGhqKnp5dq3T9NTU2NMmXK4Orqir29fdobpEKp81w9PT2VdncmQRAE4e+iigK3P8zGLVcPDw9MTU2pUaMGxYoVw9nZmWfPnmVoX3/NTSQEQRCE7E1VVQW1NJakg/Kyk+7du3P69Gnevn3LoEGDOHr0KMWLF6dSpUosWbIk7R38QiRXQRAEQSn+9hv3/5Q7d25GjhzJ7du3efz4MQ0bNkz3TSREchUEQRCU4r+SXH9VvHhxZs2ahZ9f+m6Fma1HCwuCIAh/DxXSHg2cnVNrdHQ0a9eu5cWLF1SvXp327ROmnsXGxqKqqpquG7WIlqsgCIKgFL+r5frhwwe6du2KsbExOXPmpFy5cty+fVvp8Q8aNAhnZ2d8fX3p2bMna9cmPOt7zpw59O/fP137EslVEARBUIrfkVyDg4OpVq0aGhoanDhxgsePH7N48WIMDAyUHv+BAwfYtWsXZ86cYenSpbi7J9xgplmzZpw/r9hjGH8S3cKCIAiCUvyOOzTNnz+ffPny4eGReOOaAgUKZCS8NKmoqGBtnfDgCXt7e6ZOnQqAsbExAQHpu8e0aLkKgiAISvHzqThpLZBw44lfl6SPJP3p8OHDVKxYkXbt2mFmZkb58uVZv379b4m/U6dObNmyBQBdXV1+/Ei4Kci1a9ewsrJKbdNkRHIVBEEQlOLn81zTWgDy5cuHvr6+dHFxcZG7z1evXuHq6kqRIkU4deoUAwcOZPjw4WzevFnp8evr67NixQocHR1ZsGAB0dHRDB06lP79+zN48OC0d/AL0S0sCIIgKIUqad+B6ef6d+/eydz+MKWnpsXHx1OxYkXmzk24l3j58uV59OgRrq6udO/eXUmRJzhx4gQFCxbky5cvfPnyhfLlyxMUFISHhwdt27ZNewe/EMlVEARBUAoVBa65/uwW1tPTU+jewrlz56ZkyZIyZSVKlGDfvn0ZDzQFd+7cUdq+RHIVBEEQlEJNVRW1NOaCprU+qWrVqvH06VOZsmfPnpE/f/50x/cnZYvkqq6a+Yeo/y7yYlNTyb7xyotNVSX1p1RkFXlxGesZ/PlAFCQ/tmzxEUpB8ti01HJkQRxpkxeXKprZ9o4DqiQ/dzVVtbMgEsX8qdh+x2jhUaNGUbVqVebOnUv79u25ceMGbm5uuLm5ZSZUuXr1Sv3Zvb+OWE6LUh85JwiCIPz/+fnIueGnR6Glk3rjIyoiihX1l6brkXNHjx5l4sSJPH/+HGtra5ycnOjXr58yQpfRurXsIzAjIiJ48OAB4eHh1KtXjwMHDii8r+z8s1sQBEH4i/yOlitA06ZNadq0aUbDUtj+/fuTlcXGxtK3b1+KFy+ern2JqTiCIAiCUqgquPxN1NXVGT9+fLqfiiNaroIgCIJS/HqTiNTq/G0kEglaWlrExMSgoaGh0DbZIrnGxvsCcVkdRgrUUFctIVMSJ3lFdo5XTaWgTEls/DOyZ7xqqKsWlSmJjrsLxGZNOGlSR1OtvEzJ33buZt94k8caE/+Q7BkrgBoaqqVkSmLjn5Cd41VXTV+3Zkb8rm7hrFayZElevXqVrm2yRXJNOCGz60kpj4j394kl+yZXef6mYwt/V7xxiHPh76JK4h2YUquTXRUsWJDUxvj6+fnx5csXKlasmObzXbNJchUEQRD+dir//pdWnexq5MiRadbR0dFh1KhRadYTyVUQBEFQivTcoSk7Gj58eJp1tLW1FaonkqsgCIKgFL/emD+1OtnZvXv3ePnyJRUrVkz3k3B+lZ27vwVBEIS/iJqKqkJLdrV8+XIqVKhAp06dKF68OP/88w8AK1asYOnSpenaV/b9KwVBEIS/SnoeOZcdLVy4kKVLlxIVFcXgwYOlj8ErW7Zsum59CCK5CoIgCEqioqLIA9OzOsqUhYSE0KxZMwDat2/P48ePAShQoEC6p+KI5CoIgiAoxc/nuaa1ZFc1a9bkypUrABgZGREWFgYkTMExMjJK177EgCZBEARBKf72AU1dunRhwoQJvHnzBktLS2JjY9m7dy/Tpk2TtmgVJZKrIAiCoBR/++0Pu3fvDoCzs7O0bPDgwbRv35758+ena18iuQqCIAhKoUi3b3buFg4ODpZ5raGhQY4cGXsGskiugiAIglKoKNAtnI0brgo/X1YRIrkKgiAISvG3dwtv2rQp1fU9evRQeF/ZfrSwq+suihRqTK6cdthV6sSVy3dSrX/p4i3sKnUiV047ihZuwrq1e2TW16vbBw21csmW5k2H/pb4g4ND6dF9HEYGlTAyqESP7uMICQlLdZsD+0/TqGFfzE2roK5aAh8fX5n1r19/QF21hNxl754TSoh3IsaGVTA2rEKP7hPTjFcikTBzxhqs8tZFV6ci9er24tGjF8nqeXn54OjQB31dO0yMqlKvbg9+/PihcGxrXfdRtHAb9HRqY2/XiyuXfVKse/XKPWrXGEBus4bo56pNaZuOLF+2M1m9Fct3UapkR/Rz1aZQgZaMcVpOZGSUwjGlRtnn7q927TyJhlo52rQaqZRY/8Z417ruoWih5ujmrErlSl25cvluinUP7D9Po/qDyWPugLFBLWpU68XpU14yddzXH6BOrb6YGdfBzLgODesP5uaNh0qL91fBwWH06D4JY8NqGBtWo0f3SQp+zlyxyuuAro4d9er2SfY5i4qKZsRwFyzMaqGvW5lWLYbz/n3gb/kb5Pn5VJy0luxq1KhRMsvQoUPp3bs3/fv3V+i+w7/K1sl1965TjB61kAkT+3Lz9k6qVy9P0yZDePvWX259P78PNGs6lOrVy3Pz9k7GT+jDqJHz2b/vrLTOnr1LePfhrHTxub8XNTU12rR1/C1/Q9cuY7nn84RjJ9w4dsKNez5P6NF9fKrbRET8oGrV8sx1cZK7Pl8+C95/vCSzOE8fio5OTho2qpmpeLt1Gc+9e084etyVo8dduXfvCT27T0p1m0ULN7Js6WaWr5iE1/UdWJib0KhBf759i5DW8fLyoWnjQTg6VuGa93a8ru9g8ODOqKoqdgru2X2WMU7LmTCxB9dveVKtelmaNx3N27cBcuvr6GgzaEgbzv2zhnsPdzBxUk+mT3Njw/qD0jo7tp9iyiRXpkztzb2HO1jrNpG9e84xZdJahWJKze84d3968+Yj48ctoXqNCpmO8++N9zSjRy1mwsTe3Li9jerVy9OsyfAUz4crl+9Sz7Eyh48ux/vmFmrVrkirFqO4e/eJtM7Fi7fp0LEBp8+t5dJVD/LlM6dxw6F8+BCktLh/6tZlAvfuPeXo8TUcPb6Ge/ee0rP75FS3WbTQg2VLt7B8xQS8rm/DwtyYRg0GynzOnEYt4NDB82zbPp8LlzwJj/hOi+bDiIv7M0/r+dsflv7161eZ5du3b7x8+ZLatWuza9eudO1LRZLa83XS0LNnT0JCQjh48GBGdwFAbArPbaxapSvly5dg9ZrEk660TSuat6jDnLnJb5w8ccIyjh65yINHB6RlgwfN5v79Z1y5ulnuey9fvpUZzq68+3AWHR15F67VUE/y3MY4yXO58Sbl6/uS0jZNueq1k8qVywLg7e1D9aqdeOR7nGLFrFPd/vXrDxQu6MCtO/spV65EqnUrVmhN+fIlWO8+DzWVIjLrFH2Gp6/vK8qUasGVa9uoXLnMv/Heo0a1rjx8fFhuvBKJBKu8dRk+oitjx/UBEn49W+auzVyXkfQf0B6AalW74OBgz4yZw37ZOvkzPKPjbiLvMWPVq/SlXIVirFo9VlpWplQnmjevyey5g9L82wDat52Ijo42HpsSRgKOGL6YJ76vOXVmpbTOuDEruHXTl/MXXeXsQR1NtUoyJX/63I2Li6NunT706NGCK1fuEBryjX0HlqXwFyc/d7NvvMljjYm/R0qPnKtWpQflyxdn1ZqJv8TbluYtajNnrmK9UGVLt6dde0emTO0nd31cXBxmxnVZtmIs3bo3TbJWHQ3VsjIlsfGPUPxz1oor17b88jm7T41q3Xj4+BDFihVItk3C58yB4SO6MHZcb+Dn56wuc11G0H9AO0JDv5HbvDaem+bQvkNDAD5+DMI6fwOOHHWlcaP+acaWUWFhYejr67PUexo5cmmnWvdHeCSj7GcSGhqq1Gucv9Pdu3fp0qWL9KYSisi2PyKio2O4c9sXR8cqMuUOjvZ4ed2Tu423930cHO1lyurXr8rtW4+JiYmRu43HxoO079AghcSaOd5ePujr60oTK4C9fTn09XXxupZyF1Z63b79CB8fX3r1aZup/Xh73fs33jLSMnv7sv/G6yN3Gz+/9wQEfMbBsaq0TEtLk5o1baX/TkFBX7hx/T6mZkbUqN4Vy9y1qFunJ1eu3FYorujoGO7ceYqjo51MuYOjHd5eDxTah8/dp3h7PaBGzcSHnVerVoa7d55y80bCB+bVqw+cPOlFo8ZVUtqN4vH+pnN39qx1mJoY0rtPq0zF+PfH+yTZ+zs62uPtdV+hfcTHxxP+LQIjo5S/3L9/jyQmJhYjI/1MxZuU/M9ZmTQ+Zx/+/Zwl/hsl/Zzduf2YmJhYHOsnfhbz5DHDplRhvLyU932TGnVVFYWWv42Kigrv3r1L1zbZdkDT58/BCb8czWXvimFubkxgwGe52wQGfMbcvKpMmZm5EbGxsXz+HELu3KYy627ceMCjhy9wW+/M7xAQ8Bkzs+R39TAzMyIghb8hIzzc91KiRCGqVi2fduVUBAamHG9g4Be52wQEJJSbmxvLbmNuzNs3CV2Kr169B2DWDFfmLxhN2XLF2brlMA0ce/PwYWWKFJFtaSf1+XNIwrmQJDZzMyMCAr+mum3B/C349CmE2Ng4pk7rQ+8+zaXr2ndw5NOnEOrUGohEIiE2No7+A1sxdnz3VPeZlt917l69ehePjQe5dSd93VP/vXgTzgfzJPGamSv+uVq6ZCsREZG0bZfy5aDJE1dhaWlKPQe7FOtkRGDgF8zMDJOVm5kZpvI5S/i75H/OPv5b5wuamhoYGsr+YDA3M5J+Tn+3v/15rocOHZJ5LZFI8Pf3Z9WqVVSvXj1d+/pjyTUqKoqoKNmBIlpaWmhpaaW6XdKRZRKJJNXRZvLqyyuHhFarTanC2NmVTjWGpGZMX8GsmatSreN9Y0+K75vW35AeP35EsmPHMSZPSblrdOaMVcyauSbV/XhdTxjsk3K8qceR/LgnlsXHJ/wb9Ovfjp69Elow5cuX4Pz5G2zcuFF6c+y0JI1BkbjOXXAlIvwH168/ZMokVwoVtqRDx/oAXLxwh/kum1ixagx2dja8fPme0aOWMdfCg0lTeikUU+rxKu/c/fYtgp7dJ7N23TRMTJJ/MSvDfz3en3buOMmsGW7sO7BY7o9JgEULN7Fr5ynOnF+Htnbq31E/zZyxmlkz5V1OSOR1fbvc2OHnZyb198jI36zIfpXlb79DU+vWrWVeq6ioYGZmRr169Vi0aFG69vXHkquLiwszZsyQKXN2dmb69Oly65uYGKKmpkZgkl9cQUFfMUvy6+0ncwuTZL9cPwUFo66ujrGxbNfO9+8/2L3rFM4zFLte96shQ7vSoWOjVOsUKGDJg/tP5f4S/fQpONkv0Izat/cU379H0q17ixTrDB7ShfYdGqS6n4R4n6UYr5mZ/HgtLBLKAwI+y/QMfAr6Iv13yp3bBIASJQrKbFuieEHevn2balwAJiYGCedCklZq0KdgzFP4cvzJ2joPAKVKFyIo8CuzZm6UJtcZzuvp3KWhtDVbqnQhIiJ+MHjgfCZM6qHwYKvk8Sr/3H306CWvX3+kZYsR0vXx8fEAaGva8sj3IIUK5fs/iTfhfEjaGvsUlPbnaveu0wzoN4sdu+ZTz6Gy3DpLFm9hvosHJ0+voUyZ1HtVfjV4SGcFPmd5/v2cJe9xSf1zlvAZSv45S/w3srAwJjo6huDgMJnWa9Cnr1SpWk7hvyMzFBkNnJ1HCytz4NcfS64TJ07EyUl29GtqrVZNTQ0q2Jbg7FkvWraqKy0/d/Y6zZrXlruNvX0Zjh29JFN25owXthVLoqGhIVO+Z/dpoqKi6dKlSTr/EjAxMcLEJO3rMPZVyhEa+o0bN+5jZ5dwfeX69XuEhn6jSia7cH/auHEfzZrXwdQ05SRjYmKIiUnaAwfsq5T9N94H0tb89ev3/423nNxtrK3zYmFhwrmzXpQvnzA4KTo6hkuXbjPXZSSQkLjz5DHj2bPXMts+e/6axo3SPg6amhpUqFCMs2dv0KJlLWn5ubM3adasRprb/ySRQHRUtPT19x+RqCb5Ga2mpopEIiET4/x+y7lbvLg1d+/tlVnvPHUV38K/s2TpOPLls/g/i7c4585ep2WrOtLys2ev06x5rRS327njJP37zmLLtjk0biK/i2/xos24zHHn2IlV2FYsma64fu/nzPLfz5m3nM9Zwg+YCrYl0dBQ5+wZL9q1T0jy/v6fePTwBfPmjU7X35JRf3u38K/Cw8P58eMHpqamaVeW448NaNLS0kJPT09mSatLeOTIbmx0P4DHxoP4+r5itNNC3r71p/+AhIE7kyetoGePKdL6/Qe0482bj4wZvQhf31d4bDyIx8YDODklv4bm4XGQFi3qYGxsoNS/81clShSiQcMaDOw/DW9vH7y9fRjYfxpNmtaWGXlrU6IxBw+ckb7++jUEHx9fHj9OmMP27KkfPj6+BAR8ktn/ixdvuHzpFr0zOZApMd6CNGhQjYEDpuPtfQ9v73sMHDCdJk1qycRbqmQzDh44ByR0mwwf0ZV5Lhs4eOAcDx8+p0+vyeTMqU2nzk2kdZzG9GTVyu3s23uaFy/e4jxtJU+f+NGnTx+FYhsxqiMe7kfw9DiKr+9rxjgt593bQPoNaAnAlEmu9O45U1rfdc0+jh65wvPn73j+/B2bPI+ydMl2OnVObFk0aVINt3UH2L3rDH5+Hzl75gbTndfTtFkN1NTUMnUslX3uamtrUapUYZlF30AX3Vw5KVWqMJqaGnLj+K/GO2JkFza6H8Rz4yF8ff0Y47SYd28D6D+gzb/xrqJXj2nS+jt3nKR3T2cWLBpJZftSBAR8JiDgM6Gh4dI6ixZuwnmqK24bppG/QG5pnfDw75mKNanEz9lMvL3v4+19n4EDZtKkSU2ZkcKlSrZI8jnrwjwX918+Z1P//Zw1BkBfX5devVsxbuxizp+7zt27vvToPolSpYtQz8FeXihK9/MOTakt2bjhCsDmzZspWLAgenp6mJubkzdvXlxdU+/ulyfbDmgCaN+hAV++hjBn9jr8/T9jU6owR46uIn/+hK4+f/9PvHuXOA/P2tqSI0dXMXr0IlzX7CJPHlOWLhtP6zYOMvt99uwNV6/c5cTJ9B+w9NqydQEjR8ylUYO+ADRrXpcVK6fI1Hn61E/mQ37k8D/06Z04t7Rzp4RfnVOnDcF5euI0A4+N+7G0NKd+/WpKi3fz1vmMHOFC44YDAGjarDYrVsrOc3369DWhod+kr8eM7c2PH1EMGzqb4OAw7CqX5vjJdejq6kjrjBjRjajIKMaMXsDXr2GUKVuUE6c2UKhQIYXiatfegS9fQpk7eyP+/l+wKVWQQ0cWkT9/biBhMMe7t4mT5ePj45k6xZXXfv6oq6tRsJAls+cOol//ltI6Eyf3REVFBedpbnz88AlTU0MaN63GzFkD0n3ckvpd5+7v8vfFW5+vX0OZM3vDv/EW4vDR5Ynng/9n3r1LnPO6Yf1+YmPjGD50PsOHJt6AvVv3prh7TAdgneteoqNj6Nhedh76lGn9mOac+XPiV5u3ujByxDwaNxwIQNNmtVixcqJMnYTPWeL3wpixvf79nM395XPmKvM5W7xkLOrqanTqOJYfP6KoW9cO90OzMv1jUVF/e7fw+vXrGTlyJKNHj6ZevXoAnD9/ntGjR6OlpUXv3r0V3lem57m+efOGpUuXypQbGRlhZWWl8H5SmnuXPWR8nmvWUMvwPNc/T/F5rtmD4vNcswfF57lmvfTNc816GZ/nmjXUUFe1+W17/znP1f3ObHLqpj7P9fu3SPpUmJLhea4uLi5MmjSJESNGsGzZsgxGLF/JkiUZNGgQw4YNkylftWoVa9eu5eFDxe/YlemW64ULFyhfXva6WY8ePfD09MzsrgVBEIS/iCJ3YMrMtcibN2/i5uZGmTJl0q6cAa9evaJRo+SDVRs2bMiYMWPSta9MXXP19PSUDv74dRGJVRAE4f/P77y3cHh4OF26dGH9+vUYGv6e6V0mJiaEhSW/x3NoaCjGxumb4ZFt79AkCIIg/F1+PhUnrQUSupJ/XZLeByGpIUOG0KRJExwcft91/bZt23Lt2rVk5VevXqVNmzbp2le2HtAkCIIg/D3UVFRQS6Nl+nN9vnyy85xTu+/Bzp07uXPnDjdv3lRKnClJ6Rru8OHJ76+dFpFcBUEQBKVIz2jhd+/eyQxoSmlq5rt37xgxYgSnT59GWzv1wVLKcvbsWe7cuYOqqioVKlSgbt26aW+UhEiugiAIglKo/LukVQeQ3u8gLbdv3yYoKAhbW1tpWVxcHJcuXWLVqlVERUUpbapRREQEjRs3xsvLCwsLCz5+/Iiuri42NjYcP348XaObxTVXQRAEQSnSc81VUfXq1ePBgwf4+PhIl4oVK9KlSxd8fHyUOod38uTJfPv2jRcvXnDx4kVy5MhBUFAQpqam6R4tLFqugiAIglIkTMVJo1s4nfvU1dWlVCnZOdA6OjoYGxsnK8+sffv24e7ujpWVFa9evQJAQ0ODadOm0bBhQ9zc3BTel0iugiAIglKoKHB7w2x8gyY+ffpEsWLFkpXr6ekRGRmZrn2J5CoIgiAohYoCA5qU8bjNCxcuZHof8lhYWPDhwwfy588vU75u3ToqVaqUwlbyieQqCIIgKMXf/lScmjVrcuLECapWrQpAZGQkRYoUITQ0lLNnz6ZrXyK5CoIgCErxt3cLu7i4EBiY8AAQAwMDxowZQ6FChWjbti0GBgbp2pdIroIgCIJSqKKiwICm7JtdLS0tsbS0BBIeQOPi4pLhfYnkKgiCICiFIlNtlHHN9W+QTZLrn3nWYMbIi03Eqxzy4somp6Rc8mLLrscW/v5zIbvGCv+NeH/Du6Tj9of/ddnimyzpMz2zOzWVglkdQrqoqxbN6hAUpqlWPu1K2cjfdu7+TfFqqCp3DuPvpq5aPKtDyHKi5ZooWyRXQRAE4e/3u5/n+jcRyVUQBEFQCtFyTSSSqyAIgqAUIrkmyhbJNSTqPBJisjoMuVTQwEBL9nFDQd9PIyE6iyJKnQqamOWsL1P2LfpCtjy+Kmigq1lbpmzfSzei4n5kTUBp0FLLQZtC/WXKYuN9gbisCShNasmusX4MP0F8Njx3VdEkT65GMmXR8feA2KwJKE3qaKqWlSnxz6bHFhKOb+4kx/f3vI/oFv4pWyRXCTHZNlnJIyE6G3+Ikvubjm9U3A8is2lylS+O7Jtck4snmniisjoMBcWSfZNrcn/Xsf09RMs1UbZIroIgCMJ/Qdq3P0z7ia//DSK5CoIgCEqhqpKwpFXn/4FIroIgCIJS/O037lcmkVwFQRAEpVBV4JFzaa3/rxDJVRAEQVAKkVwTieQqCIIgKIUqCiRX0S0sCIIgCIpTIe2xwP8fqVUkV0EQBEFJRLdwIpFcBUEQBKUQN5FIJJKrIAiCoBSiWziRSK6CIAiCUohu4UQiuQqCIAhKkdByTesmEv8fsvUDCq5dfkjn1rMoad0TY+3mHDvsrfC21689xkynJbXsRsiUP3n8lh4dXShXtC/G2s1Zu/KQ0uL1vvKI7m3nUr5QX/LotOHEkeup1h/ZfyV5dNokW2pXTIx515bzcutERmbuRvxXLz+kQ6sZFC/QHQOtphw95JVq/cMHr9Gy0RQKWXYmn0k7HGuO5tzp2zJ1mjhOwECrabKlfYvpmYo1qWNuF+hTciI7XI6kWOfJjVf0KTkx2eL/Kkha5+KeG8zruo5h9jMYZj+DRb038Or+O6XF6eq6iyKFGpMrpx12lTpx5fKdVOtfungLu0qdyJXTjqKFm7Bu7Z4U6+7aeRINtXK0aTUy03GuXnSAZrUmUjJ3DypY96Nfx4W8fPYxze28rzymSY0JFDXpSvXSw9jqfkZm/YlD12lacyKl8/aiuHl3GlUdx/4dlzId709rXfdStFAr9HLWxL5SD65c9kmx7tUrPtSu0Y/cpvXR16lF6ZIdWL5sh0ydg/v/oYpdT8yMHDDUrU2lCt3YtuVEpuO8fuUxvdrNp2KRgVjpduDUkZtpbrPJ7RR1bUdRxLQrtcuPZO/2izLrY2JiWTZvL9XLDKeISVcaVBnLhTM+mY4101RAJY3l/yW7ZuuW6/fvUdiUtqZT93r07DhP4e3CQiMY3GcZNeuU5VNQSLJ9FrC2oEXrakwZ567ceCOisCldgI7d6tK388I0689c2JtJM7tKX8fGxeNo70TTVlVl6unq5eTy3RUyZdrampmMNZLSZQrSpYcj3TvMTbP+tcsPqVOvHNNmdUdfX4dtm8/SsfUszl5ZTNlyhQDYumsy0dGJTzH5+jWM6hWH0aJN9UzF+iu/B++4tOcGeYtZKFR/znEncuhoS1/rGulI///pjVfYNSlD4XLN0NBS54T7JZb028iswyMxNNfPVJy7d51i9KiFrFw1iarVyrHebS9Nmwzh/sP9WFnlTv53+X2gWdOh9Onbmk2b53Dtqg/Dhs7F1NSQ1m0cZOq+efOR8eOWUL1GhUzF+NP1q75079eAsraFiI2NY+GMXXRrOYezNxeT85dj96u3r4Po2WYenXrWZdn6odzyfspUJ3eMTPRo3KIyAAZGuRg6phWFiuZBU1OdcyfvMGaQK8ametRyKJepmPfsOsOYUctYsWosVaqVYYPbQZo3GYXPwx1YWSU/N3R0cjBocDtKlylMTh1trl25x5BB89HJmYO+/VsCYGikx4SJPSlaPD+amhocP3aVfn1mY2pmSP0G9hmO9fv3KEqWzk/7rrUZ0HVJmvW3bDjN/Ok7mLeyP2UrFOLe7ReMH+aGvkEuHBvbArBw5i4O7LrM/JUDKFQ0D5fO3aNf50UcODuLUmWtMxxrZonbHybKUHKVSCQ4OjqipqbGqVOnZNatWbOGiRMn8uDBA6ysrDIVnEMDWxwa2KZ7O6eha2jToSZqaqocT9J6rFCxCBUqFgFg5pTNmYovqboNKlC3geJfeHr6OujpJ37ZnzhynZDgCDp2qyNTT0UFzCwMlRYngGPDijg2rKhw/XmLZZ9jOm1WD44fuc7JYzekydXQSFemzr7dl8iZU4uWSkqukRFRrB+3ix4zWnN03XmFttEzykVOvRxy1/Vf2FHmdc+Zrbl9+iG+3i+p2iJziWvZsi306t2KPn1bA7Bk6TjOnPZi3do9zJk7PFl9t3V7sLLKzZKl4wAoUaIgt28/ZsmSzTLJNS4uju7dJjHNeRBXrtwhNORbpuIE2HxgkszrRa6DqFCwHw/uvqJy9ZJyt9nmfoY8eY1xnt8TgCLF8/Lg7ivclh+RJtcqNWxktuk9uDF7t1/kptfTTCfX5ct20LN3M3r3bQHA4qWjOHPaG7e1+5k9d3Cy+uXKF6Nc+WLS1wUK5OHggQtcveIjTa61ast+1wwb3oGtm49x7eq9TCXXOvXLU6d+eYXr799xmS69HGjeJuFHdn5rc+7cfI7r0kPS5Lp/52WGjW1F3QYJ++3Wtz4Xz95j/cqjLN8wLMOxZpZIroky1C2soqKCh4cH169fZ926ddJyPz8/xo8fz/LlyzOdWDNq26az+L3yZ9yUTlny/pmxY9M5atQpQ14rM5nyiPBIKhUfgG2RfnRvM5cHPq+yKMJE8fHxhIf/wNAwV4p1tnqepnW7muik0PpJr22zD1GmVnFKVi2s8DYz2qzEqeZcFvbawJPrL1OtGxUZQ1xsHDr68pOxoqKjY7hz2xdHxyoy5Q6O9nh53ZO7jbf3fRwcZb/A69evyu1bj4mJSXzQ/exZ6zA1MaR3n1aZijE138K+Awktz5TcufGMmvVkHxZes15ZHtx9RUxM8mewSiQSrlx4wKvn/lSuViLZ+vRIOL5PcXSsLFPu4FgZb68HCu3D5+5TvL0eUKOm/KQnkUg4f+4mz56+pXqNcpmKN72iomPQ0taQKdPW1uTe7RfSYxsdFYOWVpI6OTS56fX0j8Upz8+pOGkt6eHi4kKlSpXQ1dXFzMyMli1b8vRp1v6disjwNdd8+fKxfPlyxowZg5+fHxKJhD59+lCvXj169uypxBAV9/LFR2ZN3cQ6z9Goq6tlSQwZFegfzD+n79K5Zz2Z8sLF8rJs3VA8d09kjecotLQ1aOEwmVcv0r4m9jutWnqAiIhIWrWtIXf97ZtPefzoDd1711fK+10/fo83jz/SZlQDheobmOrSfUYrBi/vwuAVXbCwNmFRb3ee3vJLcZt9S05iaKZHySqKJ295Pn8OJi4uDjNzI5lyc3NjAgM+y90mMOAz5ubGMmVm5kbExsby+XMIAFev3sVj40HWuk3LVHypkUgkzJq4mUpVilOsZMo/kD8FhmJiKtt1bmKmT2xsHF+/JLamw0K/U8KiO4WNutC77XxmLOxFjbplMhXj588hKRxfIwICvqS6bUGrZujmqEEVu14MHNxW2vL9KTQ0HCO9OuTSrk7LZqNZunw0DkmS+O9Wq15Zdmw6z/27r5BIJNy785LdWy4QE5N4bGs5lGX9qmP4vfAnPj6eS+fvc/rYLYICgv9orEn9juR68eJFhgwZgre3N2fOnCE2Npb69esTERHxm/4K5cjUNdcePXpw4MABevXqRZs2bXj48CEPHz6UWzcqKoqoqCiZMi0tLbS0tDITglRcXBz9eyxi/NTOFC5iqZR9/km7t/6DnoEODZvZyZTb2hXF1q6o9HWlKsWpX3UsG9eeYPaiPn86TAD27rrIvNnb2b53KqZmBnLrbPE4Q0mb/NhWKiZ3fXp89Q9hp8tRnNb3RiPJr/WUWFibYmFtKn1duFx+ggNCOeVxmWIVk1+TOuF+kevH7jFuUz+F3yMtSb9EJBJJql8s8ur/LP/2LYKe3Sezdt00TEyUe4ngV1NHb+TJo7fsPT0jzbpJ/5Rf4/0pl642J64uICIikqsXHjB70masrM2SdRlnRHqPL8C5i+uICP/Ode+HTJm0hkKF8tKhU+IPQF3dnNy4s5mI8B+cP3+TcWOWY10wT7Iu499pxPg2fAoMoWXdKUgkEkzM9GnbpRZrlx1GTS2hPTR9fk/GD1tHHdtRqKiokN/anPZda7N764U/Fqc8v2Oe68mTJ2Vee3h4YGZmxu3bt6lZs2Y69/bnZHpAk5ubG6VKleLy5cvs3bsXMzMzufVcXFyYMUP2A+vs7Mz06dMzGwIA4d9+4HP7BQ98XjF+ZEJXdXy8BIlEgplOS/YenUHNOmXT2EvWkEgk7NxyjrYda6GpmfoXu6qqKuVsC+P3wv8PRSdr/55LDBuwAs/tE6hdr5zcOt+/R7J/zyUmTuuilPd8/egDYV/CmdlulbQsPi6eZ7dec367N+t8ZqGqlnYnTMEyVngfvZus/OTGSxxzu8AY9z7kK5Z8sFF6mZgYoqamRmCSVlRQ0FfMkrROfzK3MCEgSav2U1Aw6urqGBvr8+jRS16//kjLFokjyePj4wHQ1rTlke9BChXKl6m4p43ZyNnjt9l9cjq5LeXH+ZOpuT6fgkJlyr58CkNdXQ3DX7qTVVVVKVAoYYCRTZkCvHj6gTWLD2YquZqYGKRwfIMxT9KaTcraOg8ApUoXJijoK7NmbpBJrqqqqhQunHAcy5YrylPf1yyYt/mPJlftHJosch2Ey4p+fA4KxczCkO0eZ8mlmwMj44RxDcamemzYOZbIyGhCvoZjntsQl2nbyZdf/vfvn5Kea65hYWEy5Yo2tkJDE847I6PU/62zWqaTq5mZGf379+fgwYO0apXydaCJEyfi5OQkU6asViskjKi9cnulTJn7uuNcvnAfzx0TsCpgrrT3Ujavy4/wexlApx710qwrkUh4dN+P4jb5/0BksvbuusjQ/svZsHksDRpXSrHegb1XiIqKoUPnOinWSY8SVQoz45DslCqPyXuxsDalUd9aCiVWgLdPPqJvKjvo6qT7JY6uO8+o9b0pUCqvUuLV1NSggm0Jzp71omWrutLyc2ev06x5bbnb2NuX4dhR2WkqZ878r707j4uqeh84/gFkQGURRHBDBDU3XDGXMkVzKXPJEk1NTc0lc0XNXXFJNMU9cd/XtNx3NM0NNQQ1NXNNFAZQdlRAmN8fo0MjAwwwCH5/z7sXr1dz5tzDw/HMPPeee8+953GrVw1TU1OqVHEm8MpOrfenTFpCXPwz5s3/AUdH/a6e1kWlUjF51FqO7LvI9oNTKFc+6y/ouvXfw++Q9lKs0yeuUqOOC6amGX+tqFSQlJj+nGx2qPu3Mn5+F+nQ0V1TftzvIu3a638ko44l8yVtKhUkJeVu2VtOmZoW0uzk7N15jo8/qYuxsfZYNzdXULK0LcnJLzm09wJtOzbS1dRbk53bHzo6au8M6nOwpVKp8PT0pHHjxri6uuYq1rxmkKU4hQoVolChzJvKyRRwfPxz7t9NO0J7+CCMa1fuYWNjSdlyJZg2cT2hIZH4rhmBsbExVd9IOCVKWGNurtAqT0pK5tZN9VrGpOSXhIZEcu3KPYpamONSoXS24ntTQvxz7t9Val4HPwjnryv3KWZrQVnHEsycvAllSCSLVmlfLbp1/XHqvl+JKtXTn+PymfkLbu9XwrliKeJin7Pa9yDXrz5g5vx+uYo1Pv459/7Tt/8+COPqlXvY2FjgWM6eqRPXERLylOVrRgLqxDqwzzxm+fTn/QZVCHt1bse8sALr/1zxDOoLmT5r3xDb4la5ivG1wkXNKFtJO3mYFVZgUayIpvzXeYeJCo/l21mdATi24QzFS9tQpqIDL5NT8N8XSMDRvxi0MO1o+tDqU+xedIx+c77CrrQNMRHq81lmRRSYF83djt/w4T34ptcE3Nyq07BRTVat/JWHD0PpP6ATABPGL+Lx43DWrZ8BQP8BHiz9eRujRs6l77df4H/+KmvX7GLTZvUSNHNzM1xdtc8FWxdT7yi8WZ5dEz1Xs3fHWVZuG01Ry8KEh0UDYGVVBPPC6iVfs6dsQRkayfwVgwHo3rcl61ccYdrYDXT9pjmXL95m+4YTLFqbthP089xd1KxbASdnB5KSXvL70UB+2/oHM+bn/nTGsOFd6d1rKm5uVWnQyJXVK/cQ/DCMfgPUO/gTxy8l5HEEa9ZPAcB36U4cHR2oXEX9XXDuzFXm+2xm0GAPTZs/zVpPXbcquFQoS1JSMocPnWPTxoMs/vmHXMWaEP+CB/f+873wbzjXrz6gmI0FZRztmPWqbxe86tt7t0MICrhLnXoViYlOYOWS/dy6Ecy85WlXQQdeuo0yJJJqNcujDIlkvvdOUlNVDBzePlex5lZ2poWDg4Oxskr7jtAnPwwePJirV69y5syZHMf4thToda5BAXfo0HqC5vXrdalffd2cn1cNJ0wZxePgiGy1qQyJxL3BcM3rJfN3sWT+Lj78yJW9x7Je75mZK5fv0unTKZrXXmPXAdC5uzsLVgwhXBnF40faU3+xMQkc2OPP9Dl9dLYZG53A6CHLiAiLxtKqCK61nPnt6HTqvFpOlFOBAbdp1yptCcaEH1YB0LXHx/iuGoFSGcWj//Tt2lWHePkyhVHDfBk1zFdT/rr+a3f+ecz5szfYdWB6ruLLrugncUSGRmtev0xOYcecg0SFx2JqZkqZig4M8+1FzaZVNHV+3+rPy+QUfIdv1mqr/aCP6TBYe21pdnXu0pqnkdH8OGM5oaFPqO5akX37l+DkpN6BCw2NIDg4befG2bkM+/YvYeTIufgu3U7p0iWYv2BMujWueWHTKvXNH7p8qn3aZq7vd3h87Q5AuDKakOC0adhy5e1Z9+tYpo1dz8aVR7AvZYPXnN6aZTigXt850XM1oY+fYl5YQYVKZViwajDtvtRex50THl1a8jQyhpkzVhMa+pTqri7s2T8PJyf1tL4y9AnBwWkJLTU1lUkTfHlwP4RChUxwqVCGGTMHaZIxQELCc4YOnsPjRxEULmxG5SpOrNvghUeXlrmK9WrgXbq0maZ5PW2ceglgp25Nmbd8ULq+TUlJZeXi/dy9HYKpqQmNPqrOLr/pWlO+iYnJzJm+neAH4RQpak6z1rVZsPJ7rItp7+i+bdmZFraystJKrlkZMmQIe/fu5Y8//qBsWcPMMuUlI9XrqxBywcvLi927dxMUFJSj7aMSj6Aif6ZesmKEAhsz7StUw57tJ7WAxmuMAocibbXKYpOOFcj+NUKBlUL7i2vLPwt5kfI8nyLKnLlJYbq9pz09/TL1LyAlfwLKkgmFjLWnzh7F7yGVxAzq5x9jzChroX3lblJqAJC7KeS8UwiFsfZ52McFtG9B3b9l3uhfQ4qNjcXa2pq/QzdgaVUk07pxsc+oUqonMTExeiVXlUrFkCFD2LVrFydPnqRSpdwdWLwtBfrIVQghxLsjL64W/v7779myZQt79uzB0tISpVI9I2FtbU3hwrlbk56XDHJvYS8vrxwftQohhPjfkBfrXH19fYmJicHd3Z1SpUppfrZv355Hf4VhyJGrEEIIg8iL2x8a4MxlvpDkKoQQwiDk3sJpJLkKIYQwCHlYehpJrkIIIQxCjlzTSHIVQghhGEbp7zmtq87/B5JchRBCGEheLMZ5N0lyFUIIYRDZubfw/zpJrkIIIQxCjlvTSHIVQghhEHJBUxpJrkIIIQxCpoXTSHIVQghhEDItnEaSqxBCCIOQaeE0BSK5GmGa3yFkSFdsRigM88SDPGCEQkdZwexfXXGZmRTcp1zojs3krcehv/SxGesYHwWB7rgKxNdTBtLHVlD7Ft5ebEZGWU/7/j+ZFTbM81yFEEL8//X6ea7KyF+xssr8ge2xsQmUtP1S7+e5vqsK8q6hEEKId4hMC6eR5CqEEMJA5JKm1yS5CiGEMBDjVz9Z1fnfVyCSa1LKZeBlfoeRgUIoTOq+URYGFNRT1UaAg1ZJiuoekJov0WTOGBMjF62SR/EHSCUpn+LJnDEKylp8plWWnPoXkJI/AWXJBFNjV62SpNQrFMzPWiEUxrW0SuKT/wCS8yecLJliYdpEqyQ59QYFeyxUy/PfItPCaQpEclV/2AviBz4jKgpmsgLde4WpFNwPvbZUkkhVJeZ3GLrp/E5I4d0au+/SZy0ZCuiOlm7v2ljICzIt/FoBSa5CCCHefTIt/JokVyGEEIZhpMcDXf+fLHSV5CqEEMIg5JxrGkmuQgghDESmhV+T5CqEEMJA5IKm1yS5CiGEMAgjTDDK4n7bWb3/v0KSqxBCCAP6/3FkmhVJrkIIIQxEzrm+9v/jrxRCCPEWGOn5k31Lly7F2dkZc3Nz3NzcOH36tGFCziOSXIUQQhiEEcZ6/WTX9u3bGT58OBMmTCAwMJCPPvqITz/9lIcPH+bBX2EYklyFEEIYSN4cuc6bN4++ffvy7bffUrVqVRYsWICjoyO+vr6GC93AJLkKIYQwjNd3aMrqB/UD1v/7k5io+57iSUlJBAQE0KpVK63yVq1ace7cuTz/k3KqwCfXZb6/8V7FTlgVbUbD+n04czpIr+3Onb1KEbMmvO/WK9170dFxDB3ig1PZ9lgVbUZN124cOpg3/0hRUdH06DEEa+sqWFtXoUePIURHx2S6jZFRGZ0/c+ak7aWtWLEJd/dOWFlVxsioTJZt6h9vDL16/oBtsXrYFqtHr54/EB0dm2H95ORkxo6ZS+2a7bCyqINjmY/4ptcYQkLCNHUiI6MZNmQ61ap8gmXR2jg7NWP40BnExMTpHddSn910aDoB19K9qecygP5dfbh7OyTTbcKVUQzrs5jmdT1xse7GtDHr09XZufkUzlZd0/0kvsj9DeOX+e7gvQrtsSzyAQ3e/5ozpwMzrLvrtxN82moQpR1aULxYUz76sDdHj5zXqrN65S6aNf0W++LNsC/ejE9aDeLSxb9yHWdavDt5r0JHrIo0oeH7vTL9rJ09E4T7R/0oVaIV1kWbUqNaFxYu2KpVZ/dvv9Oo/jfY27bAxtKd9+v2YPPGQ7mO88zpa3h87kUlp6+xVLRh357MP7vK0Ej69JhNner9sDL7jDEjl+ust+e3M9SrOYDiFu2pV3MAe3cb7jthme8vvFehLZZFGtLg/W6cOX05w7qhoRH06D6e6lU7YlbIjZEj5qSrc/36XTp3GkUll89QmNRl0cLNBos1d4z1/AFHR0esra01P97e3jpbfPLkCSkpKTg4aD/ty8HBAaVSmVd/SK4V6OS64xc/RnkuZOy4nlz4cy0fNq5J+7ajePgw8w6NiYmnT+/pNGvulu69pKRk2nwynH8fhLJ1+wyu3diK77IxlClTIk/+hm7dBhMUdIPDhzdx+PAmgoJu0KPH0Ey3CQ0N1PpZs2YeRkZGfPllG02dZ8+e88kn7owfP8Sg8X7dfRRXgm5y4NBKDhxayZWgm/Tq+UOG9Z89e0Fg4A0mTBzEpYBf2fHrYv755wEdOwzS1AkJCSckNJzZc34g6OpeVq/15siR0/T7dpzecV04c5Me/Vvx2/FpbNgznpSXKfT83JtnCS8y3CYp8SW2dlZ8P+pzqtYol2E9S6vCXLztq/VjZq7QOzZdftl+lJEjfBg7rg8XAzbTuHEd2n02NMOxe+Z0IB+3bMDe/Qvxv7SRpu716NhhBIGBf2vqnDoVQJevWnP0+DL+OLsWR0cH2nwymMePw3MVK8CO7ccYNWIBY8d9w4WA9XzYuDbtPxuRYbxFixbmu0EeHD+5jCvXtzJu/Dd4TVrOqhW7NXVsbK0YO+4bTp1dyZ9Bm+j5TVv69Z3B0SP+uYr1WcILatR0Zu6C7/Sqn5iYjF0Ja0aP/YoaNZ111rngf5Ne3WfxVffmnPvzZ77q3pxe3by5dPFvnfWz45ftRxg5Yi5jx/XlYsCWV2NhCA8fhmYYb4kSNowd15eatd7TWef5sxe4uJRhxsyhlCxpl+sYDcVIz/8AgoODiYmJ0fyMG5f594HRG/ckVqlU6coKEiOVSmXwB5MmJSWhUOj/5ZSUchFdj2pq3Kgfteu+x5KfR2vKarp2o337j5gxM+MP1tfdJlOxYllMTEzYu/cPLgWkHbGsWL6LeT5buHZ9K6am+qxEKoTCpP4bZUr0eeTczZu3qVbNHX//fTRooH4mrL9/AI0atefvv09RuXJFPX4/fP55H+Li4jl+/Jd07508eY5mzTyIirpBsWLWqPeXSmrVSVHdQZ9Hzt28eZca1T/j7PntNGhQ61W8QTT+4Cuu3zxI5couWbSgdunSNRo18ODegxOUK1daZ52dOw7Ts8doEhKeUahQ2r/Dw/hdej1y7umTWOq5DGDbock0+LBqlvW/ajONajWcmDxbeyZj5+ZTTBu7gavBq7Nsw9jIjHIWHbXKkjN4PuqHjXpRp04VlixN+8KoUb0T7Tu48+PMwVn+LoBaNTrj0bklEyf10/l+SkoK9sWbs2DRaHr0bKujRiFM33hGalJqgM54GzfqQ+06lVmydIymrGb1LrTv0JQZMwelq69L5y/HULRoYdZu8MqwToN6Pfm0zYd4TRuQLlaFsfbOcHzycbJ65Jylog1bdkykXYcP9Irx0xZjqFnLhdk+2r+/Vzdv4uKe8du+6Zqyjm0nUayYBWs3jXmzGUCBhenHWiXJqdfQPRZ6vhoL4zVlNap/QfsOzfhxZuY7xy2a96NWrffwmT86wzqVXD5jyLBuDB3WPZOWCmFqXCPT35UbsbGxWFtbExntj5WVRRZ147Et1pCYmBisrKyybDspKYkiRYqwY8cOOnZM+/wNGzaMoKAgTp06lev484JBjlzd3d0ZPHgwnp6e2NnZ0bJly1y3mZSUzOXLt2jZUjuxtWhZH//zGU+FrV93gHv3HjNxch+d7+/fd4aGDV0ZOsQHx9JtqVPra2Z7ryclxfDPOz1/PgBraytNYgVo2NANa2srzp0L0KuNsLAIDhw4Tt++XQ0e35v8zwdhbW2pSawADRvWxtrakvPnMp7SfFNMTBxGRkYUK5bxBycmJg4rKwutxJodcTHPAChmk/kHWR/P4l/wYfUhNKryPX09fuL6lfu5ai8pKZnLAX/TomVDrfKWLRvif/6qXm2kpqYSH5eArW3Gffjs2QuSk19ia2ttgHhv0bJlA63yFi0b4H/+ml5tBAXewv/8NT5qUkfn+yqVihPHL/HPrYc0/qh2ruLNCxcv/E3zFnW1yj5uWZcL/jdy1a66b2/qGAuN8D9/JVdtF0z6TwvrS6FQ4ObmxrFjx7TKjx07xgcf6LdTlR8MdhOJ9evX891333H27Fl0HQwnJiamO2FtZmaGmZmZzvaePIlW75nb22qVO9jboAx7qnOb27eDmTjel+Mnl2b4pX3/fggnf79M126t2LNvLnfuPGLYEB9evkxhwiTdCTmnlMpw7O2Lpyu3ty+OUqnfVN769TuwtLTgiy8+NWhsuiiVEen6G8De3hal8olebbx4kciEcT507dY2wz3Yp0+j+HGGL/36f5WjOFUqFTPGb6Reo8pUruaYozZeq1CpNHN8B1K5ejni456z1vcQnVp5cfDsLJwrlspRm6/HroODdl/aO+jfj/PnbSIh4QWdPDLeUZ0wbgllypTg4xZvzqzkLF77N+J1cLBFqdT9WXvNpVw7IiKiefkyhUlTvqXPtx203o+JicfZsR2JiUmYmJiwaMloWryRxAuCMGUU9vbFtMrs7YsRpozKVbtpY0H7e8Bej759F+XV7Q89PT3p0aMH9erVo1GjRqxYsYKHDx8ycODAnIaa5wyWXCtWrMhPP/2U4fve3t5MnTpVq2zKlCl4eXll2m76efb0ZaCeIuvVw4tJU/ry3nsZn19LTVVhb2/D0mU/YGJiQl23KoSEPGG+zxa9k6uX11ymTvXJtM6lSwd1xq/+G/Q/V7BmzTa6d++Iubm5XvV1meq1iOnTFmdax//iDiCjeHWXvyk5OZluXT1JTVWx5OcpOuvExsbTru1AqlarwOQpOTtfPHnkWv6+/pAdR7xytP1/1alfiTr1K2le12v4Hm0/Gs/65UfwmvNNrtrO6TmibVsPM33qCn7d5aNzZwdg7pz1bN92hGMnlmNurnsH9W3Ee/zUchLin3HB/y8mjl9KhQpl6dI17apOS8siXLy8gYT455w4cYkfRi3E2aU0Td3TXw+R3/T9rslZ29qvC/r5wpzLmxv3d+nShadPnzJt2jRCQ0NxdXXl4MGDODk55SjKt8FgybVevXqZvj9u3Dg8PT21yjI6agWwsyuGiYkJYW8cpYZHROGg4wsnLu4ZAX/+TVDgbYYPnQ+op9ZUKhVFzJpw4NB8mjV3o1TJ4hQyLYSJSdreU5UqTiiVT0lKSkahMM3ybx08uDdffdU+0zrly5fl6tWbhIWlP1KJiIjEwSHrC6hOn77ArVt32b49d2u5vh/8NV2+yvzIt3z5Mly7+k+6/obX8aY/Av+v5ORkvuoyggf3H3Hs+DqdR61xcfG0+fRbLCyK8OtvSzA1zbqv3zRl1FqOHwpg+6EplCqTeUw5YWxsTM26Ljy4m/OrEF+P3TePTCLCo7Lsx1+2H2VAv+ls3T6bj1voPsKb57OR2d5rOXx0KTVrVtJZJyfxhr0Rb3h4VLqj7zc5O6vPqbvWqEh4eCTTp63SSq7GxsZUrKieXahV+z1u3XzAT7M2FLjk6lDShrAw7aPUiIho7B2K5ardzMdC5n377sqbnYZBgwYxaJB+5/8LAoMl16JFi2b6fmZTwLooFKbUrVsZP79LdPi8qab8uN8l2rVrnK6+lVVRLgdt1Cpbtuw3Tv4ewLbtP1LeWT3F1+iDGmzfdozU1FSMjdVz/7dvB1OqVHG9EiuAnV1x7OxssqzXqJEbMTGxXLwYSP366nNRFy5cJiYmlg8+yPrLZfXqrbi51aRWrep6xZVxvLbY2WV9Xq5ho9rExMRx8eJV6tev+SreK8TExNHoA93n0iAtsd65/S9+J9ZTvHj6vomNjefTT/piZqZg956l2T7aUqlUTBm1jqP7L7H1wCQcy9tna/vs/J6bV/+lcvWcTzcrFKbUdavCcb8LfN6xmabcz+8C7do3zXC7bVsP0//b6Wzc/CNtPks/xgF85m7A+8fVHDi0BLd61XIcY/p4K+Pnd5EOHd015cf9LtKufRO921GpICkx8wuQVCr1BSoFTf0GVfj9eCCDh6VdMHPC7zINGuauj9V9W/XVWGiuKffz86dde/dctV0wyb2FXyvQN+4fNqILvXtNx82tCg0aurJ65R6CH4bRb4D6AzBxvC8hIU9Ys24SxsbGVHfVvprVvoQN5uYKrfL+Azuy9OedeI5YwKDvO3Hn9iN+mrWB7wd7GDz+qlUr8cknzejXbzTLl89W//7+Y2jbtoXWlcJVqjTB23scHTumHV3GxsaxY8d+fHwm62xbqQxHqQznzp0HAFy79jeWlkUpV84RW9uSOrfJOt4KtP7kIwb2n8TSZeop/O8GTOaztu5aVwpXr/opP8705POOLXn58iWdPYYRePkGe/YtIyUlBaUyAgBbW2sUCgVxcfF80rovz589Z8PGOcTGxhMbGw+YUNLeWWsWISOTPdewZ+c5VmwdiYVlYSLCogGwtCqCeWH1lek/eW1FGRLFvBVpe7c3rqr751nCCyKfxHHj6gNMFYWoVKUsAAu9d1L7/Uo4VyhJfNxz1i07zI1r/zLVp3eO+vC1YcO707vXZNzcqtKgUU1Wr/yN4IdK+g/4EoAJ45cQ8jicteunAerE2uebKcxbMIoGDV0152YLFzbH2lo9CzB3znq8Ji9jw6YZOJUvpaljYVEEC4siuYy3K717TX0Vr67P2lJCHkewZr16ut936U4cHR2oXEU9LXfuzFXm+2xm0H8+Rz/NWk9dtyq4VChLUlIyhw+dY9PGgyz+OeOlXfqIj3/OvTtpa5z/fRDG1aC72Nha4ljOnikT1hIa8pQVa0dp6lwNugtAQvxznkTEcDXoLgqFKVWqqU8hfTekA580/4F5c3bwWbuGHNjnz+/Hgzh6Mv0a0+xSj4VJmYyFxa/GQtqVykFBt179rc+IiIgmKOgWCoUp1aqpP4dJScncuHFP8/8hj8MJCrqFhUVhKlbM+LRYXjMyMspyuvt/czo8vQKdXD06t+Dp01hmzlhLaOhTqru6sGffXJyc1MlDqXxK8MOwLFrR5ujowIFDCxg9ciH16vSidBk7Bg/xYNQPX+fFn8DmzYsZOnQyrVp1A6B9+1YsWTJDq86tW3eJidG+UcO2bXtQqVR07fq5znaXLdvI1KnzNK+bNPkCgLVrF/DNN1kvTcnIxk1zGD7sRz5t3ReAdu2bs2jxpDfiva+5AcSjR0r27T0BgFsd7Vj9TqzH3b0BAQHXuXhBfWVk5Urad1m5f/8+5cuXzzKuTav9AOjaZrpW+RzfgXTqrj4aDFdGE/JIexr+s8ZpS2GuBd5nz46zlClnx5m/1OegY2OeMX7YKp6ERWNpVYRqNcuz/dBkatfTb5lURjp3aUVkZAw/zlhFaOgTqrtWYO/+hTg5qWdQlKFPCA5Om3petfI3Xr5MYejg2QwdPFtT3qNnW1av9QJgue9OkpKS+aqz9tKQiZP7MXnKm0tbssejS0ueRsYwc8bqtM/a/nkZxpuamsqkCb48uB9CoUImuFQow4yZgzTJGCAh4TlDB8/h8aMIChc2o3IVJ9Zt8MKjS+5WEwQG3KZNy7Ga1+NGrwSgW48WLF/tiVIZRXBwhNY2H9ZPO78fePkOv2w7STkne67fXgdAw0bVWLdpLNOmbGCG10acXUqxbvNY3q9fJVexAnTu0vrVWFj5n7GwCCcn9ZT6m30LUN8tbXXA5YCbbNt6CCenUty+dwCAkJAIrTrzfDYyz2cjTZq64XdiZa5jzjl5WPprBlnn6u7uTu3atVmwYEGOts9onWvBkPN1rvkj5+tc3z4TTIy0k5i+61zzQ3bWuRYM+q9zzX85W+eaf/Rf51owvJ11rtExf2FlZZlF3TiKWbvqvc71XWWQI9eTJ08aohkhhBDvNDlyfa1ATwsLIYR4d+jzSLmcPHLuXSTJVQghhEGoL1TM/MhUXed/nyRXIYQQuaJQKChZsiSOju/rVb9kyZLZuv/8u0iSqxBCiFwxNzfn/v37eq9hVigUubrr3LtAkqsQQohcMzc3/59PmNnx/+PMshBCCPEWSXIVQgghDEySqxBCCGFgklyFEEIIA5PkKoQQQhiYJFchhBDCwCS5CiGEEAZWQNa5FpAwdNIVmxEFd79E163HCmqs6eMyRlFg7+ttjK47ymT9LNr8oyu2gvpZ0xWX6VuPQn+6YnvXxoLISwZ55JwQQggh0hTUQxohhBDinSXJVQghhDAwSa5CCCGEgRWIqxtiEk+gIjm/w9DJCFOszZprlSkTDpGKfk9/eNuMUVCy6KdaZQW1f3X17aF/15KU+iKfIsqcwticT516a5W9TP0bSMmfgLJkQiHjKlolofEFc+wao6CUhfa4TU69QUHuW1PjalolBbVvQXf/irxVIJKrimRUBXRQ6pJKUoH9EOnyLvVvUuoLklKf53cY2ZBCwU0A6anHbmJ+h6GnFOBlfgeht3erb0Vek2lhIYQQwsAkuQohhBAGJslVCCGEMDBJrkIIIYSBSXIVQgghDEySqxBCCGFgklyFEEIIA5PkKoQQQhiYJFchhBDCwCS5CiGEEAYmyVUIIYQwMEmuQgghhIFJchVCCCEMrEAn13On/6LrF9Op5vwNtubtObDXP9P6/mdv8In7D1Qo3Z3SxTrRoOZ3LF20R6vOvt3naP6BJ+UdulLW1oMm9YexffPvBonX/8wNenvMwq3iABwtOnN438Ust1m3/DDN6o6gol13mtYZxs4tp7Te37LWjy9aTsa1bG9cy/ama9vpBP55J9exvmt9+1+7fI/TueJI1s3YnWGdqPBYFo7YxLCWs+hSaVSmdQHO7g+kc8WR/DRwjWGD/W9MUbH06jme4jYfUtzmQ3r1HE90dGym26hUKqZN9aVc2RZYFq3Px837cv269r9/YmISw4Z6U9K+KdaWDejYYSiPHoVlK7YLZ27Q22M29SoNpJxlF47su5TlNutXHKG52wgqlfga9zrD043d5OSXLJi1k8Y1h1LJ7mtaNxrNyWNB2YorM8t8f+G9Cm2xLNKQBu9348zpyxnWDQ2NoEf38VSv2hGzQm6MHDEnXZ3r1+/SudMoKrl8hsKkLosWbjZInO9i34rcK9DJNeFZIq41nJk9v79e9YsUNaPfd5+x388b/6CfGTm2MzO9NrFu1WFNHRsbSzzHeHDk1E+cvrSIbj0/ZnD/hRw/lvEHU1/PnyVS1bU8M3z66FV/w8qjzPbayojxHhy/NA/P8Z2Z6LmaYwf/1NQ5f/oGHTw+ZPvBKew+PoPSjsX5usMMQkMicxXru9a3r925+hC/7f44VSmVab3kpJdY2VrwxaAWWdaNeBzJRu99VH3fxWBx6tKj+1iuXLnF/oNL2X9wKVeu3OKbnhMy3WbunLUsmL+RhYvGcv7CZko6FOfT1gOJi0vQ1PEc8RN7dp9g85bZnPxjHfEJz+jQfggpKfo/Cu/Zs0Sq1XBi+tzeWVcGNq5Sj93h4z3wu+iD5wQPJo1cw7GDAZo6c6ZtZ/MaP6bN6Y3fJR++7tuSft3m8teV+3rHlZFfth9h5Ii5jB3Xl4sBW2jcuA7tPhvCw4ehOusnJiZTooQNY8f1pWat93TWef7sBS4uZZgxcyglS9rlOsbX3rW+FYZRIJ7nmpGWrd1o2dpN7/o1a1egZu0Kmtflyjuwf895/M/e4JtvPwGgcdMaWtsMHNyebZtO4H/2Bh+3rJureJu1qkOzVnX0rv/btj/o3qcF7Tt9AICTswOBl27jO38PLdvUA2DxmqFa2/y0ZCAHd1/g7MlrdOrWNMexvmt9C/AiIZHFnpsZ8KMHv/3sl2ld+7K29J70OQC/78h4BiE1JZVFnpvpPKw1N/+8R0Js3jxL9ubNexw5cpYz5zbSoEFNAJYtn8JHH/bg1q0HVK5cPt02KpWKRQs3M278t3T8ogUAa9bNoEyp5mzdcpD+AzyIiYlj7ZpdrFv/Ix+3aAjA+g0zcXZqzXE/f9p8WlOv+LI9dreepnvvFrT/Mm3sXtaMXfW4+m3baYaM7kjz1up2e3zbilN+V1i5eD8LVw3R+3fpsnDBZnr3+Zw+33YEwGf+aI4ePc/yZTv5cWb6tsuXL828BaMBWL9uT7r3Aeq9X51671cHYOL4RbmK77/etb4VhpGtI9d9+/ZRrFgxUlNTAQgKCsLIyIjRo0dr6gwYMICuXbsaNsocuhp0l4v+f/PBR64631epVJw6cYU7/zzmg8bV33J0kJSYjJmZqVaZeWEFQX/eITlZ90Oinz9LJDn5JcVsLN5GiBnKj75d5fUbddyrUfND3UceObFz8VGsbC1o3rmBwdrUxf/8FaytLTWJFaBhw5pYW1ty/lyQzm3u33+MUvmEFi0bacrMzBQ0aeLG+fNXALgccIPk5Je0bPWBpk7p0vZUd63I+fOBefPHAIlJyZiZvzF2zRVcCUgbuxmN70vnb+XqdyclJXM54CYtWjbUKm/ZshH+r/rlXZaffSsMJ1tHrk2aNCEuLo7AwEDc3Nw4deoUdnZ2nDqVdj7g5MmTjBgxIt22iYmJJCYmapWZmZlhZmaWw9AzVr1Cb55GxPDyZSpjJn5Fzz6ttN6PjUmguktvEhOTMTExZs7CgTRrof+epaE0aVGLbetP0LpdfWrUduZq4D22b/id5OQUIp/G4VDSJt023pM3U7K0LY2b1dDRYt7Lr749uz+Q+9cf4b1reK7beu3vgPuc2HGRn/Z5GqzNjISFPcXePv2/p729DWFhT3Vuo1Q+AcDBobj2Ng7FefhvyKs6T1EoTLGxsdKq42Bvi1Kpu11DaPpxLbauP0Grtu9rxu4vG09qjd2mLWqxcskBGnxYFScXB86c/IujB/4kNSU1V7/7yZNoUlJSdPRL3v7Nb0t+9q0wnGwlV2tra2rXrs3Jkydxc3PTJNKpU6cSFxdHQkIC//zzD+7u7um29fb2ZurUqVplU6ZMwcvLKzfx63TQz5uEhBdcunCLaZM24FKhFF92SZtCtbAszKmLC0iIf8Gp368wccwayjuXTDetmdeGjelERFg0HZpNQKVSYWdvjcfXTfGdvxcT4/STCr7z97Bn51l2HPLC3FzxVmN9LT/69klIFOum72bCugEo3thbz6nn8S9YPHILA2Z6YGWb81mAaVN/Zvo030zrnL+wBQAjI6N076lUoKNYy5vbqVQqnW1lt93cGDbmSyLCovm8+UTN2O3UvSnLFuzFxEQ9dr1mf8OYIctp5jYCIyMjnJwd6Py1O79sOmmQGN78+/Tpl3dBQehbkXvZPufq7u7OyZMn8fT05PTp08yYMYNff/2VM2fOEB0djYODA1WqVEm33bhx4/D01D5CyIujVgAn55IAVHMtT0R4NLNnbNNKAMbGxrhUKA1AjVou/PP3I+bP2fnWk2vhwgp8fAcxa1F/noTHYF/Shs1r/LCwLIytnaVW3WUL97Jk7i627JtEVVentxrnf+VH3967/oiYp/GM/Xy+piw1JZWbl+5xeONZttyYjbFJ9q7NC3v4lIhHkczun3Z1sCpVBcBXlUez4OgYSjplfVHLoO+70blL60zrlC9fmmtX/yEsLP1FaBERUdjbF9exFZqLapTKJ5QqVSJtm/BI7F8dtZUsWZykpGSiomK1jl7DIyJp9EHtLOPPKfPCCub6fof3on6asbtl7auxW1w9douXsGLVttG8eJFEdGQ8DqVs8J68BUcn+1z9bju7YpiYmKQ7So0Ij8LBwTZXbRcE+dm3wnBylFxXr17NlStXMDY2plq1ajRt2pRTp04RFRVF06a6L7LJqyngrKhUKhITk7Osk5RFnbxkalqIUmXUX5Z7d57l40/qYvyfI9dlC/ay6Kdf2bR7ArXqVsiombfubfVtjUaVmHtwlFaZ75jtlHaxp8OAZtlOrAClK9ina3PbvEO8SEjkm0mfY1eqmF7t2NnZYGdnlWW9ho1qERMTx8WL16hfX72jceHCVWJi4jJMgs7OZShZ0o7jfv7UqVMVUJ9v/OOPAGZ6DwOgrls1TE0L4XfsPB6d1Uk+NDSC63/dYdaskXr9DbmhPXbPpRu7oD5fWLK0LcnJLzm09wJtOzbS1ZTeFApT6rpV5bjfBT7v2FxT7ufnT7v27rlquyDJj74VhpPt5Pr6vOuCBQto2rQpRkZGNG3aFG9vb6Kiohg2bJjBgouPf879u2mX1v/7IIxrV+5hY2NJ2XIlmDZxPaEhkfiuUZ/jXbXsAGUdS1CpcllAvTZzyYLd9P+uraaN+T/toHbdiji7lCIp+SXHDv/J9s2/M3fRd7mONyH+BQ/uKTWvg/8N5/rVBxSzsaCMox2zpmxBGRLJgpWDAbh3O4SggDvUqVeJ6OgEVi7ez62bwcxf8b2mDd/5e5g7fTuL1wylrJM94WHRABQtak5RC/Mcx/ou9W1hC3PKvae9nMassAJLmyKa8i1zDhAZFsPgud00dR7ceAzAi2eJxEbG8+DGYwqZmlC2UkkUZqbp2ixqVRggXbkhVK3qQuvWHzJwwDSW+k4C4LuB0/jssyZaVwq7VuvAjB+H8nnHjzEyMmLosO7M8l5NxYrlqFipHLO9V1OkiDldu7UBwNrakt59OvLDaB+KFy+Gja0VY36Yh2uNSpqrh/Wh19gNjWTBiv+O3bvUqVeRmOgEVi7Zz60bwcxbPkjTRuCl2yhDIqlWszzKkEjme+8kNVXFwOHtc9OVAAwb3p3evSbh5laVBo1qsnrlbwQ/VNJ/wJcATBi/mJDH4axdP12zTVCQ+mKf+PhnREREExR0C4XClGrV1EuwkpKSuXHjnub/Qx6HExR0CwuLwlSsWC7Hsb5rfSsMI9vJ9fV5102bNrFw4UJAnXA9PDxITk7Web41p4IC7tC+ddo6wIk/rAag69fN+XnVcMKUUTwKjtC8n5qqYtqkDTx8EIZJIROcXUoyZUZPzVIRgGcJiYwetoyQx08xL6ygUuWyLFvryRceH+U63quX79K5Tdp55WljNwDQqXtT5i//njBlFI+Dn2jeT0lJZcWi/dy9HYKpqQmNmlRnt98MramdDSuPkpT0kgFfz9P6XSPGdcJzQuccx/qu9W1WoiJieRISrVX2Q/u0Prv31yPO7A2kRBkbfj41Mc/j0WXDJm+GD5tFm08GAtC2XVMWLR6nVefWrQfExMRrXo8a3ZvnzxMZMngmUVGx1G9Qg4OHfbG0LKqp4zNvNIUKmdD1q9E8f55I8+b1Wb1nOiYmJnrHdjXwLl3aTNO8njbu1djt1pR5ywcRrowmJDhtGjYlJZWVi/8zdj+qzi6/6VpjNzExmTnTtxP8IJwiRc1p1ro2C1Z+j3WxtNhzqnOX1kRGxvDjjJWEhj6humsF9u5fhJOT+pSEMvQJwcFKrW3qu6WtYrgccJNtWw/h5FSK2/cOABASEqFVZ57PRub5bKRJUzf8TqzMcazvWt8KwzBSqVSq7G40atQofHx8+Ouvv6heXb3Monbt2oSEhBAWFpbtiwqiE4+gIim7YbwVRigoZqZ9Ti0kYQ+pBTReYxSULtpBq6yg9q+uvt1z35ek1LxZa5pbCuPCdHDWPgp/mXod0P9mDW+XCYWMtZdBPY7fQyqJGdTPP8aYUcZCe9wmp14DdC9Jy3+FMDXWvo6goPYt6O5fkbdydIemuXPnolKpNIkV1Gtew8PD/yeu1hNCCCFyo0Df/lAIIYR4F0lyFUIIIQxMkqsQQghhYJJchRBCCAOT5CqEEEIYmCRXIYQQwsAkuQohhBAGJslVCCGEMDBJrkIIIYSBSXIVQgghDEySqxBCCGFgklyFEEIIA8v2I+fyghGm+R1ChnTFZowiHyLRj67YCmr/6opLYZzzZ9TmNd2x6f9Yt7cvfWwFdezqjkv61lAKcmz/q3L0yDkhhBBCZEymhYUQQggDk+QqhBBCGJgkVyGEEMLAJLkKIYQQBibJVQghhDAwSa5CCCGEgUlyFUIIIQxMkqsQQghhYJJchRBCCAOT5CqEEEIYmCRXIYQQwsD+D1w5KPpJ0mKSAAAAAElFTkSuQmCC",
       "text/plain": [
-       "         0         1         2         3         4         5         6   \\\n",
-       "A       1.0  0.950000  0.902500  0.857375  0.814506  0.773781  0.735092   \n",
-       "C       1.0  0.994328  0.987598  0.979904  0.971334  0.961969  0.951887   \n",
-       "I       1.0  0.936704  0.876975  0.820622  0.767466  0.717334  0.670064   \n",
-       "K       1.0  0.999213  0.996976  0.993407  0.988616  0.982707  0.975777   \n",
-       "L       1.0  0.942414  0.887937  0.836407  0.787668  0.741576  0.697992   \n",
-       "Y       1.0  0.967306  0.935722  0.905210  0.875732  0.847250  0.819729   \n",
-       "lambda  1.0  0.994328  0.987598  0.979904  0.971334  0.961969  0.951887   \n",
-       "r       1.0  0.936485  0.876555  0.820018  0.766692  0.716405  0.668994   \n",
-       "w       1.0  0.978304  0.956837  0.935612  0.914644  0.893943  0.873519   \n",
-       "\n",
-       "              7         8         9   ...        20        21        22  \\\n",
-       "A       0.698337  0.663420  0.630249  ...  0.358486  0.340562  0.323534   \n",
-       "C       0.941158  0.929851  0.918027  ...  0.768105  0.753679  0.739249   \n",
-       "I       0.625503  0.583505  0.543930  ...  0.235620  0.216647  0.198840   \n",
-       "K       0.967916  0.959211  0.949739  ...  0.813244  0.799204  0.785059   \n",
-       "L       0.656783  0.617826  0.581002  ...  0.288388  0.269865  0.252392   \n",
-       "Y       0.793135  0.767435  0.742598  ...  0.518401  0.501843  0.485829   \n",
-       "lambda  0.941158  0.929851  0.918027  ...  0.768105  0.753679  0.739249   \n",
-       "r       0.624305  0.582190  0.542510  ...  0.233599  0.214608  0.196788   \n",
-       "w       0.853383  0.833541  0.814000  ...  0.620035  0.604344  0.588975   \n",
-       "\n",
-       "              23        24        25        26        27        28        29  \n",
-       "A       0.307357  0.291989  0.277390  0.263520  0.250344  0.237827  0.225936  \n",
-       "C       0.724836  0.710462  0.696145  0.681903  0.667750  0.653701  0.639770  \n",
-       "I       0.182134  0.166468  0.151784  0.138026  0.125143  0.113085  0.101806  \n",
-       "K       0.770841  0.756577  0.742291  0.728007  0.713747  0.699530  0.685374  \n",
-       "L       0.235914  0.220376  0.205728  0.191923  0.178914  0.166658  0.155116  \n",
-       "Y       0.470341  0.455361  0.440872  0.426857  0.413299  0.400184  0.387497  \n",
-       "lambda  0.724836  0.710462  0.696145  0.681903  0.667750  0.653701  0.639770  \n",
-       "r       0.180074  0.164403  0.149717  0.135962  0.123083  0.111033  0.099764  \n",
-       "w       0.573925  0.559192  0.544772  0.530665  0.516865  0.503370  0.490176  \n",
-       "\n",
-       "[9 rows x 30 columns]"
+       "
"
       ]
      },
-     "execution_count": 17,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "acorr_matrix"
+    "gp.plot_covariance_matrix(\n",
+    "    sigma,\n",
+    "    [\"A\", \"K\", \"C\", \"I\", \"L\", \"Y\", \"r\", \"w\"],\n",
+    "    figsize=(5, 5),\n",
+    "    cbar_kw=dict(shrink=0.5),\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "604fe2dc",
+   "metadata": {},
+   "source": [
+    "Similarly, there is a function to plot the autocorrelation functions, `plot_acf`. This only plots self-autocorrelations. For the off-diagonals, you will need to hand-roll something."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "id": "6b63ce7e",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "
"
       ]
@@ -1302,9 +2248,7 @@
     }
    ],
    "source": [
-    "gp.plot_acf(\n",
-    "    acorr_matrix, vars_to_plot=[\"A\", \"K\", \"C\", \"I\", \"L\", \"Y\", \"r\", \"w\"], n_cols=3\n",
-    ");"
+    "gp.plot_acf(acorr, vars_to_plot=[\"A\", \"K\", \"C\", \"I\", \"L\", \"Y\", \"r\", \"w\"], n_cols=3);"
    ]
   },
   {
@@ -1319,23 +2263,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "id": "f9d6c675",
    "metadata": {},
    "outputs": [],
    "source": [
-    "simulation = model.simulate(shock_cov_matrix=np.eye(1) * 0.01, n_simulations=100)"
+    "simulation = ge.simulate(model, T, R, shock_cov_matrix=cov, n_simulations=100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "id": "7cf6f12c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "
"
       ]
@@ -1360,13 +2304,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 25,
    "id": "b19ccf67",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1376,9 +2320,10 @@
     }
    ],
    "source": [
-    "simulation = model.simulate(\n",
-    "    shock_cov_matrix=np.eye(1) * 0.01, n_simulations=10_000, simulation_length=100\n",
+    "simulation = ge.simulate(\n",
+    "    model, T, R, shock_cov_matrix=cov, n_simulations=100_000, simulation_length=100\n",
     ")\n",
+    "\n",
     "gp.plot_simulation(\n",
     "    simulation,\n",
     "    vars_to_plot=[\"Y\", \"C\", \"I\", \"K\", \"w\", \"r\"],\n",
@@ -1398,7 +2343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 26,
    "id": "b239cab7",
    "metadata": {},
    "outputs": [
@@ -1407,17 +2352,16 @@
      "output_type": "stream",
      "text": [
       "Absolute difference between stationary covariance matrix and sample covariance matrix is less than:\n",
-      "           0.10000  0.01000  0.00100  0.00010  0.00001\n",
-      "Variables                                             \n",
-      "A             True     True    False    False    False\n",
-      "C             True     True    False    False    False\n",
-      "I             True    False    False    False    False\n",
-      "K             True     True    False    False    False\n",
-      "L             True     True    False    False    False\n",
-      "Y             True    False    False    False    False\n",
-      "lambda        True     True    False    False    False\n",
-      "r             True     True    False    False    False\n",
-      "w             True     True    False    False    False\n"
+      "        0.10000  0.01000  0.00100  0.00010  0.00001\n",
+      "A          True    False    False    False    False\n",
+      "C          True    False    False    False    False\n",
+      "I          True    False    False    False    False\n",
+      "K          True    False    False    False    False\n",
+      "L          True     True    False    False    False\n",
+      "Y          True    False    False    False    False\n",
+      "lambda     True    False    False    False    False\n",
+      "r          True    False    False    False    False\n",
+      "w          True    False    False    False    False\n"
      ]
     }
    ],
@@ -1425,10 +2369,12 @@
     "import pandas as pd\n",
     "\n",
     "tols = [1e-1, 1e-2, 1e-3, 1e-4, 1e-5]\n",
-    "accuracy_df = pd.DataFrame(0, columns=tols, index=simulation.index)\n",
+    "accuracy_df = pd.DataFrame(\n",
+    "    0, columns=tols, index=[x.base_name for x in model.variables]\n",
+    ")\n",
     "for tol in tols:\n",
     "    accuracy_df[tol] = (\n",
-    "        (simulation.xs(axis=1, key=99).T.cov() - sigma).abs() < tol\n",
+    "        (np.cov(simulation.isel(time=-1).values.T) - sigma).abs() < tol\n",
     "    ).all()\n",
     "print(\n",
     "    \"Absolute difference between stationary covariance matrix and sample covariance matrix is less than:\"\n",
@@ -1454,13 +2400,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "id": "a6aa1c3a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1470,7 +2416,7 @@
     }
    ],
    "source": [
-    "irf = model.impulse_response_function()\n",
+    "irf = ge.impulse_response_function(model, T=T, R=R, shock_size={\"epsilon_A\": 1.0})\n",
     "gp.plot_irf(\n",
     "    irf,\n",
     "    vars_to_plot=[\"Y\", \"C\", \"I\", \"K\", \"w\", \"r\"],\n",
@@ -1492,12 +2438,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "id": "e215f667",
    "metadata": {
     "scrolled": false
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You provided a function to compute the full hessian, but method trust-ncg allows the use of a hessian-vector product instead. Consider passing hessp instead -- this may be significantly more efficient.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5e028228b1884d509b9dfc82edc02969",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Steady state IS found, although optimizer returned success = False.\n",
+      "This can be ignored, but to silence this message, try reducing the solver-specific tolerance, or use a different solution algorithm.\n",
+      "--------------------------------------------------------------------------------\n",
+      "Optimizer message             A bad approximation caused failure to predict improvement.\n",
+      "Sum of squared residuals      7.817537743289768e-29\n",
+      "Maximum absoluate error       8.552207923778995e-15\n",
+      "Gradient L2-norm at solution  5.825605443052315e-16\n",
+      "Max abs gradient at solution  5.620504062164855e-16\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -1571,14 +2575,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 29,
    "id": "84ff5f51",
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
       "Model Building Complete.\n",
@@ -1593,16 +2597,15 @@
       "\t\t 0 / 1 has a defined prior. \n",
       "\t6 parameters\n",
       "\t\t 0 / 6 has a defined prior. \n",
-      "\t0 calibrating equations\n",
-      "\t0 parameters to calibrate\n",
-      " Model appears well defined and ready to proceed to solving.\n",
+      "\t0 parameters to calibrate.\n",
+      "Model appears well defined and ready to proceed to solving.\n",
       "\n"
      ]
     }
    ],
    "source": [
     "file_path = \"../GCN Files/RBC_steady_state.gcn\"\n",
-    "model = ge.gEconModel(file_path, verbose=True)"
+    "model = ge.model_from_gcn(file_path, verbose=True)"
    ]
   },
   {
@@ -1623,7 +2626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "id": "2e68ba2c",
    "metadata": {},
    "outputs": [
@@ -1642,58 +2645,10 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle C_{ss} = \\left(\\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right)\\right)^{\\sigma_{L} + 1} \\left(1 - \\alpha\\right)^{- \\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C}}} \\left(\\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{\\frac{\\left(-1\\right) \\sigma_{L}}{\\sigma_{C}}}$"
-      ],
-      "text/plain": [
-       "Eq(C_ss, (((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha))**(sigma_L + 1)/(1 - alpha)**sigma_L)**(1/sigma_C)*(((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L)))**((-sigma_L)/sigma_C))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle I_{ss} = \\frac{\\alpha \\delta \\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}$"
-      ],
-      "text/plain": [
-       "Eq(I_ss, alpha*delta*((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L))/(-(1 - delta) + 1/beta))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle K_{ss} = \\frac{\\alpha \\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}$"
-      ],
-      "text/plain": [
-       "Eq(K_ss, alpha*((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L))/(-(1 - delta) + 1/beta))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle L_{ss} = \\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{- \\frac{\\alpha}{1 - \\alpha}} \\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}$"
-      ],
-      "text/plain": [
-       "Eq(L_ss, ((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L))/(alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/latex": [
-       "$\\displaystyle P_{ss} = 1.0$"
+       "$\\displaystyle C_{ss} = - \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} \\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} \\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{\\alpha} \\left(\\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{1 \\cdot \\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{1 - \\alpha}$"
       ],
       "text/plain": [
-       "Eq(P_ss, 1.0)"
+       "Eq(C_ss, -delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha))*((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha))*((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))**alpha*(((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))**(1 - alpha))"
       ]
      },
      "metadata": {},
@@ -1702,10 +2657,10 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle TC_{ss} = - \\alpha \\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}} - \\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}} \\left(1 - \\alpha\\right)$"
+       "$\\displaystyle I_{ss} = \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} \\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}$"
       ],
       "text/plain": [
-       "Eq(TC_ss, -alpha*((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L)) - ((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L))*(1 - alpha))"
+       "Eq(I_ss, delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha))*((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))"
       ]
      },
      "metadata": {},
@@ -1714,10 +2669,10 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle U_{ss} = \\frac{\\frac{\\left(\\left(\\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right)\\right)^{\\sigma_{L} + 1} \\left(1 - \\alpha\\right)^{- \\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C}}} \\left(\\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{\\frac{\\left(-1\\right) \\sigma_{L}}{\\sigma_{C}}}\\right)^{1 - \\sigma_{C}}}{1 - \\sigma_{C}} - \\frac{\\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{- \\frac{\\alpha}{1 - \\alpha}} \\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{\\sigma_{L} + 1}}{\\sigma_{L} + 1}}{1 - \\beta}$"
+       "$\\displaystyle K_{ss} = \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} \\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}$"
       ],
       "text/plain": [
-       "Eq(U_ss, (((((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha))**(sigma_L + 1)/(1 - alpha)**sigma_L)**(1/sigma_C)*(((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L)))**((-sigma_L)/sigma_C))**(1 - sigma_C)/(1 - sigma_C) - (((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L))/(alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**(sigma_L + 1)/(sigma_L + 1))/(1 - beta))"
+       "Eq(K_ss, (alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha))*((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))"
       ]
      },
      "metadata": {},
@@ -1726,10 +2681,10 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle Y_{ss} = \\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}$"
+       "$\\displaystyle L_{ss} = \\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{1 \\cdot \\frac{1}{\\sigma_{C} + \\sigma_{L}}}$"
       ],
       "text/plain": [
-       "Eq(Y_ss, ((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L)))"
+       "Eq(L_ss, ((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))"
       ]
      },
      "metadata": {},
@@ -1738,10 +2693,10 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\lambda_{ss} = \\left(\\left(\\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right)\\right)^{\\sigma_{L} + 1} \\left(1 - \\alpha\\right)^{- \\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C}}} \\left(\\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{\\frac{\\left(-1\\right) \\sigma_{L}}{\\sigma_{C}}}\\right)^{- \\sigma_{C}}$"
+       "$\\displaystyle Y_{ss} = \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} \\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{\\alpha} \\left(\\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{1 \\cdot \\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{1 - \\alpha}$"
       ],
       "text/plain": [
-       "Eq(lambda_ss, ((((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha))**(sigma_L + 1)/(1 - alpha)**sigma_L)**(1/sigma_C)*(((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L)))**((-sigma_L)/sigma_C))**(-sigma_C))"
+       "Eq(Y_ss, ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha))*((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))**alpha*(((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))**(1 - alpha))"
       ]
      },
      "metadata": {},
@@ -1750,10 +2705,10 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle q_{ss} = \\left(\\left(\\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right)\\right)^{\\sigma_{L} + 1} \\left(1 - \\alpha\\right)^{- \\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C}}} \\left(\\left(\\frac{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}{- \\alpha \\delta - \\left(1 - \\delta\\right) + \\frac{1}{\\beta}}\\right)^{\\frac{\\sigma_{C}}{\\sigma_{C} + \\sigma_{L}}} \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}}\\right)^{\\sigma_{L}}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{\\frac{\\left(-1\\right) \\sigma_{L}}{\\sigma_{C}}}\\right)^{- \\sigma_{C}}$"
+       "$\\displaystyle \\lambda_{ss} = \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} \\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} \\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{\\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{\\alpha} \\left(\\left(\\left(1 - \\alpha\\right) \\left(- \\delta \\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{\\frac{1}{1 - \\alpha}} + \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{- \\sigma_{C}} \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}\\right)^{1 \\cdot \\frac{1}{\\sigma_{C} + \\sigma_{L}}}\\right)^{1 - \\alpha}\\right)^{- \\sigma_{C}}$"
       ],
       "text/plain": [
-       "Eq(q_ss, ((((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha))**(sigma_L + 1)/(1 - alpha)**sigma_L)**(1/sigma_C)*(((-(1 - delta) + 1/beta)/(-alpha*delta - (1 - delta) + 1/beta))**(sigma_C/(sigma_C + sigma_L))*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha)*((alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha)))**sigma_L)**(1/(sigma_C + sigma_L)))**((-sigma_L)/sigma_C))**(-sigma_C))"
+       "Eq(lambda_ss, (-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha))*((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha))*((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))**alpha*(((1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha/(-delta*(alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)) + ((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)**sigma_C)**(1/(sigma_C + sigma_L)))**(1 - alpha))**(-sigma_C))"
       ]
      },
      "metadata": {},
@@ -1774,10 +2729,10 @@
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle w_{ss} = \\left(\\frac{\\alpha}{- (1 - \\delta) + 1 \\cdot \\frac{1}{\\beta}}\\right)^{\\frac{\\alpha}{1 - \\alpha}} \\left(1 - \\alpha\\right)$"
+       "$\\displaystyle w_{ss} = \\left(1 - \\alpha\\right) \\left(\\left(\\frac{\\alpha \\beta}{1 - \\beta \\left(1 - \\delta\\right)}\\right)^{1 \\cdot \\frac{1}{1 - \\alpha}}\\right)^{\\alpha}$"
       ],
       "text/plain": [
-       "Eq(w_ss, (alpha/(-(1 - delta) + 1/beta))**(alpha/(1 - alpha))*(1 - alpha))"
+       "Eq(w_ss, (1 - alpha)*((alpha*beta/(1 - beta*(1 - delta)))**(1/(1 - alpha)))**alpha)"
       ]
      },
      "metadata": {},
@@ -1787,9 +2742,8 @@
    "source": [
     "from gEconpy.classes.time_aware_symbol import TimeAwareSymbol\n",
     "\n",
-    "for var, eq in model.steady_state_relationships.items():\n",
-    "    sp_var = TimeAwareSymbol(var.split(\"_\")[0], time_index=\"ss\")\n",
-    "    display(sp.Eq(sp_var, eq))"
+    "for eq in model.steady_state_relationships:\n",
+    "    display(eq)"
    ]
   },
   {
@@ -1804,17 +2758,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "id": "4194848a",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Steady state found! Sum of squared residuals is 1.140814196572275e-28\n",
-      "CPU times: user 974 ms, sys: 11.2 ms, total: 985 ms\n",
-      "Wall time: 987 ms\n",
       "A_ss               1.000\n",
       "C_ss               2.358\n",
       "I_ss               0.715\n",
@@ -1825,39 +2776,18 @@
       "r_ss               0.030\n",
       "w_ss               2.436\n"
      ]
-    }
-   ],
-   "source": [
-    "%time model.steady_state()\n",
-    "model.print_steady_state()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f448fab9",
-   "metadata": {},
-   "source": [
-    "Nevertheless, you still get additonal speedup after the solution function is complied in the first execution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "9e485406",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Steady state found! Sum of squared residuals is 1.140814196572275e-28\n",
-      "CPU times: user 334 μs, sys: 3 μs, total: 337 μs\n",
-      "Wall time: 337 μs\n"
+      "66.7 ms ± 1.37 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
    "source": [
-    "%time model.steady_state()"
+    "%timeit ss_res = model.steady_state()\n",
+    "ge.print_steady_state(ss_res)"
    ]
   },
   {
@@ -1872,27 +2802,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "id": "f82b2d46",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Solution found, sum of squared residuals:  2.425259481224941e-31\n",
+      "Solution found, sum of squared residuals: 0.000000000\n",
       "Norm of deterministic part: 0.000000000\n",
       "Norm of stochastic part:    0.000000000\n"
      ]
     }
    ],
    "source": [
-    "model.solve_model()"
+    "T, R = model.solve_model()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "id": "31992db2",
    "metadata": {},
    "outputs": [
@@ -1900,46 +2830,274 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "====================    T     ====================\n",
-      "               A    C    I             K    L    Y  lambda    r    w\n",
-      "A       0.950000 -0.0 -0.0 -8.203114e-17 -0.0 -0.0    -0.0 -0.0 -0.0\n",
-      "C       0.309657  0.0  0.0  4.787472e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "I       3.640697 -0.0 -0.0 -5.127277e-01 -0.0 -0.0    -0.0 -0.0 -0.0\n",
-      "K       0.072814 -0.0 -0.0  9.697454e-01 -0.0 -0.0    -0.0 -0.0 -0.0\n",
-      "L       0.206602  0.0  0.0 -1.566471e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "Y       1.084291  0.0  0.0  2.481794e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "lambda -0.464485  0.0  0.0 -7.181208e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "r       1.084291  0.0  0.0 -7.518206e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "w       0.877689  0.0  0.0  4.048265e-01  0.0  0.0     0.0  0.0  0.0\n",
-      "====================    R     ====================\n",
-      "        epsilon_A\n",
-      "A        1.000000\n",
-      "C        0.325955\n",
-      "I        3.832313\n",
-      "K        0.076646\n",
-      "L        0.217476\n",
-      "Y        1.141359\n",
-      "lambda  -0.488932\n",
-      "r        1.141359\n",
-      "w        0.923883\n"
+      "====================    T     ====================\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "      \n",
+       "      \n",
+       "      \n",
+       "    \n",
+       "  \n",
+       "
ACIKLYlambdarw
A0.950000-0.0-0.0-0.000000-0.0-0.0-0.0-0.0-0.0
C0.3096570.00.00.4787470.00.00.00.00.0
I3.640697-0.0-0.0-0.512728-0.0-0.0-0.0-0.0-0.0
K0.072814-0.0-0.00.969745-0.0-0.0-0.0-0.0-0.0
L0.2066020.00.0-0.1566470.00.00.00.00.0
Y1.0842910.00.00.2481790.00.00.00.00.0
lambda-0.4644850.00.0-0.7181210.00.00.00.00.0
r1.0842910.00.0-0.7518210.00.00.00.00.0
w0.8776890.00.00.4048260.00.00.00.00.0
\n",
+       "
"
+      ],
+      "text/plain": [
+       "               A    C    I         K    L    Y  lambda    r    w\n",
+       "A       0.950000 -0.0 -0.0 -0.000000 -0.0 -0.0    -0.0 -0.0 -0.0\n",
+       "C       0.309657  0.0  0.0  0.478747  0.0  0.0     0.0  0.0  0.0\n",
+       "I       3.640697 -0.0 -0.0 -0.512728 -0.0 -0.0    -0.0 -0.0 -0.0\n",
+       "K       0.072814 -0.0 -0.0  0.969745 -0.0 -0.0    -0.0 -0.0 -0.0\n",
+       "L       0.206602  0.0  0.0 -0.156647  0.0  0.0     0.0  0.0  0.0\n",
+       "Y       1.084291  0.0  0.0  0.248179  0.0  0.0     0.0  0.0  0.0\n",
+       "lambda -0.464485  0.0  0.0 -0.718121  0.0  0.0     0.0  0.0  0.0\n",
+       "r       1.084291  0.0  0.0 -0.751821  0.0  0.0     0.0  0.0  0.0\n",
+       "w       0.877689  0.0  0.0  0.404826  0.0  0.0     0.0  0.0  0.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "====================    R     ====================\n"
      ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
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+       "      \n",
+       "    \n",
+       "  \n",
+       "
epsilon_A
A1.000000
C0.325955
I3.832313
K0.076646
L0.217476
Y1.141359
lambda-0.488932
r1.141359
w0.923883
\n",
+       "
"
+      ],
+      "text/plain": [
+       "        epsilon_A\n",
+       "A        1.000000\n",
+       "C        0.325955\n",
+       "I        3.832313\n",
+       "K        0.076646\n",
+       "L        0.217476\n",
+       "Y        1.141359\n",
+       "lambda  -0.488932\n",
+       "r        1.141359\n",
+       "w        0.923883"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "for name, policy_matrix in zip([\"T\", \"R\"], [model.T, model.R]):\n",
+    "for name, policy_matrix in zip([\"T\", \"R\"], [T, R]):\n",
     "    print(name.center(10).center(50, \"=\"))\n",
-    "    print(policy_matrix.to_string())"
+    "    display(ge.matrix_to_dataframe(policy_matrix, model))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 34,
    "id": "8a163b29",
    "metadata": {},
    "outputs": [
     {
      "data": {
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9+nQ+/fRTRo0axQsvvEBsbCzLly9nzpw53HvvvY0mqJEjRxIYGMjUqVN54YUXcHFxYf78+Zw8ebJO2y5durBw4UIWLVpEu3btMBqNdOnSpd5+Z8+ezZAhQxg4cCCPPfYYbm5uzJkzhz179rBgwQKrHiWS2AnHjkOSaI3GRs8C4sMPP1TaXjx6Vggh9uzZIwYPHiyMRqMIDAwUU6dOFZ988okAxK5duyzarlu3TowaNUoEBgYKV1dX0aZNGzFq1Cjx9ddfK23MoxHPnz9fr2ft0Yhm+vXrJwBx66231ruN8+bNE/Hx8cLd3V20a9dOzJ49W8ydO7dOf/WNyjxz5oy48cYbRWBgoPDz8xO33Xab2Lp1a53RjkIIsWvXLjF+/HgRGhoqXF1dRXh4uLjmmmvEe++9p7R56qmnRGpqqggICFB8pk+fLrKzs+t1r81ff/0lxowZI/z8/ISbm5vo1q1bHQchWjZ6tjEuHj1r5qOPPhJdu3YVbm5uws/PT1x33XV1RlKb23Xs2FG4ubmJTp06iXnz5onJkydbjJ4VQoiKigrx2muviW7dugmj0Si8vb1FQkKC+Nvf/iYOHTqktLNm9GxeXp546aWXxDXXXCPatGkj3NzchJeXl+jevbt46aWXRGlpqUX748ePi4kTJ4qgoCDh6uoq4uPjxauvvqqMeq4dr9qjZ4UQYvPmzaJfv37Cy8tLtGnTRsyYMUN89NFHdWKWkZEhhg4dKnx8fASgbH99o2eFEOL3338X11xzjfDy8hIeHh6iT58+4vvvv7doY/5cbNmyxWJ5QyO/Jc3HIEQTT3RLJDZy9913s2DBAnJyclp02VUikUi0grw8K7ErL7zwApGRkbRr147i4mJ++OEHPvroI/7xj3/IhCmRSHSPTJoSu+Lq6sqrr75KZmYmlZWVdOzYkddff52HHnrI0WoSiURiM/LyrEQikUgkViIfOZFIJBKJxEouy6RZVFTEE088wdChQwkJCWl2IVlzJYOGXrUng7alSK0euBQLUV9c1Li0tJSZM2fWO3WbeV9o6cPrU6ZMqXdqOmv59ddfSU1NxcvLC4PBwLJly+pU1GgO9VXYWL9+PTNnziQ/P7/FnvUxZcoUDAYDPj4+9T5Defz4cZycnOz+GWqoWo01XCoFovft28fMmTPtWkh8wIABdO7c2W79aZXL8p5mTk4OH3zwAd26dWPs2LF89NFHLepnxYoV9T4PV3u6qw0bNhAVFdViV0nrM2fOHIufS0tLlRqMtZOpoxFCMH78eDp16sR3332Hl5cX8fHxVFZWsmHDhgbLYDVGfQWQ169fz6xZs5gyZQr+/v523IKae+CVlZUsWrSozjR9H3/8MT4+ProseK119u3bx6xZsxgwYECTRcklllyWSTM2Npa8vDzlDKGlSTMlJaXJ6atkMVn9cfGE81rl9OnT5ObmMm7cOGXWHTMNTUDRFK1dANnNzY0xY8Ywb948i6QphGD+/PlMmDBBdwWvJZc2l+XlWWuL8drrvS6+tPTHH3/Qt29fjEYjbdq04bnnnuOjjz6q95LaokWL6Nu3L15eXnh7ezNs2DB27Nhh0cZ8ie/w4cOMHDkSb29voqOjefTRR5V5UisqKggNDWXSpEl1HPPz8/Hw8OCRRx4BwGQy8eijj9K9e3f8/PwIDAykb9++fPvtt01ub0OXBhu6JGZNIerz589z9913Ex0drRQjvuKKK1i1alWDHnv37sVgMCgz7wBs27YNg8FAcnKyRdtrr73WYjL62pdnMzIylAQ0a9YsZd+5+JL02bNnGy2S3Rzi4uIYPXo0K1asoGfPnnh4eJCQkGBRFHrmzJnKFYwnn3zSoohxfX8D86WzLVu2cNVVV+Hp6Um7du145ZVXLAorX3z5cebMmTz++OMAtG3bVtn+tWvXMnXqVAIDA+ud7u+aa66pE+eGuPPOO1m/fr1FpY5Vq1Zx/PhxizqotWmqALeZ/fv3M3z4cDw9PQkODuaee+6hqKioTruGbjNcfKm+PqZMmVLv2Vp9Bam//vprevfujZ+fn/I3uHiqx/owGAzcf//9fPbZZyQmJuLp6Um3bt344Ycf6rRtqlj1/PnzuemmmwAYOHCg8jdt7JJzcz6DTe1jYF2R7169ejFq1CiL9bp06YLBYGDLli3KsiVLlmAwGPjrr78A6wvXt5TLMmnai5YUat69ezdDhgyhtLSUTz75hPfee4/t27fzz3/+s07bl19+mVtuuYWkpCS++uorPvvsM4qKirjqqqvqzO9aUVHBtddey6BBg/j222+58847eeONN/jXv/4F1FwGu+2221i8eHGdy10LFizAZDIpB6iysjJyc3N57LHHWLZsGQsWLODKK6/k+uuv59NPP7UlZBZYW4jaPJ/s888/zy+//MJHH33E4MGDG602kZycTEREhMWHetWqVXh4eLBv3z5lztbKykrWrVvH4MGD6+0nIiJCmTR86tSpSsHgi+dibapIdnPZtWsXjz76KNOnT+fbb7+la9euTJ06ld9++w2oKTJsnr7vgQceaLKIMdRMin7rrbdy22238d133zFixAiefvrpOnPG1uauu+7igQceAGoOTubt79mzJw899BB5eXl8+eWXFuvs27ePNWvW8Pe//92qbR08eDCxsbEWXwrmzp3L1VdfTceOHeu0t6YAN9R8kTHPhzxnzhw+++wziouL7VZIurls2LCBCRMm0K5dOxYuXMjy5ct5/vnn652usj6WL1/O//3f//HCCy+wePFiAgMDGTduHEePHlXaWFOsetSoUbz88ssAvPPOO8rf9OIEVRtrP4PW7GPWFvkePHgwv/32GxUVFUDN33PPnj14eHgolYyg5nMdFhZWZwpCe38mFRw5HZEWOH/+fL1TYTWGeWq3+l7t27e3aHtx3zfddJPw8vKymBauqqpKJCUlWUyzdeLECeHi4iIeeOABi/6KiopEeHi4GD9+vLJs8uTJAhBfffWVRduRI0eK+Ph45efdu3cLQHzwwQcW7dLS0kRKSkqD21tZWSkqKirE1KlTRY8ePSx+d/FUeg1Nb3fxNF7NKUTt7e0tHn744Qb9GuK2224T7dq1U34ePHiwmDZtmggICBCffPKJEEKIP//8UwDil19+UdpdPC1bY/uItUWyG2Ly5MnCy8vLYllsbKwwGo3i+PHjyrILFy6IwMBA8be//U1ZZp5u7dVXX7VYv76/Qf/+/estrJyUlCSGDRtWp8/aU7g1NG2eud/u3btbLLv33nuFr6+vRcHkprZ9xowZIjw8XFRUVIicnBzh7u4u5s+fX2/srS3A/eSTTwqDwSB27txp0W7IkCF1ppSrb0pI8/bV3hfqi099UwCat6n2Ifa1114TgEWBcGsBRFhYmCgsLFSWZWVlCScnJzF79mxlmbXFqr/++utmTatnzWfQ2n3M2iLfq1atEoD47bffhBBCfP7558LHx0fcd999YuDAgcp6HTt2VAqdC2H7Z7Ip5JmmDaxatapOoeamSkyZvwnWvhfq5OTE+PHjLdr9/PPPVFZWcvvtt1ucyRqNRvr371/nMqfBYGDMmDEWy7p27WpRdLZLly6kpKTw8ccfK8vS09PZvHlznUtEX3/9NVdccQXe3t64uLjg6urK3LlzlYoQttKcQtRpaWnMnz+fl156iY0bNyrfPJti0KBBHD16lGPHjmEymfjjjz8YPnw4AwcOVL6prlq1Cnd3d6688kqbtqepItnNpXv37sTExCg/G41GOnXqZFMR4fDw8DqFlS/eR5rLQw89xM6dO/nzzz8BKCws5LPPPmPy5MnNGhV8xx13KKXRvvjiC9zc3JRLiBdjbQHuNWvWkJycTLdu3SzaTZw4sTmbaDd69eoFwPjx4/nqq684depUs9YfOHCgRXWcsLAwQkNDlb9fS4pVW4u1n0Fr9jFri3xfccUVGI1G5WrRypUrGTBgAMOHD2f9+vWUlpZy8uRJDh06VO+VInt/Js3IpGkD3bp1q1OyqKkh180t1NyrV686RXgXLVpU5xEHT09Pi/qQUDOow2QyWSy788472bBhg1J55OOPP8bd3Z1bbrlFabNkyRLGjx9PmzZt+Pzzz9mwYQNbtmzhzjvvrNNfS2lOIepFixYxefJkPvroI/r27UtgYCC33357vXUZa2P+IK1atYo//viDiooKrrnmGgYPHqxc/l21ahVXXHEFHh4eNm2PvYv+XtyfuU9bigir0ed1111HXFwc77zzDlBzv6ykpMTqS7NmYmNjGTRoEPPmzWPevHncfPPNDVZ5sbZIdE5OjlUFr1uLq6++mmXLlilfhqOioujcuTMLFiywav2m/n4tKVZtLdZ+Bq3Zx6z9+xmNRov7pr/++itDhgxhwIABVFVV8fvvvytffutLmmoV4pZJs5VpbqHmb775ps7Z7JYtW9i0aVOL3v+WW27B3d2d+fPnU1VVxWeffcbYsWMJCAhQ2nz++ee0bduWRYsWMXbsWPr06UNqamqd4sv10dxCzW+//Xa927dlyxbli0RwcDBvvvkmGRkZHD9+nNmzZ7NkyZImnw811xlctWoVK1euJDU1FX9/fwYNGsSZM2fYtGkTGzdubPB+pqRpnJyc+Pvf/84333zDmTNnmDNnDoMGDWp2vUuo+UL33XffsXPnzkYHx1hbJDooKMiqgtdQs9/Wt39b8/xtc9a97rrr+PXXXykoKGDt2rVERUUxceJE5ezYFtQsVt3Sz2B9NKfI96BBg9i8eTObN28mMzOTIUOG4OPjQ69evVi5ciWrVq2iU6dOda46qIlMmq1M//79Wb16tcUHqrq62mKUJ8CwYcNwcXHhyJEjDRYybgkBAQGMHTuWTz/9lB9++IGsrKx6CzW7ublZjPzLysqyavSstYWaW1qIOiYmhvvvv58hQ4bUKRJcH4MHD2b16tWsXLmSIUOGANCpUydiYmJ4/vnnqaioaDJp2usbql5pavvvuusu3NzcuPXWWzlw4ECLB9qMGzeOcePGceeddzb62Iu1BbgHDhzI3r172bVrl0W7iwcuQc1+e/E+e/DgQasuZ8bFxXHu3DmLL8Pl5eX8/PPPDa7j7u5O//79lYF6F4+IbwnNKVZtyz7d3M/gxTSnyPfgwYOprKzkueeeIyoqioSEBGX5qlWrWL16dat/6b0sn9ME+OmnnygpKVGGn+/bt49vvvkGqCkk21gBYDPbtm2rd3KDpKQkfH19613n2Wef5fvvv2fQoEE8++yzeHh48N577yn378yFmuPi4njhhRd49tlnOXr0KMOHDycgIICzZ8+yefNmvLy8lAfum8udd97JokWLuP/++4mKiqqz040ePZolS5Zw3333ceONN3Ly5ElefPFFIiIiOHToUKN99+rVi/j4eB577DEqKysJCAhg6dKl/PHHHxbtrC1EXVBQwMCBA5k4cSIJCQn4+PiwZcsWVqxYwfXXX9/ktg4aNIg5c+aQnZ3Nm2++abH8448/JiAgwOJxk/rw8fEhNjaWb7/9lkGDBhEYGEhwcPBl81C4eVTiW2+9xeTJk3F1dSU+Pl65v+bv78/tt9/Ou+++S2xsbJ1769ZiNBqVz2BjWFuA21wUfdSoUbz00kuEhYXxxRdfKLcmajNp0iRuu+027rvvPm644QaOHz/Ov//9b6ued50wYQLPP/88N998M48//jgmk4n//ve/dUbSP//882RmZjJo0CCioqLIz8/nrbfewtXVlf79+1sZpcaxtli1+TbSBx98gI+PD0ajkbZt29Z7edXWz+DFNKfId0pKCgEBAfzyyy8Wjx8NHjyYF198Ufl/Sxk0aBDr1q2zegQzcPmOno2NjW1wBGx9owRr09joWUCsXLlSaUs9oy5///130bt3b+Hu7i7Cw8PF448/Lv71r3/VO7Ju2bJlYuDAgcLX11e4u7uL2NhYceONN4pVq1YpbeobgVnb82KqqqpEdHS0AMSzzz5b7za+8sorIi4uTri7u4vExETx4Ycf1ttffaMODx48KIYOHSp8fX1FSEiIeOCBB8Ty5cvrHa3XVCFqk8kk7rnnHtG1a1fh6+srPDw8RHx8vJgxY4YoKSmp1702eXl5wsnJSXh5eYny8nJl+RdffCEAcf3119dZp76ixqtWrRI9evQQ7u7uAlC2uSVFsmvT0OjZUaNGNenV3NGzycnJ9b5/7ZGfDRVAfvrpp0VkZKRwcnKq9++4du1aAYhXXnml0e29+L3r229r09DIZWsLcO/bt08MGTLEoij6t99+W2cbqqurxb///W/Rrl07YTQaRWpqqli9erVVo2eFEOLHH38U3bt3Fx4eHqJdu3bi//7v/+p8Xn744QcxYsQIpQh2aGioGDlypPj999+bjBUNFAev7/NnTbFqIYR48803Rdu2bYWzs3O922TG2s+gtfuYENYX+RZCiHHjxglAfPHFF8qy8vJy4eXlJZycnEReXp5F++Z8Js0jfpuDrHKiEYYOHUpGRgYHDx50tIpE0mweffRR3n33XU6ePFnv2YpEcqlw2V6edSSPPPIIPXr0IDo6mtzcXL744gtWrlzJ3LlzHa0mkTSLjRs3cvDgQebMmcPf/vY3mTAllzwyaTqAqqoqnn/+ebKysjAYDCQlJfHZZ59x2223OVpNImkWffv2xdPTk9GjR/PSSy85WkciUR15eVYikUgkEiuRj5xIJBKJRGIlMmlKJBKJRGIlMmlKJBKJRGIlMmlKJBKJRGIlMmlKJBKJRGIlMmlKJBKJRGIlMmnaSHNr4jkS6aoOenIFfflKV3WQri1HJk0byczMdLSC1UhXddCTK+jLV7qqg3RtOTJpSiQSiURiJXJGIBupqKjA1dXV0RpWIV3VQU+uoC9f6aoO0rXlyDNNG6mvNp9Wka7qoCdX0JevdFUH6dpyZNK0kdLSUkcrWI10VQc9uYK+fKWrOkjXliOTpo14e3s7WsFqpKs66MkV9OUrXdVBurYceU/TRkwmE0aj0dEaViFd1UFPrqAvX+mqDtK15cgzTRvZtWuXoxWsRrqqg55cQV++0lUdpGvLkUlTIpFIJBIrkUnTRmJiYhytYDXSVR305Ar68pWu6iBdW46qSfO3335jzJgxREZGYjAYWLZsWZPrrFu3jpSUFIxGI+3ateO9996r02bx4sUkJSXh7u5OUlISS5cuVcFeIpFIJBJLVE2aJSUldOvWjf/7v/+zqv2xY8cYOXIkV111FTt27OCZZ57hwQcfZPHixUqbDRs2MGHCBCZNmsSuXbuYNGkS48ePZ9OmTWptRqOcOHHCIe/bEqSrOujJFfTlK13VQbq2HBc1Ox8xYgQjRoywuv17771HTEwMb775JgCJiYls3bqV1157jRtuuAGAN998kyFDhvD0008D8PTTT7Nu3TrefPNNFixYYPdtkEgkEonEjKbuaW7YsIGhQ4daLBs2bBhbt26loqKi0Tbr169vNc/adOvWzSHv2xKkqzroyRX05Std1UG6thxVzzSbS1ZWFmFhYRbLwsLCqKysJDs7m4iIiAbbZGVlNdhvWVkZZWVlFsvc3d1xd3e32fnIkSMkJyfb3E9rIF3VoTVdTSYT+/btY9++faSnp3Pw4EGOHz/O8ePHOXfunN3eJzg4mNjYWDp06EDHjh3p1KkTXbp0ITEx0S6fG2uR+4E6SNeWo6mkCWAwGCx+Ns+9UHt5fW0uXlab2bNnM2vWLItl06dPZ8KECQD07NmT9PR0Lly4gI+PD23btmX37t0AxMbGUl1dzcmTJwHo3r07hw8fpri4GC8vL4qKipT7qVFRUTg7O3P8+HEAunbtSkZGBoWFhRiNRpKTk9m2bRsAkZGRGI1Gjh49CkDnzp3JzMwkPz8fNzc3unfvzubNmwEIDw/H29ubw4cPAzWXrc+ePUtubi4uLi6kpKSwefNmhBCEhIQQEBDAwYMHAYiPjyc3N5fz58+Tl5dHcnIyW7dupaqqiqCgIEJDQ0lPTwegY8eOFBYWcvbsWQB69+7N9u3bqaioICAggMjISPbu3QtA+/btKS0t5cyZMwCkpqayZ88eTCYTfn5+xMTE8NdffwEQFxdHZWWlUuKnZ8+e7N+/n9LSUry9vWnfvr3yLJZ5pNzJkycpLi6mW7duHDlyhOLiYjw9PUlISGD79u1KvF1cXMjIyACgS5cunDhxgoKCAoxGI507d2br1q0ARERE4OnpyZEjRwBITk7m9OnT5OXl4erqSs+ePZW/Y1hYGL6+vhw6dEiJ97lz58jJycHZ2ZnU1FS2bNlCdXU1ISEh5OXlKet26tSJvLw8zp8/j8FgIC0tjW3btlFZWUlgYCBhYWFKvDt06EBxcbHyha9Xr158//33/PHHH+zYsYNt27aRn5/f4H6tJtnZ2WRnZyv7a2N4eXmRlpbG2LFjCQ8PJyYmhoiICLy8vJR4JyUlkZWVRW5ubp14h4aG4ufnp8Q7ISFBef/8/HySk5OVeAcHBxMcHKzMR9qxY0cKCgqULwy199nAwEDCw8PZt28fULPPlpSUWMR79+7dlJWV4e/vT3R0tLLPtm3blvLycqWWo7XHiOLi4jrHiE6dOrFjxw5AO8eI2vts7WOEk5MTvXr10tQxoqioqN5jhPlepz2OEb17925yP1cQrQQgli5d2mibq666Sjz44IMWy5YsWSJcXFxEeXm5EEKI6Oho8frrr1u0ef3110VMTEyD/ZpMJlFQUGDxMplMLduQi9i9e7dd+mkNpKs6NNe1qqpKrFmzRkybNk34+fkJwKqXi4uLSEtLE9OmTRNvvPGGWLFihTh27JioqKhQxbeiokIcOXJE/Pzzz+K///2vuOuuu0RaWppwd3e32tnf319MmzZNrFy5UlRWVjbLszmuWkC6qoPWXFttGj2DwcDSpUsZO3Zsg22efPJJvv/+e+WbIcC9997Lzp072bBhAwATJkygqKiIH3/8UWkzYsQI/P39HTIQSGtlaxpDuqpDY67l5eUsXryYDz74gLVr1zbaj6+vL8OHD2fYsGEMGTKEqKioRq+gqOHbUjIyMlizZg0///wzy5cvp7i4uNH2V199Nffeey/XX389bm5ureqqFtJVHTTnqmZGLioqEjt27BA7duwQgHj99dfFjh07xPHjx4UQQjz11FNi0qRJSvujR48KT09PMX36dLFv3z4xd+5c4erqKr755hulzZ9//imcnZ3FK6+8ItLT08Urr7wiXFxcxMaNG9XclAZx1Pu2BOmqDrVdjxw5Iu65555Gz74CAwPFAw88IDZs2CCqq6sd6qs21dXVYuPGjeLhhx8WgYGBjcbl7rvvFgcPHnSYq61IV3XQmquqSXPNmjX1fjgmT54shBBi8uTJon///hbrrF27VvTo0UO4ubmJuLg48e6779bp9+uvvxbx8fHC1dVVJCQkiMWLF6u5GY2itT9oY0hX+3Pw4EExfPjwBhNBUFCQmDVrljh16pSjVRW0ENtTp06JWbNmidDQ0AZjN2HCBPHVV185WtVqtBBXa5GuLafV7mleqmRmZjpawWqkq+0UFxeLWbNmNXigHzRokFi5cqVDziCtRYuxra6uFqtXrxZDhgxpMLYzZ84UJSUljlZtEC3GtSGka8vR1HOaesTFRXMDkBtEuraMffv2cc0112AwGPD29mbGjBnK7wYOHMh3332HqPkCyqpVqxg8eLAq9yLthZZia8ZgMDBw4EB++eUXJZbr16+nb9++SpuZM2fi5eWltDWP6NQKWoxrQ0jXliOTpo2YhzLrAelqPb///jtxcXEYDAaSk5NZs2YNAG5ubnz44YdUVlYihGD16tWEhoY61LW5ODq21tK3b1/eeOMNhBBUVlbywQcfKM+Irl27lqSkJAwGA3Fxcfz5558OttVPXEG62oJMmhLJ/2fjxo1ER0djMBi4+uqrlWfp+vTpw65duxBCUFZWxl133YWzs7ODbS8vnJ2dmTZtGiaTCSEEu3btUs5Cjx8/zpVXXonBYKBNmzbKs4sSiRq02iMnlyqlpaV4eno6WsMqpGtdTpw4wS233FJnGsZRo0bx/vvv06ZNmyb70FNcQV++1rhmZmZyzz33sHz5covlvXv35uuvvyY6OlpNRYVLLa5aQWuu8kzTRrQ2A39jSNcaKisrefLJJzEYDMTGxioJc8CAAZw4cQIhBD/88INVCVNtVzXQk681rlFRUfzwww8IITh+/Dj9+/cHYNOmTcTExGAwGHjssceorKx0uKtWkK4tRyZNGykoKHC0gtVc7q5bt27F3d0dV1dX/v3vfwMQFBSkTC+2Zs2aFp2V6CmuoC/f5rrGxMSwdu1ahBBs2rSJoKAgAP7zn//g6uqKq6uramUEL+W4OhKtucqkaSNGo9HRClZzObpWV1fzzDPPYDAY6NWrF+Xl5QC8/vrrVFdXk52dTa9evWx6Dz3FFfTla4trWloa2dnZVFdX88YbbwA1Vxn69OmDwWDgySefpKqqyl6ql01cWxutucp7mjZSVVWlm0Ehl5Pr+fPnGTRokDIhNNRMeP3jjz/a/R6XnuIK+vK1t+upU6cYMWKExX7RpUsXVq1aZfMo6Ms5rmqiNVd5pmkj5ioaeuBycN22bRsGg4HQ0FDlwDhjxgyqq6v566+/VBkUoqe4gr587e3apk0bdu/eTXV1NS+99BIAf/31F2FhYRgMBququjTE5RxXNdGaq0yakkuC77//HoPBQGpqqrLMfG9r5syZmp5sQNL6GAwGnn32WYQQrFu3TlmempqKwWBg2bJljpOTaBqZNG0kIiLC0QpWcym6fvrppxgMBq699lqgpniyeQSseRSl2ugprqAv39ZwvfrqqxFCcOLECaXA/bhx4zAYDMybN8/qfmRc1UFrrjJp2oiWnh9qikvJ9aOPPsJgMDB58mQAUlJSyM/P5/z58632XJ4ZPcUV9OXbmq7R0dFkZWVRWFioDA6bOnUqBoOBjz76qMn1ZVzVQWuuMmnaiLkyvR64FFwXLVqEwWBg2rRpAFxzzTWUlpaydetW/Pz8WlNRQU9xBX35OsLVx8eHzZs3c+HCBYYNGwbAtGnTMBgMfP755w2uJ+OqDlpzlUlTogtWr16NwWDg5ptvBmqSpclk4tdff8XDw8PBdpJLEaPRyIoVKzCZTAwZMgSASZMmYTAYWLlypYPtJI5CPnJiI8XFxXh7eztawyr06Lp//34SExOV5d27d+ePP/7Ay8vLgXaW6CmuoC9fLbmWlpZy5ZVXsmPHDmVZeno6CQkJgLZcm0K6thx5pmkjp0+fdrSC1ejJ9eDBg0RERCgJ08vLi3PnzrFjxw5NJUzQV1xBX75acvX09GT79u1kZ2crtwISExMJDg6msLBQU65NIV1bjkyaNpKXl+doBavRg6sQgilTppCSkkJWVhZQU8+yuLiYkJAQB9vVjx7iWhs9+WrRNSgoiPz8fPbt2wdATk4Ofn5+TJ8+Hb1cuNNiXBtCa64yadqIq6uroxWsRuuuy5Ytw8nJiU8++QSAr7/+GiGExeVZLaL1uF6Mnny17JqYmIgQgiVLlgDw448/4uTkxNKlSx1s1jRajuvFaM1V3tOUOJzs7GyLs8i77rqLDz74QE5IINENQgjuv/9+5syZoyzLyspSnvuUXDrIM00bUatighpo0fXee+9VEqaLiwt5eXl8+OGHuiokrMW4NoaefPXiajAYuP3228nPz8fNzQ2A8PBw7rnnHgeb1Y9e4grac5VJU+IQdu/ejcFg4L333gPgp59+oqKiAn9/f8eKSSQ24OfnR1lZGatWrQLg/fffx2AwsH37dgebSeyFTJo2oqfLL1pwFUJwzTXX0K1bNwBGjx5NdXU1w4cPt2inBVdr0ZMr6MtXr66DBg2iurqaMWPGADUzVg0cOFAzA4X0GlctIJOmjfj6+jpawWoc7bplyxacnJxYs2YNUPOMm3mi9YtxtGtz0JMr6MtXz64Gg4HvvvuOAwcOADUFBJycnNi4caMj9CzQc1wdjUyaNnLo0CFHK1iNo1yFEIwYMYK0tDSgZkoyIYTyUHh9yLiqh558LwXXTp06IYTg3nvvBaBv374MGzbMoWedl0JcHYVMmhJVOXToEE5OTqxYsUL5+YMPPnCwlUTS+syZM0eZR/WXX37BycmJgwcPOthK0lxk0rQRrT9DWJvWdn322Wfp1KkTANdffz1CCDp06GDVujKu6qEn30vNtV27dgghlDmU4+Pjefrpp9VWq8OlFtfWRCZNGzl37pyjFaymtVxLS0sxGAy8/PLLQM2Q8cWLFzerDxlX9dCT76XqumDBAuVRildeeQWDwUBpaalaanW4VOPaGsikaSM5OTmOVrCa1nD9/ffflblh/f39qaioUO5lNgcZV/XQk++l7JqWlkZFRQWhoaFAzfzK69atU0OtDpdyXNVGJk0bcXZ2drSC1ajteu+993L11VcD8Oabb5KXl4eLi0uL+pJxVQ89+V7qri4uLpw9e5a33noLgAEDBii1YtXkUo+rmshp9CQ2U15ejru7u/Lz4cOHad++vQONJBL9kZGRQdu2bZWfTSaTxedKog3kmaaNbNmyxdEKVqOG6/79+5UPdnBwMFVVVXZJmJd7XNVET76Xk2tcXBxVVVXK5Vqj0Uh6ero91OpwOcXV3sikaSPV1dWOVrAae7t+/PHHysi2Z599lvPnz+PkZJ9d6nKOq9royfdyc3VycuLs2bO88MILACQlJTF37lyb+72Yyy2u9kQmTRvRao3H+rCn60033cSdd94JwJ9//slLL71kt77h8o1ra6An38vV9bnnnmP9+vVATdWfsWPH2q1vuHzjag9UT5pz5syhbdu2GI1GUlJS+P333xtsO2XKFAwGQ51XcnKy0mb+/Pn1tjGZTGpvSr0EBgY65H1bgj1cq6qqMBgMfPPNN0DNyLZ+/frZ3O/FXG5xbU305Hs5u/bt21cpwPztt99iMBioqqqyS9+Xc1xtRdWkuWjRIh5++GGeffZZduzYwVVXXcWIESM4ceJEve3feustzpw5o7xOnjxJYGAgN910k0U7X19fi3ZnzpzBaDSquSkNYp5XUg/Y6lp7NKyvry/V1dWq7dCXU1xbGz35Xu6u/v7+VFdXExAQANSMts3NzbW538s9rragatJ8/fXXmTp1KnfddReJiYm8+eabREdH8+6779bb3s/Pj/DwcOW1detW8vLyuOOOOyzaGQwGi3bh4eFqboYE2Ldvn5IgJ06cSEFBgSwSLZG0AgaDgdzcXOU4GBQUxN69ex1sdfmiWtIsLy9n27ZtDB061GL50KFDlWv1TTF37lwGDx5MbGysxfLi4mJiY2OJiopi9OjR7Nixo9F+ysrKKCwstHiVlZU1b4MawDxNnB5oqetPP/2kXCKfM2cOX3zxhT216uVyiKuj0JOvdP0f8+bNU044OnfuzA8//NDivmRcW07Lnjy3guzsbKqqqurUQgsLCyMrK6vJ9c+cOcNPP/3El19+abE8ISGB+fPn06VLFwoLC3nrrbe44oor2LVrFx07dqy3r9mzZzNr1iyLZdOnT2fChAkA9OzZk/T0dC5cuICPjw9t27Zl9+7dAMTGxlJdXc3JkycB6N69O4cPH6a4uBgvLy/c3NyUSZejoqJwdnbm+PHjAHTt2pWMjAwKCwsxGo0kJyezbds2ACIjIzEajRw9ehSo+RBkZmYqld+7d+/O5s2bgZoK8N7e3hw+fBiomYvx7Nmz5Obm4uLiQkpKCps3b0YIQUhICAEBAYpTfHw8ubm5nD9/ntLSUgYOHMjWrVupqqoiKCiI0NBQZVh7x44dKSws5OzZswD07t2bJ554gldffRWAH374geDgYDZt2kT79u0pLS3lzJkzAKSmprJnzx5MJhN+fn7ExMTw119/ATVD6SsrK8nMzFTivX//fkpLS/H29qZ9+/bs2rULgJiYGKCmbJiXlxfdunXjyJEjFBcX4+npSUJCglLQNyoqChcXFzIyMgDo0qULJ06coKCgAKPRSOfOndm6dSsAEREReHp6KhNmJycnc/r0afLy8nB1daVnz57KtGZhYWH4+voq1RUSExM5d+4cOTk5ODs7k5qaypYtW6iuriYkJIQLFy4o8e7UqRN5eXmcP38eg8FAWloa27Zto7KyksDAQMLCwpR4d+jQgeLiYuXzkJaWxs6dOykvL8ff35+oqCj27NkD1MxZajKZOH36NFBTn3Hv3r2YTCZ8fX2Ji4uz2GerqqqUePfo0YODBw9SUlKCt7c3rq6uim90dDROTk4W++yxY8coKirCw8ODxMREJd5t2rTBzc2NY8eOKfE+efIk+fn5uLu707VrV+XxgPDwcLy8vJR4JyUlkZWVRW5ubp14h4aG4ufnp8Q7ISGB7OxssrOzuXDhAgMGDFDiHRwcTHBwMPv371f22YKCAmWqtd69e7N9+3YqKioIDAwkPDycffv2AdC+fXtKSkqUePfq1Yvdu3dTVlaGv78/0dHRyj7btm1bysvLOXXqlLLPNnWM2L9/P15eXnWOEZ06dVK+2Nt6jBg4cCDvvvsu9957L2PGjOGRRx5h/PjxzT5GlJaW1nuMcHJyolevXs06RpjjHRAQQGRkpHIWbK9jRGVlJSkpKXWOEebbfPY4RvTu3RurESpx6tQpAYj169dbLH/ppZdEfHx8k+u//PLLIigoSJSVlTXarqqqSnTr1k088MADDbYxmUyioKDA4mUymazbkCbYuHGjXfppDZrr+sgjjwhAAOLgwYMqWdXPpRxXR6MnX+laP4cOHVI+m3//+9+bvb6Ma8tR7UwzODgYZ2fnOmeV586da7IStxCCefPmMWnSJNzc3Bpta/5m1FjNNXd3d9Vm1tDTfb3muI4dO5Zvv/0WqLlqEBQUpJZWvVyqcdUCevKVrvXToUMHcnJyCAoK4p133uHkyZPK59UaZFxbjqrT6PXu3ZuUlBTmzJmjLEtKSuK6665j9uzZDa63du1aBg4cyF9//UXnzp0bfQ8hBGlpaXTp0oV58+bZzf1yJi0tTbnMVlpaioeHh4ONJBJJfZhMJuXz2bVrV+USpkQ9VB09+8gjj/DRRx8xb9480tPTmT59OidOnOCee+4B4Omnn+b222+vs97cuXPp3bt3vQlz1qxZ/Pzzzxw9epSdO3cydepUdu7cqfTZ2pjvP+gBa1xjYmKUhFlZWemwhHmpxVVL6MlXujaO0WiksrISgN27dxMREWHVejKuLUe1y7MAEyZMICcnhxdeeIEzZ87QuXNnfvzxR2U07JkzZ+o8s1lQUMDixYuVWf8vJj8/n7vvvpusrCz8/Pzo0aMHv/32W4vKT9kD8w6rB5py9fPzo7CwEKiZusqRl0UupbhqDT35StemcXZ2prq6GicnJ7KysvDw8FBq2jaEjGvLkVVObOTQoUMNjtrVGg25CiEwGo2Ul5fj5OREZWWlw+8jXApx1Sp68pWuzcNoNCqP0zV2aNeCq7VozVXOPWsjTQ1q0hINubq7u1NeXo6Hh4cyTZ6juRTiqlX05Ctdm4fJZMLf3x+oOQNtKHFqwdVatOYqk6aNqFW6Rw3qc/X396eiogIvLy9KS0sdYFU/eo+rltGTr3RtPnl5efj4+FBdXd3gmAStuFqD1lxl0ryMiY6OpqCgAKiZZUkikVwaFBYW4u7uTllZGW3atHG0ziWFTJo20qFDB0crWE1t17S0NGX2Da3VqwP9xlUP6MlXuracCxcuAHD69Gm6detm8TutuTaG1lxl0rQRPZ2hmV3HjRunPFbi6FGyDaHHuOoFPflK15ZjMBiUL8S7d+/m2muvVX6nNdfG0JqrTJo2Ys08ulohKyuLRx55hGXLlgFQUVGhyYQJ+ourntCTr3S1DYPBoDyy8f333/PQQw8B2nRtCK25qvqcpkRbfPPNN7zxxhtAzUw/5tqYEonk0sXZ2ZkLFy7g4eHBf//7X9q1a0efPn0craVb5HOaNiKE0OzZWm1++uknRo4cCcD58+cJDg52sFHj6CWuoC9X0JevdLUfubm5yhzSP/zwA6NGjXKwkXVoLa7y8qyN7Ny509EKTZKenq4kzIMHD2o+YYI+4mpGT66gL1/paj8CAwOVwhajR49Wys5pHa3FVSZNGykvL3e0QqPk5uaSlJQEwNtvv62pmTUaQ+txrY2eXEFfvtLVvnTo0IF169YBNbUl8/LyHGzUNFqLq0yaNmKefUOLVFdXK5dj/u///o8hQ4Y42Mh6tBzXi9GTK+jLV7ran6uvvpqZM2cCNWefVVVVjhVqAq3FVd7TtJGSkhK8vLwcrVEvTk5OCCG4+eabWbBggaZdL0a6qoeefKWrOpSUlHDffffx6aefAo3PU+totBZXeaZpI1q9L3DjjTcihMDT05MFCxYA2nWtD+mqHnryla7qsGfPHj755BPlLO7GG290rFAjaC2uMmlegnz66acsXrwYgKKiIgfbSCQSrZKbmwvA4sWLmTt3roNt9IG8PGsj58+fJyQkxNEaCvv37ycxMRGA7Oxs5Z4maM+1MaSreujJV7qqQ23XvLw8AgMDgZrjR3x8vCPV6qC1uMozTRsxmUyOVlAoLy9XEubvv/9ukTBBW65NIV3VQ0++0lUdarsGBATw559/ApCQkKDU49QKWourTJo2cvr0aUcrKLi7uwPwzDPPcOWVV9b5vZZcm0K6qoeefKWrOlzs2q9fP5577jmgppC1ltBaXGXSvES47777APDz8+Of//yng20kEoneeOGFF5SJT+69914H22gXeU/TRiorKx0+h+v69eu54oorFB9nZ+d622nB1Vqkq3royVe6qkNDrlVVVcrytWvX0r9//9ZWq4PW4irPNG1k7969Dn3/CxcuKAnz0KFDDSZMcLxrc5Cu6qEnX+mqDg25Ojs7c+TIEQAGDBig1OR0JFqLq0yaNuLom9Senp4AvPbaa00Wa3W0a3OQruqhJ1/pqg6NubZr106phmQ+vjgSrcVVJk0b8fX1ddh7z5o1CwBvb28effTRJts70rW5SFf10JOvdFWHplwffvhhZeKDZ555phWMGkZrcZX3NG3EXKeutTly5IhyZlleXo6rq2uT6zjKtSVIV/XQk690VQdrXCsrK5XjyqFDh5q8kqUWWourPNO0kd27dzvkfc078IYNG6xKmOA415YgXdVDT77SVR2scXVxcWHDhg0AdOzY0WHz02otrjJp6pCxY8cCcO2118oK7BKJRDX69OmjzEtrrsl7uSMvz9pIVlYW4eHhrfZ+27ZtIzU1FWh+ZYLWdrUF6aoeevKVrurQXFeDwQDA5s2b6dWrl1pa9aK1uMozTRtpzVp0QgglYZorsDcHrdfNq410VQ89+UpXdWiuq/l4k5aW1uqXabUWV5k0bSQzM7PV3st8eWTKlCktuinfmq62Il3VQ0++0lUdmuvaoUMH/va3vwG0ejF7rcVVJk2dsGfPHlasWAHAxx9/7GAbiURyufHee+8B8Ouvv2pucE5rIu9p2kh5eTlubm6qv4/5nsK+ffuUSibNpbVc7YF0VQ89+UpXdWipa+3Sg62VOrQWV3mmaSMHDx5U/T0eeughAAYNGtTihAmt42ovpKt66MlXuqpDS10TEhIYNWoUAPfff789lRpEa3GVSdNGSkpKVO0/JyeH//73vwCsXLnSpr7UdrUn0lU99OQrXdXBFtfvv/8egHfeeYfz58/bS6lBtBZX1ZPmnDlzaNu2LUajkZSUFH7//fcG265duxaDwVDntX//fot2ixcvJikpCXd3d5KSkli6dKnam9Eg3t7eqvZvLtWzfPly5RJtS1Hb1Z5IV/XQk690VQdbXA0GgzK+IjQ01F5KDaK5uAoVWbhwoXB1dRUffvih2Ldvn3jooYeEl5eXOH78eL3t16xZIwBx4MABcebMGeVVWVmptFm/fr1wdnYWL7/8skhPTxcvv/yycHFxERs3blRzUxrEZDKp1vf3338vAGGvP5OarvZGuqqHnnylqzrYw9XJyUkA4rvvvrODUcNoLa6qJs20tDRxzz33WCxLSEgQTz31VL3tzUkzLy+vwT7Hjx8vhg8fbrFs2LBh4uabb7bZtyWolayrq6uVhJmTk2OXPh31xaIlSFf10JOvdFUHe7jm5uYqx6jq6mo7WNWP1uKq2uXZ8vJytm3bxtChQy2WDx06lPXr1ze6bo8ePYiIiGDQoEGsWbPG4ncbNmyo0+ewYcMa7bOsrIzCwkKLV1lZWTO3qHW57777ALj99tsJDAx0sI1EIpFYEhAQwLRp0wC48847HWzTeqhWDjs7O5uqqirCwsIsloeFhZGVlVXvOhEREXzwwQekpKRQVlbGZ599xqBBg1i7di1XX301UDOlUnP6BJg9e7ZSRsvM9OnTmTBhAgA9e/YkPT2dCxcu4OPjQ9u2bZXnkGJjY6murubkyZMAdO/encOHD1NcXIyXlxcRERFs2rQJgKioKJydnTl+/DgAXbt2JSMjg8LCQoxGI8nJyWzbtg2AyMhIjEYjR48eBaBz585kZmaSn59PZWWl8kzUvffey/Hjx/H29ubw4cMAJCYmcvbsWXJzc3FxcSElJYXNmzcjhCAkJISAgABlxFl8fDy5ubmcP39e+aKwdetWqqqqCAoKIjQ0lPT0dKBmUubCwkLOnj0LQO/evdm+fTsVFRUEBAQQGRmpFIRt3749paWlnDlzBoDU1FT27NmDyWTCz8+PmJgY/vrrLwDi4uKorKxUHlLu2bMn+/fvp7S0FG9vb9q3b8+uXbsAiImJAWpq6G3atIlu3bpx5MgRiouL8fT0JCEhge3btyvxdnFxISMjA4AuXbpw4sQJCgoKMBqNdO7cma1btyr7lqenp1JgNzk5mdOnT5OXl4erqys9e/ZU/o5hYWH4+voqs6AkJiZy7tw5cnJycHZ2JjU1lS1btlBdXU1ISAiBgYHKup06dSIvL4/z589jMBhIS0tj27ZtVFZWEhgYSFhYmBLvDh06UFxcrOy7aWlp7Ny5k/Lycvz9/YmKimLPnj1ATY1Dk8nE6dOnAUhJSWHv3r2YTCZ8fX2Ji4uz2GerqqqUePfo0YODBw9SUlKCt7c34eHhim90dDROTk4W++yxY8coKirCw8ODxMREJd5t2rTBzc2NY8eOKfE+efIk+fn5uLu707VrV7Zs2QJAeHg4Xl5eSryTkpLIysoiNze3TrxDQ0Px8/NT4p2QkEB2djbZ2dnKPmuOd3BwMMHBwco4h44dO1JQUMC5c+fq7LOBgYGEh4ezb98+ZZ8tKSlR4t2rVy92795NWVkZ/v7+REdHK/ts27ZtKS8v59SpU8o+29QxwrzPXnyM6NSpEzt27FD2WXscI9zc3OjevTubN29W4t3cY4Q5/rWPEU5OTvTq1cvqY8T777/Phx9+yPz583nggQfo1KmT3Y8RUHM8uPgYceLECQC7HCN69+6N1ah1Cnvq1CkBiPXr11ssf+mll0R8fLzV/YwePVqMGTNG+dnV1VV8+eWXFm0+//xz4e7u3mAfJpNJFBQUWLzsdZ38zJkzdumnNrGxsQIQixYtsmu/ariqhXRVDz35Sld1sKfrV199JQARFhZmtz5ro7W4qnZ5Njg4GGdn5zpngOfOnatzptgYffr0sZhnNTw8vNl9uru74+vra/Fyd3e32qExzN8Y7cWRI0eUPsePH2/Xvu3tqibSVT305Ctd1cGerjfddBMAZ8+eVeWZSq3FVbWk6ebmRkpKSp1nC1euXEm/fv2s7mfHjh1EREQoP/ft27dOn7/88kuz+tQy5jllL+dpqiQSib4w30aIj493sEkroOZprPmRk7lz54p9+/aJhx9+WHh5eYmMjAwhhBBPPfWUmDRpktL+jTfeEEuXLhUHDx4Ue/bsEU899ZQAxOLFi5U2f/75p3B2dhavvPKKSE9PF6+88opDHzkpLS21W1+//fabAISbm5vd+qyNPV3VRrqqh558pas6qOFqNBoFIFatWmXXfrUWV1WTphBCvPPOOyI2Nla4ubmJnj17inXr1im/mzx5sujfv7/y87/+9S/Rvn17YTQaRUBAgLjyyivF8uXL6/T59ddfi/j4eOHq6ioSEhIskmprs3fvXrv1xf8fvp2VlWW3PmtjT1e1ka7qoSdf6aoOarieO3fOrs+Vm9FaXFUbPWvmvvvuUx6fuJj58+db/PzEE0/wxBNPNNnnjTfeqFQTdzRFRUV26eebb74BakYONueeb3Owl2trIF3VQ0++0lUd1HANCQkhOTmZvXv38vXXXyv3Om1Fa3GVc8/aiIeHh136Me9g5iH7amAv19ZAuqqHnnylqzqo5bpx40bAvoMYtRZXmTRtxJaqI2Y+//xzoGaQk5rzLNrDtbWQruqhJ1/pqg5quXp7ezNgwACg7pXElqK1uMqkaSPmh2htYdKkSUBNcVc1sYdrayFd1UNPvtJVHdR0/emnnwC444477NKf1uIqk6aDWbRoEQBXXHGF5i5DSCQSSXMxGo30798fgE8//dTBNvbHIEQrld++RMnMzCQqKqrF65vLfZmn3FITW11bE+mqHnryla7qoLbrhQsX8PT0BMDWFKO1uMozTRtxc3Nr8brmmnSdO3dWPWGCba6tjXRVDz35Sld1UNvVw8OD7t27A/Djjz/a1JfW4iqTpo2YJ69uCSNGjADgt99+s5dOo9ji2tpIV/XQk690VYfWcF27di0Ao0aNsqkfrcVVJk0HYZ4mz9PTk4CAAAfbSCQSiX3x8/MjKCgIQKlQcikg72naSGlpqXLtvjmY72UeOXKEdu3a2VurXlrq6gikq3royVe6qkNruWZkZNC2bVug5fc2tRZXeaZpI+Y6m80hLy9P+X9rJUxomaujkK7qoSdf6aoOreUaFxen/P/8+fMt6kNrcZVJ00by8/ObvY75XqZ5IFBr0RJXRyFd1UNPvtJVHVrTdc2aNcD/jnvNRWtxlUnTRppbl1MIoVRMHzZsmBpKDWKvGqKtgXRVDz35Sld1aE1X8wxB27Zto7q6utnray2uMmnaSNeuXZvV/p///CcATz75pBo6jdJcV0ciXdVDT77SVR1a2/Uf//gHAM8//3yz19VaXGXStJHmTrD+3HPPAfDyyy+rodMoak4Gb2+kq3royVe6qkNru86aNQv430lDc9BaXGXSbEXMw67bt2+Pk5MMvUQiuTxwcnKiY8eOgPbmkm0u8shtI+Hh4Va3HThwIAC//PKLWjqN0hxXRyNd1UNPvtJVHRzhunLlSqBmnu3moLW4yqRpI9ZOf1dVVaU8atKaj5nUpjWm6rMX0lU99OQrXdXBEa6xsbEAmEwmqqqqrF5Pa3GVSdNGjhw5YlW7F198EWjZNX17Ya2rFpCu6qEnX+mqDo5yfeWVVwCYMWOG1etoLa4yabYS5hvhTz31lINNJBKJxDE8/vjjgGNPHmxFTqNnI0VFRfj4+DTaJisri4iICIxGIxcuXGgls7pY46oVpKt66MlXuqqDI139/f0pKCggMzOTNm3aNNlea3GVZ5o2kpWV1WSb2267DXDcACAz1rhqBemqHnryla7q4EhXc6mwW2+91ar2WourTJo2kpub22SbX3/9FYCrrrpKbZ1GscZVK0hX9dCTr3RVB0e69uvXD4B169ZZ1V5rcZVJ00ZcXV0b/b352czU1NTW0GmUply1hHRVDz35Sld1cLSr+bETa57ZdLTrxch7mioTHx/PwYMHOXz4MO3bt3e0jkQikTico0eP0r59e9q3b8/hw4cdrdMs5JmmjZgnX2+IgwcPAmgiYTblqiWkq3royVe6qoOjXc3PqlvzOImjXS9GJk0V2bZtG/C/Wf4lEolEUoO5ypPWkmJTyKRpI6GhoQ3+btKkSQDMnTu3tXQapTFXrSFd1UNPvtJVHbTg+v777wNw++23N9pOC661kUnTRvz8/Br8XXp6OuC4afMupjFXrSFd1UNPvtJVHbTgap5Wz3wLqyG04FobmTRt5NChQ/UuP3bsGACdO3duTZ1GachVi0hX9dCTr3RVB624mp8qaMxHK65mZNJUifvvvx+Ajz76yMEmEolEok3ee+89AB588EEHm1iPfOTERgoKCuq9fGAwGADQUngbctUi0lU99OQrXdVBS65NHSu15AryTNNmsrOz6ywrKytzgEnT1OeqVaSreujJV7qqg5ZczUnTZDLV+3stuUIrJM05c+bQtm1bjEYjKSkp/P777w22XbJkCUOGDCEkJARfX1/69u3Lzz//bNFm/vz5GAyGOq+GAq429f1B//vf/wLw+uuvt7ZOo2ht52sM6aoeevKVruqgJde33nrL4t+L0ZIrqJw0Fy1axMMPP8yzzz7Ljh07uOqqqxgxYgQnTpyot/1vv/3GkCFD+PHHH9m2bRsDBw5kzJgx7Nixw6Kdr68vZ86csXgZjUY1N6VBnJzqhvCJJ54A4O9//3tr6zRKfa5aRbqqh558pas6aMn1nnvuARoum6glV1D5nmbv3r3p2bMn7777rrIsMTGRsWPHMnv2bKv6SE5OZsKECTz//PNAzZnmww8/TH5+vhrKdkGL9zMlEolEq+jpmKlaCi8vL2fbtm0MHTrUYvnQoUNZv369VX1UV1dTVFREYGCgxfLi4mJiY2OJiopi9OjRdc5EL6asrIzCwkKLl73uO27ZssXiZ/NZtBYmaL+Yi121jHRVDz35Sld10Jprnz59AMjIyKjzO625uqjVcXZ2NlVVVYSFhVksDwsLs7o+2n/+8x9KSkoYP368siwhIYH58+fTpUsXCgsLeeutt7jiiivYtWsXHTt2rLef2bNnM2vWLItl06dPZ8KECQD07NmT9PR0Lly4gI+PD23btmX37t1AzQO41dXVnDx5EoDu3btz+PBhiouL8fLyorKyUpkGKioqimeeeQaomQ3owoULZGRkUFhYiNFoJDk5WZlaLzIyEqPRyNGjR4Ga5zkzMzPJz8/Hzc2N7t27s3nzZgDCw8Px9vZWJjZOTEzk7Nmz5Obm4uLiQkpKCps3b0YIQUhICAEBAcoDw/Hx8eTm5nL+/Hny8vIA2Lp1K1VVVQQFBREaGqpMwtCxY0cKCws5e/YsUHOlYPv27VRUVBAQEEBkZCR79+4FaubSLS0t5cyZM0DNl4Q9e/ZgMpnw8/MjJiaGv/76C4C4uDgqKyvJzMxU4r1//35KS0vx9vamffv2SjWYmJgYAHJycti0aRPdunXjyJEjFBcX4+npSUJCglIZISoqChcXF+WD1qVLF06cOEFBQQFGo5HOnTuzdetWACIiIvD09FTmukxOTub06dPk5eXh6upKz549lb9jWFgYvr6+yvNhiYmJnDt3jpycHJydnUlNTWXLli1UV1cTEhJCWVmZsm6nTp3Iy8vj/PnzGAwG0tLS2LZtG5WVlQQGBhIWFqbEu0OHDhQXFyufh7S0NHbu3El5eTn+/v5ERUWxZ88eoGaCDJPJxOnTpwFISUlh7969mEwmfH19iYuLs9hnq6qqlHj36NGDgwcPUlJSgre3t8U+Gx0djZOTE8ePHwega9euHDt2jKKiIjw8PEhMTFTi3aZNG9zc3JRnkLt06cLJkyfJz8/H3d2drl27Kge48PBwvLy8lHgnJSWRlZVFbm5unXiHhobi5+enxDshIYHs7Gyys7OVK0rmeAcHBxMcHMz+/fuVfbagoIBz587V2WcDAwMJDw9n3759yj5bUlKixLtXr17s3r2bsrIy/P39iY6OVvbZtm3bUl5ezqlTp5R9tqljhHmfvfgY0alTJ+WLfVRUFM7OzhbxdsQxovY+W/sY4eTkRK9evVr9GPHYY49x44038sADD/CPf/zD4hhRVFSEyWSqc4wwn6DY4xjRu3dvrEaoxKlTpwQg1q9fb7H8pZdeEvHx8U2u/+WXXwpPT0+xcuXKRttVVVWJbt26iQceeKDBNiaTSRQUFFi8TCaTdRvSBIcPH7b4GRCAqK6utkv/9uRiVy0jXdVDT77SVR205lpdXa0cOy9Ga66qnWkGBwfj7Oxc56zy3Llzdc4+L2bRokVMnTqVr7/+msGDBzfa1vzNqLFZI9zd3XF3d7devhkEBwfXu9x8jV5LNOSqRaSreujJV7qqg9ZcGzteas1VtXuabm5upKSksHLlSovlK1euVCp318eCBQuYMmUKX375JaNGjWryfYQQ7Ny5k4iICJudW4L5MhGgXMoxX5/XGrVdtY50VQ89+UpXddCi65VXXgmgXLo2ozVXVcfyPvLII3z00UfMmzeP9PR0pk+fzokTJ5Qhxk8//bTFDPcLFizg9ttv5z//+Q99+vQhKyuLrKwsCgoKlDazZs3i559/5ujRo+zcuZOpU6eyc+dOpU9H8uabbwLw5JNPOlZEIpFIdMazzz4LwGuvveZgkyZQ+/rvO++8I2JjY4Wbm5vo2bOnWLdunfK7yZMni/79+ys/9+/fX7muXfs1efJkpc3DDz8sYmJihJubmwgJCRFDhw6tc9+0NcnJyVH+b/atqqpymE9j1HbVOtJVPfTkK13VQYuuDd3X1JqrnHvWRo4dO0bbtm0B7T9rVNtV60hX9dCTr3RVB6261ncM1ZqrtqZa0CHmoe5VVVUADpuZyBrMrnpAuqqHnnylqzpo1dXX1xeAiooKZZnWXGXStBM//fQT8L+SYBKJRCJpHubj5w8//OBgk4aRl2ftxMiRI/npp584cuQI7dq1c7SORCKR6I6MjAzatm3L8OHDlRMRrSHPNG3EPPOE+Q+s5YRpdtUD0lU99OQrXdVBq65xcXEArFixQlmmNVeZNG2k9rV3rSNd1UFPrqAvX+mqDtK15cikaSOBgYHKSC83NzcH2zTOxRPfaxnpqh568pWu6qBlVw8PD+B/I2i15iqTpo2Eh4crEyzfdtttDrZpnPDwcEcrWI10VQ89+UpXddCy66RJkwDYuHEjoD1XmTRtZN++fSxcuBCAm2++2cE2jWOu9qAHpKt66MlXuqqDll1vvfVWAL788ktAe64yadoBc9IcOHCgg00kEolE31xxxRUALF682MEm9SMfObGR7OxsQkJCAO3OBGQmOztbcxUDGkK6qoeefKWrOmjdtfbMQFpzlWeaNlJSUuJoBauRruqgJ1fQl690VQfp2nJk0rSRi+uFahnpqg56cgV9+UpXddC6q6urq/J/rbnKpGkj5nkRran9KZFIJJKmGTFiBAAnT550sEldZNK0kZycHACGDBniYJOm6dWrl6MVrEa6qoeefKWrOmjd1Zw0V6xYoTlXmTRt5JtvvgFg8ODBDjZpmt27dztawWqkq3royVe6qoPWXa+55hoA1qxZozlXmTRtZMuWLQAkJSU52KRpysrKHK1gNdJVPfTkK13VQeuuHTt2BGDt2rWac5VJ00bM9zTNQ6S1jL+/v6MVrEa6qoeefKWrOmjd1Xw8PXPmjOZc5XOaNlJfpXGtUlpaiqenp6M1rEK6qoeefKWrOujB1XxsLSkp0ZSrPNO8jPjrr78crWA10lU99OQrXdVBurYcmTQlEolEIrESmTRtoLq6GoCEhAQHm1hH27ZtHa1gNdJVPfTkK13VQQ+u3bp1AyA6OtrBJpbIpGkDx44dA6B79+6OFbGS8vJyRytYjXRVDz35Sld10INr586dATh8+LCDTSyRSdMGzCVrkpOTHWxiHadOnXK0gtVIV/XQk690VQc9uJqT5oYNGxxsYolMmjZw4MABAOLj4x1sIpFIJJcW5mffjx496mATS2TStAG9Jc2ePXs6WsFqpKt66MlXuqqDHlzbt28PaG8iBpk0bcD8Dahdu3YONrGO9PR0RytYjXRVDz35Sld10IOrebDS3r17HWxiiUyaNmCegd/b29vBJtZx4cIFRytYjXRVDz35Sld10IOreUIDrVU6kUnTBrT2x2wKHx8fRytYjXRVDz35Sld10JNrdna2oxUskNPo2YCeptCDmm+XHh4ejtawCumqHnryla7qoBdXLR5j5ZnmZYTWSuw0hnRVDz35Sld10JOr1nBxtIBEImkdTp8+zcmTJzGZTI5WkUgoKCigsLCQwMBAXF1dOXfuHG5uboSEhDRZNUoIQU5ODiaTiZCQENzd3VvJWp5pXlbExsY6WsFqpKv9ePPNNwkLD6dNmzb06dOHxx9/nNCwcF5//XVHqzWJ1mNbG+lqHT/++CODBw/B39+fmJgYfHx88PL2ITo6mrCwMBISk3jnnXfqnbWoqqqKjz76iB7duhESEkJ0dDTBgYHcd999HDlypFX8VU+ac+bMoW3bthiNRlJSUvj9998bbb9u3TpSUlIwGo20a9eO9957r06bxYsXk5SUhLu7O0lJSSxdulQt/UsK81y5ekC62oebbrqJ6dOnk1dlJHDIvYTeNAu/lFHkVxt59NFHuf766x2t2Chaju3FSNemmTlzJqNGjWL9/kx8r7gFg7sXBncvPHteS+iNMwi+7ilOGYJ44MGHGDlqlMW6lZWVjL/xRu6++24CMzJ4LSKS99pEMcnDg2/mzSOlRw82btyo+jaomjQXLVrEww8/zLPPPsuOHTu46qqrGDFiBCdOnKi3/bFjxxg5ciRXXXUVO3bs4JlnnuHBBx9k8eLFSpsNGzYwYcIEJk2axK5du5g0aRLjx49n06ZNam7KJYGeRvtKV9v5v//7P7755ht8Uq8j4s7/w6fnKDzapdA/rQcRd/4fvmnXs3TpUt58801HqzaIVmNbH9K1cZYtW8asWbPwv/p2gie8TMlfq3D1Dydy2vsEDJiCR/teeCVcSfB1TxM6/kXWrrM8wXrxxRf57rvveDsyknci2zDS15ervb15IDiEH6Jj6FBdzZiRIyksLFR1O1RNmq+//jpTp07lrrvuIjExkTfffJPo6Gjefffdetu/9957xMTE8Oabb5KYmMhdd93FnXfeyWuvvaa0efPNNxkyZAhPP/00CQkJPP300wwaNEjTH3yJxBG8PHs2LoFRBFwztc49IoPBgP+AO3ANiuaVf/3LQYaSy4lXX/sPnrFd8O1zExcOraeq8DzBox/D2dOvTltjbFe8025QfjaZTLzz3/9yi58f13jXfVzGx9mZV8PCycvP5/PPP1d1O1RLmuXl5Wzbto2hQ4daLB86dCjr16+vd50NGzbUaT9s2DC2bt1KRUVFo20a6hNqpmEqLCy0eGltaqbWQC/VWEC62kp+fj5nTp/Gp/sIDAbLj/mCIzU/GwwGvHuM5GxWluaehTOjxdg2hHRtmDNnzrD+zz/w6DIUg8FAyf4/cI9KwjW44bJf3t2GKf9fvXo1Ofn53OTn32D7cFdXrvb2ZuGXX9pTvQ6qjZ7Nzs6mqqqKsLAwi+VhYWFkZWXVu05WVla97SsrK8nOziYiIqLBNg31CTB79mxmzZplsWz69OlMmDABqJmHMT09nQsXLuDj40Pbtm2VIdmxsbFUV1crlzO6d+/O4cOHKS4uVvoyXxqOiorC2dmZ48ePA9C1a1cyMjIoLCzEaDSSnJzMtm3bAIiMjMRoNCpT8XXu3JnMzEzy8/Nxc3Oje/fubN68GYDw8HC8vb2VEjmJiYmcPXuW3NxcXFxcSElJYfPmzQghCAkJISAggIMHDwI18+Lm5uZy/vx5ioqKGDx4MFu3bqWqqoqgoCBCQ0OVKbU6duxIYWEhZ8+eBaB3795s376diooKAgICiIyMVKa0at++PaWlpZw5cwaA1NRU9uzZg8lkws/Pj5iYGKXielxcHJWVlWRmZirx3r9/P6WlpXh7e9O+fXt27doFQExMDAB79uzB19eXbt26ceTIEYqLi/H09CQhIYHt27cr8XZxcSEjIwOALl26cOLECQoKCjAajXTu3JmtW7cCEBERgaenpzJYIDk5mdOnT5OXl4erqys9e/ZU/o5hYWH4+vpy6NAhJd7nzp0jJycHZ2dnUlNT2bJlC9XV1YSEhJCXl0dlZSUAnTp1Ii8vj/Pnz2MwGEhLS2Pbtm1UVlYSGBhIWFiYEu8OHTpQXFys7LtpaWns3LmT8vJy/P39iYqKYs+ePUDNVI0mk4nTp08DkJKSwt69ezGZTPj6+hIXF2exzx4/fpxnnnkGt4iOLMoSDG1TTbARzpnA1UkQ4FZz5vl7WTx5Bb3Yvn07fn5+dO3alWPHjlFUVISHhweJiYlKvNu0aYObm5tSEq9Lly6cPHmS/Px83N3d6dq1K1u2bFH2WS8vLyXeSUlJZGVlkZubWyfeoaGh+Pn5KfFOSEggOzub7OxsiouLGTRokBLv4OBggoOD2b9/v7LPFhQUcO7cuTr7bGBgIOHh4Uo1ovbt21NSUqLEu1evXuzevZuysjL8/f2Jjo5W9tm2bdtSXl6uVAOx5hixd+9efH19LY4RXl5edOrUiR07dmjqGFF7n619jHBycqJXr152P0YUFRXh6urK7QO7EBhQxYGBafy2J4O74qsA+OOsAQ9nSAmueR7z88NOjOjiz5vUkJeXV7M/e3lT/P8/0wW9Ums+r4uXkHfVlZSHhjIiM5OFf/5pcUy25hjRu3dvrEW1yQ1Onz5NmzZtWL9+PX379lWW//Of/+Szzz5TdvradOrUiTvuuIOnn35aWfbnn39y5ZVXcubMGcLDw3Fzc+OTTz7hlltuUdp88cUXTJ06tcGh9GVlZXXOLN3d3W0epqzFB28bY9OmTc3aORyJdLWNwsJC/Pz88R9wB369LQf73BVfxUcHnGvabfmWvNUfkZOTTWBgoCNUG0WLsW0I6dowZ8+eJTw8nKCR0/HuMohzS/9JVXEuEZP+0+A6lYXnOfXuHQCsWLGC4cOHszQ2jnijscF1/nbqFM49urPmt9/svg1mVLs8GxwcjLOzc50zwHPnztU5UzQTHh5eb3sXFxeCgoIabdNQn1CTIH19fS1erflcj1bw8vJytILVSFfb8PX1JTo6muKdPyKqqyx+l/3/v1sKUU3RjuW0aROpyYQJ2oxtQ0jXhgkLC6P/gIGU7l6BEAKvxP6Unz5A+bljDa5TtHOF8v+BAwcSGhTEVwX5DbbPLC/nj+IiJk6aZE/1OqiWNN3c3EhJSWHlypUWy1euXEm/fv3qXadv37512v/yyy+kpqbi6uraaJuG+pT8j06dOjlawWqkq+3MmPE8lflZ5P4yxyJx/nLKCVFdRe7K96nMO81zzz3nQMvG0Wps60O6Ns4Tjz/Ghcx08n/7FI8OvXHxjyD7u1epLMqp07b0yBaKN//vqQk3NzceeuQRFhUU8GM9o2Pzq6p45OxZwkNDmThxoqrbgVCRhQsXCldXVzF37lyxb98+8fDDDwsvLy+RkZEhhBDiqaeeEpMmTVLaHz16VHh6eorp06eLffv2iblz5wpXV1fxzTffKG3+/PNP4ezsLF555RWRnp4uXnnlFeHi4iI2btyo5qbUCyBUDqFdcUSMWop0tQ9TpkwRgHDxCxP+A6aI4OueEjP+865w8QsTgLj99tsdrdgoWo7txUjXpnn11VcFIDzC4oRv7xuFk4ePMLgahXePUSL42idF0IiHhGe7FAGIMddea3GMraysFJNuu00AIs3bW7wQFi5ej4wUkwMChK+rqwgOCBDbt29XfRtUP+K/8847IjY2Vri5uYmePXuKdevWKb+bPHmy6N+/v0X7tWvXih49egg3NzcRFxcn3n333Tp9fv311yI+Pl64urqKhIQEsXjxYrU3o15k0lQP6Wo/5s2bJ2Jj4wQYBCCeeeYZERMTKz766CNHqzWJ1mNbG+lqHWvXrhVjx40TTs7OAhDOzi7C6OGpHE97pqSK+fPni8rKyjrH2KqqKrFw4UJxRd++yu+C/P3FE088IU6ePNkq/rLKiQ3obSDQqVOnaNOmjaM1rEK62h/zSEYnJyelwK/W0UtsQbo2l/LycoqLi/H19cXJyYnCwkLc3NyUOprQ+DH2woULlJWVKeu3FnLC9ssIZ2dnRytYjXS1P56enrRv377Rx7O0hl5iC9K1ubi5uVkMQPP392/W+h4eHg4pbyYnbL+MMD8bpgekq3royVe6qoOeXLWGTJo2oIcirhKJRKJntHBWXBt5T9MG4uPjOXjwINXV1U3Wf9MCeqnWDtJVTfTkK13VQS+uBoOBmJgYTZ0ZyzNNGzBP+VZUVORgE+swTyWlB6SreujJV7qqgx5czVOVRkREONjEEpk0baBdu3YAytyQWkftkjn2RLqqh558pas66MHVnNhDQ0MdK3IRMmnaQHx8PAAHDhxwsIl1GBuZs1FrSFf10JOvdFUHPbiaJ5+PjY11sIklMmnagN6SZnJysqMVrEa6qoeefKWrOujB1VydZuDAgQ42sUQmTRtISkoCUErhaB1zySE9IF3VQ0++0lUd9OBqLotXXV3tYBNLZNK0gbi4OAB27tzpUA+JRCK51DAnzcjISAebWCKTpg2YHzMxF3zWOlrb+RpDuqqHnnylqzrowdVcEDw6OtrBJpbIpHkZoYeb/2akq3royVe6qoN0bTkyaV5G6OXRGJCuaqInX+mqDtK15cikKZFIJBKJlchp9GwkNjaWEydOUFVV1arlaVpCSUkJXl5ejtawCumqHnryla7qoHVXIQROTk5ERUWxf/9+Tblq+yivA1JSUoD/3bTWMpmZmY5WsBrpqh568pWu6qB11/379wPQv39/zbnKpGkj3bp1A2DVqlUONmma/Px8RytYjXRVDz35Sld10Lrrr7/+CtRMbKA1V5k0baRfv34ArFy50sEmTePm5uZoBauRruqhJ1/pqg5ad/3pp58AGD58uOZc5T1NGzFfezf/X8sIIXRRwgykq5royVe6qoPWXZ2cnBBCKC8tucozTRvZvHmzoxWsRrqqg55cQV++0lUdtO5a+wREa64yaUokEolEYiUyadpIeHi4UrqmvLzcwTaNEx4e7mgFq5Gu6qEnX+mqDlp2raysBP43fZ7WXGXStBFvb29uvvlmQPsjaL29vR2tYDXSVT305Ctd1UHLrr/99hsAN9xwA6A9V5k0beTw4cNK0ly0aJGDbRrHXNRVD0hX9dCTr3RVBy27fv755wBMnDgR0J6rTJp2wPys5meffeZgE4lEItE35uNoamqqg03qRz5yYiOFhYX4+voqQ6K1HE6zqx6QruqhJ1/pqg5adr34WKo1V3mmaSNnz551tILVSFd10JMr6MtXuqqDdG05MmnaSG5uLgDXXnstAAcOHHCkTqOYXfWAdFUPPflKV3XQquuhQ4cAGD16tLJMa64yadqIi4sLAPfddx8Ac+bMcaROo5hd9YB0VQ89+UpXddCq64cffgjA3/72N2WZ1lzlPU07UV1djbOzM05OTlRVVTlaRyKRSHSHh4cHJpOJiooKzSVLM/JM00bMUzyZ55+trq52pE6jaG06qsaQruqhJ1/pqg5adTWZTIDl2aXWXGXStJHaJ+rOzs4Amj3T1NNFBemqHnryla7qoEXXhk44tOaqatLMy8tj0qRJ+Pn54efnx6RJkxqtjVZRUcGTTz5Jly5d8PLyIjIykttvv53Tp09btBswYAAGg8HiZZ5goLUJCQlR/v/EE08AsGTJEoe4NEVtV60jXdVDT77SVR206PrDDz8AMH36dIvlWnNV9Z7miBEjyMzM5IMPPgDg7rvvJi4uju+//77e9gUFBdx4441MmzaNbt26kZeXx8MPP0xlZSVbt25V2g0YMIBOnTrxwgsvKMs8PDzw8/NTa1MaJC8vj4CAAACysrKIiIggJSXFwlcr1HbVOtJVPfTkK13VQYuuvXv3ZvPmzZw6dYrIyEhlueZchUrs27dPAGLjxo3Ksg0bNghA7N+/3+p+Nm/eLABx/PhxZVn//v3FQw89ZE/dFlN7+4QQAhAqhtUmLnbVMtJVPfTkK13VQYuuDR07teaq2uXZDRs24OfnR+/evZVlffr0wc/Pj/Xr11vdT0FBAQaDAX9/f4vlX3zxBcHBwSQnJ/PYY49RVFRkL3W7IDR2HV4ikUi0ip6Ol6olzaysLEJDQ+ssDw0NJSsry6o+TCYTTz31FBMnTrSYRunWW29lwYIFrF27lueee47Fixdz/fXXN9hPWVkZhYWFFq+ysrLmb1Q9xMfHW/z897//HYCffvrJLv3bk4tdtYx0VQ89+UpXddCa6y+//AJYPp9pRmuuzX4QZubMmcyaNavRNlu2bAH+N4dgbYQQ9S6/mIqKCm6++Waqq6vrTBgwbdo05f+dO3emY8eOpKamsn37dnr27Fmnr9mzZ9dxnj59OhMmTACgZ8+epKenc+HCBXx8fGjbti27d+8GIDY2lurqak6ePAlA9+7dOXz4MMXFxXh5eeHm5qbMAhQVFcVdd93FO++8w0MPPcTAgQPJyMigsLAQo9FIcnIy27ZtAyAyMhKj0cjRo0eV7cjMzCQ/Px83Nze6d++uDLUODw/H29tbme0/MTGRs2fPkpubi4uLCykpKWzevBkhBCEhIQQEBHDw4EGgZofLzc3l/PnzlJaWMnDgQLZu3UpVVRVBQUGEhoaSnp4OQMeOHSksLFSmrerduzfbt2+noqKCgIAAIiMj2bt3LwDt27entLSUM2fOADWTK+/ZsweTyYSfnx8xMTH89ddfAMTFxVFZWUlmZqYS7/3791NaWoq3tzft27dn165dAMTExACQnp6Ol5cX3bp148iRIxQXF+Pp6UlCQgLbt29X4u3i4kJGRgYAXbp04cSJExQUFGA0GuncubNybzkiIgJPT0+OHDkCQHJyMqdPnyYvLw9XV1d69uzJpk2bAAgLC8PX11eZnSQxMZFz586Rk5ODs7MzqampbNmyherqakJCQrhw4YKyD3Tq1Im8vDzOnz+PwWAgLS2Nbdu2UVlZSWBgIGFhYUq8O3ToQHFxsfIlMi0tjZ07d1JeXo6/vz9RUVHs2bMHgHbt2mEymZRBcSkpKezduxeTyYSvry9xcXEW+2xVVZUS7x49enDw4EFKSkrw9vbG1dVV8Y2OjsbJyYnjx48D0LVrV44dO0ZRUREeHh4kJiYq8W7Tpg1ubm4cO3ZMiffJkyfJz8/H3d2drl27Kp/98PBwvLy8lHgnJSWRlZVFbm5unXiHhobi5+enxDshIYHs7Gyys7O5cOECAwYMUOIdHBxMcHAw+/fvV/bZgoICzp07V2efDQwMJDw8nH379in7bElJiRLvXr16sXv3bsrKyvD39yc6OlrZZ9u2bUt5eTmnTp1S9tmmjhH79+/Hy8urzjGiU6dO7NixQ9lnnZ2dLeLtiGNEaWmpsg/UPkY4OTnRq1evVj9GmAdRjho1ik2bNlkcIyorK0lJSalzjDhx4gSAXY4Rta+INkWzBwKZd+bGiIuL48svv+SRRx6pM1rW39+fN954gzvuuKPB9SsqKhg/fjxHjx5l9erVBAUFNfp+Qgjc3d357LPPlERYm7Kysjpnlu7u7ri7uzfarzVs2rSpTsC1Onl7fa5aRbqqh558pas6aM21sWOm1lybfaZp/qbXFH379qWgoIDNmzeTlpYG1Gx8QUEB/fr1a3A9c8I8dOgQa9asaTJhAuzdu5eKigoiIiLq/b29EmR9mCc1qI2LiwuVlZWYTCaMRqMq79sS6nPVKtJVPfTkK13VQUuuTd0q05IroO4wz+HDh4uuXbuKDRs2iA0bNoguXbqI0aNHW7SJj48XS5YsEUIIUVFRIa699loRFRUldu7cKc6cOaO8ysrKhBBCHD58WMyaNUts2bJFHDt2TCxfvlwkJCSIHj16iMrKSjU3x2reeustAYhXXnnF0SoSiUSiaV599VUBiDfeeMPRKlahatLMyckRt956q/Dx8RE+Pj7i1ltvFXl5eZYCID7++GMhhBDHjh1Thh1f/FqzZo0QQogTJ06Iq6++WgQGBgo3NzfRvn178eCDD4qcnBw1N6VBtmzZUmdZeXm5Jh89qc9Vq0hX9dCTr3RVBy25mo+V5hOji9GSqxBCqDojbmBgIJ9//nmjbUSta9hxcXFN3geMjo5m3bp1dvGzB/VNmefq6uoAk6bR6vR+9SFd1UNPvtJVHbTo6ubmVu9yrblq7GKx/mjonuu4ceMA+P3331tTp1GsuT+sFaSreujJV7qqg1Zczc/sjx07tsE2WnE1I0uD2UhhYaHFM6RmTp48SUxMDB07dlQe/3A0DblqEemqHnryla7qoBXXLl26sGfPHo4dO0ZcXFy9bbTiakaeadqI+fmli4mOjgb+V4lcCzTkqkWkq3royVe6qoNWXM3PITeUMEE7rmZk0lSR7t27AygPEUskEomkBvMJRdeuXR1s0jxk0rSRjh07Nvi7Tz/9FIApU6a0kk3jNOaqNaSreujJV7qqgxZczTO7zZ8/v9F2WnCtjUyaNlJYWNjg77p06QLAxo0bW0unURpz1RrSVT305Ctd1UELruanIHr06NFoOy241kYmTRsxz8HYEOZLD1q4Lt+Uq5aQruqhJ1/pqg6OdjXfskpOTm6yraNdL0YmTZVZuHAhADfddJODTSQSiUQbTJw4Efjf8VFPyEdOWgGtTuAukUgkjkDPx0R5pmkj5hI0jTFq1CgAVq1apbZOo1jjqhWkq3royVe6qoMjXVevXg3A8OHDrWqvtbjKpGkjFRUVTbYxjw4bMmSIyjaNY42rVpCu6qEnX+mqDo50HTFiBACffPKJVe21FleZNG0kICCgyTa1S6k5ch5Fa1y1gnRVDz35Sld1cJRrVVUV5eXlQE3xcWvQWlxl0rSRyMhIq9q98sorAMycOVNFm8ax1lULSFf10JOvdFUHR7m+8MILAPzzn/+0eh2txVUmTRvZu3evVe0ef/xxAF566SU1dRrFWlctIF3VQ0++0lUdHOVqTppPPfWU1etoLa4yabYSTk5OyjcmOa2eRCK53DBPmxcQEICTk35Tj37NNUL79u2tbvvrr78CMHDgQLV0GqU5ro5GuqqHnnylqzo4wtV83GtuuUStxVUmTRspLS21um1CQgIAZ86cobKyUi2lBmmOq6ORruqhJ1/pqg6t7VpVVcWpU6cA62YBqo3W4iqTpo2cOXOmWe3/85//APDII4+oodMozXV1JNJVPfTkK13VobVdn3jiCaB5A4DMaC2uMmm2MtOnTwfg7bffdrCJRCKRtA6vv/46AE8//bSDTWxHTqNnI1VVVTg7OzdrnWuuuYY1a9awdOlSxo4dq45YPbTE1VFIV/XQk690VYfWdP3pp58YOXIkV199tVLZpDloLa7yTNNGzJXHm8OyZcsAGDdunJ1tGqclro5CuqqHnnylqzq0puvIkSMB+Pbbb1u0vtbiKpOmjZhMpmav4+vri6urKwD79++3t1KDtMTVUUhX9dCTr3RVh9ZyPXjwIFDzyJ2/v3+L+tBaXGXStBE/P78WrffXX38BkJiYaE+dRmmpqyOQruqhJ1/pqg6t5dqtWzfAtrNFrcVV3tO0kdLSUjw9PVu0rrk8zrlz5wgJCbGnVr3Y4traSFf10JOvdFWH1nDNzs5Wjmu2pBmtxVWeadqI+YyxJaxduxaA3r1728mmcWxxbW2kq3royVe6qkNruPbt2xf4XymwlqK1uMqk6UD69+8PwLFjxygsLHSwjUQikdiH4uJiDh8+DDhuBjS1kEnTRuLi4mxa/7vvvgPg6quvtoNN49jq2ppIV/XQk690VQe1Xc2J8ptvvrG5L63FVSZNG7F1OrwxY8YAsGvXLoqKiuyh1CCOmLqvpUhX9dCTr3RVBzVdi4uL2bp1KwA33HCDzf1pLa4yadpIZmamzX0sXrwYgH79+tncV2PYw7W1kK7qoSdf6aoOaroOGDAAgK+++sou/WktrjJpaoDrr78eqBmWnZ+f71gZiUQiaSH5+fls27YNgJtuusnBNuogHzmxkYqKCmWiAlv48ccfGTVqFFFRUZw8edIOZnWxl2trIF3VQ0++0lUd1HKNi4vj+PHj/PDDD4waNcoufWotrvJM00bsNaOPeaqpzMxM1ZJma84+ZCvSVT305Ctd1UEN19OnT3P8+HEAuyVM0F5cVU2aeXl5TJo0CT8/P/z8/Jg0aVKTlx+nTJmCwWCwePXp08eiTVlZGQ888ADBwcF4eXlx7bXXOuy6tz1rvW3evBmAmJgYu/VZG63VpWsM6aoeevKVruqghmubNm0A+PPPP+3ar9biqmrSnDhxIjt37mTFihWsWLGCnTt3MmnSpCbXGz58OGfOnFFeP/74o8XvH374YZYuXcrChQv5448/KC4uZvTo0VRVVam1KQ3i7e1tt7569eql/N+cQO2JPV3VRrqqh558pas62Nt1+/btyv/tPaBRa3FV7Z5meno6SUlJbNy4UZnxZuPGjfTt25f9+/cTHx9f73pTpkwhPz9fqQRyMQUFBYSEhPDZZ58xYcIEoOayQHR0ND/++CPDhg1TY3MaxGQyYTQa7dbfyZMnlTNNe/9p7O2qJtJVPfTkK13Vwd6u5ilBjx07ZvfnKrUWV9XONDds2ICfn5/FFHF9+vTBz8+P9evXN7ru2rVrCQ0NpVOnTkybNo1z584pv9u2bRsVFRUMHTpUWRYZGUnnzp2b7FcNdu3aZdf+oqOj6dy5MwDz5s2za9/2dlUT6aoeevKVrupgT9f58+cD0LFjR1UmItBaXFVLmllZWYSGhtZZHhoaSlZWVoPrjRgxgi+++ILVq1fzn//8hy1btnDNNddQVlam9Ovm5kZAQIDFemFhYQ32W1ZWRmFhocXL3J8WMV+anTp1KtXV1Q62kUgkkvoRQnDHHXcA2ktuauHS3BVmzpzJrFmzGm2zZcsW4H+n7LURQtS73Iz5kitA586dSU1NJTY2luXLlyvPM9ZHY/3Onj27jvP06dOV9+rZsyfp6elcuHABHx8f2rZty+7duwGIjY2lurpaGdHavXt3Dh8+THFxMV5eXkRGRrJp0yYAoqKicHZ2VkaQde3alYyMDAoLCzEajSQnJyvPMEVGRmI0Gjl69KiyrZmZmeTn5+Pm5sZjjz3Ga6+9xjXXXMMnn3yCt7e3MpdjYmIiZ8+eJTc3FxcXF1JSUti8eTNCCEJCQggICFDq2MXHx5Obm8v58+eVLwpbt26lqqqKoKAgQkNDSU9PB2q+KRYWFnL27FmgZiL57du3U1FRQUBAAJGRkezduxeA9u3bU1paypkzZwBITU1lz549mEwm/Pz8iImJUSZajouLo7KyUhms1bNnT/bv309paSne3t60b99e+cCZL02bTCY2bdpEt27dOHLkCMXFxXh6epKQkKDcP4mKisLFxYWMjAwAunTpwokTJygoKMBoNNK5c2dlZpKIiAg8PT05cuQIAMnJyZw+fZq8vDxcXV3p2bOn8ncMCwvD19eXQ4cOKfE+d+4cOTk5ODs7k5qaypYtW6iuriYkJISgoCBl3U6dOpGXl8f58+cxGAykpaWxbds2KisrCQwMJCwsTIl3hw4dKC4uVr7spaWlsXPnTsrLy/H39ycqKkopqdSuXTtMJhOnT58GICUlhb1792IymfD19SUuLs5in62qqlLi3aNHDw4ePEhJSQne3t5EREQovtHR0Tg5OVnss8eOHaOoqAgPDw8SExOVeLdp0wY3NzeOHTumxPvkyZPk5+fj7u5O165dlc9+eHg4Xl5eSryTkpLIysoiNze3TrxDQ0Px8/NT4p2QkEB2djbZ2dmUl5cDKPEODg4mODhYGVHZsWNHCgoKlKtRtffZwMBAwsPD2bdvn7LPlpSUKPHu1asXu3fvpqysDH9/f6Kjo5V9tm3btpSXl3Pq1Clln23qGGHeZy8+RnTq1IkdO3Yo+6y9jhHdu3dXvmCHh4c36xgRGBioxL/2McLJyYlevXpZfYx44403ABg/fjwnT55U5RhhMBgwmUx1jhEnTpwAsMsxojlFM5p9T9O8MzdGXFwcX375JY888kid0bL+/v688cYbyrcTa+jYsSN33XUXTz75JKtXr2bQoEHk5uZanG1269aNsWPH1pvQy8rK6pxZuru74+7ubrVDQ5w5c4aIiAib+6kP85eArKwswsLCbO5PTVd7I13VQ0++0lUd7OF67tw55bhUXV3d6MmQLWgtrs2+PBscHExCQkKjL6PRSN++fSkoKLAYBbpp0yYKCgqaNboqJyeHkydPKkFLSUnB1dWVlStXKm3OnDnDnj17GuzX3d0dX19fi5c9Eib879uOGvz6669AzbdIe6Cmq72RruqhJ1/pqg72cDUnzJUrV6qWMEF7cVXtnmZiYiLDhw9n2rRpbNy4kY0bNzJt2jRGjx5tMXI2ISGBpUuXAjUT/T722GNs2LCBjIwM1q5dy5gxYwgODmbcuHFATRXvqVOn8uijj/Lrr7+yY8cObrvtNrp06cLgwYPV2hyHcM011yjJ/euvv3awjUQikdSwZMkS5f+X2nG3SYSK5OTkiFtvvVX4+PgIHx8fceutt4q8vDyLNoD4+OOPhRBClJaWiqFDh4qQkBDh6uoqYmJixOTJk8WJEycs1rlw4YK4//77RWBgoPDw8BCjR4+u06a1uHDhgqr9FxQUCEAAorKy0qa+1Ha1J9JVPfTkK13VwRbXqqoq5Zh08fFcDbQWV1WT5uXAnj17VH+Pf/zjHwIQqampNvXTGq72Qrqqh558pas62OJ65ZVXCkA8/fTTdjRqGK3FVc49ayPFxcWqv8eLL74I1Ix6NY/Cawmt4WovpKt66MlXuqpDS1137tzJH3/8AcDLL79sT6UG0VpcZdK0EU9Pz1Z5H/Nw/J49e7Z4pqDWcrUH0lU99OQrXdWhJa5CCHr06AHAgQMH7K3UIFqLqywNZiOtWbbmlltuYeHChYwdO1YZPNUctFZipzGkq3royVe6qkNLXM3HnxtuuIFvvvlGJbO6aC2u8kzTRmpPVKw2CxYsAGDZsmXKQ+/NoTVdbUW6qoeefKWrOjTXdc+ePSxcuBBo/ZH8WourTJo6w5wsu3TpYvcJ3SUSieRihBB06dIFgL/++kvVZzL1gEyaNhIVFdWq75ecnMyUKVMAGDBgQLPWbW1XW5Cu6qEnX+mqDs1xHTJkCFBzedZcTKI10VpcZdK0EReXZk/fazMff/wxAL/99psya5A1OMK1pUhX9dCTr3RVB2td16xZoxxjvvzySzWVGkRrcZVJ00bMEwG3NuYJ5AcPHozJZLJqHUe5tgTpqh568pWu6mCNa1lZGddccw3g2KnstBZXmTR1SlRUlFJhwMPDw8E2EonkUsNc+Hn27NlER0c72EY7yEdObKS0tNShzxEZjUbKysp47rnneOGFFxpt62jX5iBd1UNPvtJVHZpyfeGFF5gxYwYuLi5UVFS0olldtBZXeaZpI46egb+oqAiomTXIXJuuIRzt2hykq3royVe6qkNjrvv27WPGjBkAlJSUtJZSg2gtrjJp2khBQYFD39/V1VWZWq9r166Nfit0tGtzkK7qoSdf6aoODblWVFSQnJwMwObNm3Fzc2tNrXrRWlxl0rQR83V/R9K9e3eefvppgEZ3ci24Wot0VQ89+UpXdWjI1bz8qaeeolevXq2p1CBai6u8p2kjVVVVODs7O1oDAG9vb0pKSrjvvvt455136vxeS65NIV3VQ0++0lUd6nN98MEHefvttzVxH7M2WourPNO0ka1btzpaQcF8GWPOnDmsWLGizu+15NoU0lU99OQrXdXhYtdffvmFt99+G4ALFy44QqlBtBZXmTQvIZydnZVnmkaMGMHp06cdKySRSDTP2bNnGTZsGFDzTKTWJhPQGjJp2khERISjFSyIjY1lyZIlALRp04bKykrld1pzbQzpqh568pWu6mB2raqqIjw8HIBFixYRGxvrSK160VpcZdK0ES09P2Rm3LhxTJs2DcCipI4WXRtCuqqHnnylqzqYXc1nlXfddRfjx493pFKDaC2uMmnayJEjRxytUC8ffPABISEhAMqlF6261od0VQ89+UpXdThy5AijRo0CwNfXlw8//NDBRg2jtbjKpHkJc/bsWaDmJv8///lPB9tIJBKt8Omnn/Ljjz8CkJ+f71gZnSEfObGR4uJivL29Ha3RIMXFxfj4+ACwcOFCJkyY4GAj69B6XGujJ1fQl690tT8//vijcpZZVFSkeWetxVWeadqI1keoent7c+zYMQBuvvlmdu/e7WAj69B6XGujJ1fQl690tS9//fWXkjAPHTqkqWTUEFqLq0yaNpKXl+dohSaJi4tj3bp1AHTr1o1Tp0452Khp9BBXM3pyBX35Slf7kZWVRdeuXYGaZ7k7dOjgYCPr0FpcZdK0kdqjU7XM1VdfzYsvvgjUlBUzT/SuVfQSV9CXK+jLV7rah6KiIuXRjY8//pjevXs72Mh6tBZXeU/zMuPFF1/k+eefB8BkMuHu7u5gI4lEoibl5eXK53zGjBnMnDnTsUI6R55p2simTZscrWA1mzZt4rnnnmPq1KlAzUTIVVVVDraqH73FVU/oyVe62kZVVZWSMO+44w4lYWrRtSG05iqT5mXIRx99xMiRI4Gah5vlxQaJ5NJDCKFMXjB48GDmzZvnYKNLA5k0bSQsLMzRClZT23X58uX07NkTACcnJ80lTr3GVQ/oyVe6tgwhBE5ONYf3Hj16sHLlSovfa8m1KbTmKpOmjfj6+jpawWoudt22bRudOnUCtJc49RxXraMnX+nafGonzJiYGLZv316njVZcrUFrrjJp2sihQ4ccrWA19bkeOHCANm3aANpKnHqPq5bRk690bR61E2ZkZCTHjx+vt50WXK1Fa64yaUrIzMwkJiYG0FbilEgk1lM7YYaFhenieWw9Ih85sZHCwkLNXT5oiKZcO3XqpHyrq6ysdGi19EsprlpDT77S1Tqqq6uVz2tcXJwyC1hDyLi2HFXPNPPy8pg0aRJ+fn74+fkxadKkJicHNhgM9b5effVVpc2AAQPq/P7mm29Wc1Ma5Ny5cw5535bQlOvBgwdJSUkBakbVlpWVtYZWvVxKcdUaevKVrk1TXl6uJMzu3bs3mTBBxtUWVE2aEydOZOfOnaxYsYIVK1awc+dOJk2a1Og6Z86csXjNmzcPg8HADTfcYNFu2rRpFu3ef/99NTelQXJychzyvi3BGtetW7cqj6MYjUYKCwvV1qqXSy2uWkJPvtK1cYqKipTnMAcPHsyOHTusWk/GteW4qNVxeno6K1asYOPGjcqUTR9++CF9+/blwIEDxMfH17ueuYq4mW+//ZaBAwfSrl07i+Wenp512joCR17CbC7Wui5fvpy77rqLuXPn4ufnR2ZmpjJYqLW4FOOqFfTkK10b5vTp08rn8s4772Tu3LlWryvjagNCJebOnSv8/PzqLPfz8xPz5s2zqo+srCzh4uIivvjiC4vl/fv3F8HBwSIoKEgkJSWJRx99VBQWFtpDW1KLl156SQACEDt37nS0jkQi+f/s2rVL+WzOmDHD0TqXFapdns3KyiI0NLTO8tDQULKysqzq45NPPsHHx4frr7/eYvmtt97KggULWLt2Lc899xyLFy+u06Y2ZWVlFBYWWrzsdb9uy5YtdumnNWiu67PPPsunn34K1Nwr+fbbb9XQqpdLOa6ORk++0rUuP/zwA926dQNqikm3ZC5ZGdeW0+zLszNnzmTWrFmNtjFvpMFgqPM7IUS9y+tj3rx53HrrrRiNRovl06ZNU/7fuXNnOnbsSGpqKtu3b1dmuanN7Nmz6zhPnz5dKcjcs2dP0tPTuXDhAj4+PrRt21apOxkbG0t1dTUnT54EapLH4cOHKS4uxsvLi8rKSmVuxKioKJydnZVno7p27UpGRgaFhYUYjUaSk5PZtm0bUPMMldFo5OjRo8p2ZGZmkp+fj5ubG927d2fz5s1AzSVrb29vDh8+DEBiYiJnz54lNzcXFxcXUlJS2Lx5M0IIQkJCCAgI4ODBgwDEx8eTm5vL+fPnlRI7W7dupaqqiqCgIEJDQ0lPTwegY8eOFBYWcvbsWQB69+5NcnIy77//Pn/7298YO3Ys99xzD1OmTKF9+/aUlpZy5swZAFJTU9mzZw8mkwk/Pz9iYmL466+/gJrRfJWVlWRmZirx3r9/P6WlpXh7e9O+fXt27doFoDz6kpOTw6ZNm+jWrRtHjhyhuLgYT09PEhISlIe1o6KicHFxISMjA4AuXbpw4sQJCgoKMBqNdO7cma1btwIQERGBp6cnR44cASA5OZnTp0+Tl5eHq6srPXv2VP6OYWFh+Pr6KiOJExMTOXfuHDk5OTg7O5OamsqWLVuorq4mJCSEsrIyZd1OnTqRl5fH+fPnMRgMpKWlsW3bNiorKwkMDCQsLEyJd4cOHSguLla+RKalpbFz507Ky8vx9/cnKiqKPXv2ANCuXTtMJpNSWzAlJYW9e/diMpnw9fUlLi7OYp+tqqpS4t2jRw8OHjxISUkJ3t7eFvtsdHQ0Tk5OFvvssWPHKCoqwsPDg8TERCXebdq0wc3NTRlo0qVLF06ePEl+fj7u7u507dpV+eyHh4fj5eWlxDspKYmsrCxyc3PrxDs0NBQ/Pz8l3gkJCWRnZ5Odna0MHDTHOzg4mODgYPbv36/sswUFBcpgkd69e7N9+3YqKioIDAwkPDycffv2AdC+fXtKSkqUePfq1Yvdu3dTVlaGv78/0dHRyj7btm1bysvLlcc2rDlGmPfZi48RnTp1Uu412nqM+Mc//sGbb74JwNq1azEajWzatKnZx4ja+2ztY4STkxO9evVq1jHCHO+AgAAiIyPZu3evEm97HCOKioowmUx1jhEnTpwAsMsxollVX5p7anr+/HmRnp7e6OvChQs2X5797bffrL4sWF1dLVxdXcXChQvr/b3JZBIFBQUWL5PJ1GS/1nDkyBG79NMa2OJ67Ngx5XLQ4MGD7WhVP5dLXB2Bnnyl6/8YOXKk8hk8evSoTX3JuLacZp9pmr/pNUXfvn0pKChg8+bNpKWlATWz1RcUFNCvX78m1587dy4pKSnKZYjG2Lt3LxUVFUq9uItxd3dXrQRWYGCgKv2qgS2ucXFxFBcX4+3tzapVqzAYDFRUVCgTQtubyyWujkBPvtK15pnp2jUli4qK8Pb2tqlPGdeWo9o9zcTERIYPH860adPYuHEjGzduZNq0aYwePdpi5GxCQgJLly61WLewsJCvv/6au+66q06/R44c4YUXXmDr1q1kZGTw448/ctNNN9GjRw+uuOIKtTanQQ4cONDq79lSbHX18vJSLklCTXFYtWYduZzi2troyfdyd83KylISZnBwMNXV1TYnTJBxtQVVn9P84osv6NKlC0OHDmXo0KF07dqVzz77zKLNgQMHKCgosFi2cOFChBDccsstdfp0c3Pj119/ZdiwYcTHx/Pggw8ydOhQVq1apb2hyZcgBoOBc+fOce+99wI19wxWrFjhYCuJ5NJjxYoVytWzu+66S7lPLnEscho9G8nLyyMgIMDRGlZhb9elS5cqo5bvvvtuu04wcTnHVW305Hu5uj744IO8/fbbAHzzzTd1Jnexlcs1rvZATthuI+YRqXrA3q7jxo1TRgF+8MEHGAwGysvL7dL35RxXtdGT7+XmWlFRgcFgUBJmRkaG3RMmXH5xtScyadrI+fPnHa1gNWq4xsTEUFlZiY+PD1Az6Mo8FN8WLve4qomefC8n17179+Lm5gaAh4cHFRUVxMbG2kOtDpdTXO2NTJo2oqd7DGq5Ojs7U1hYyHPPPQfUPDf1/PPP29SnjKt66Mn3cnGdMWMGnTt3BuCpp56itLRUtZHpcPnEVQ3kPU2JXdm1axfdu3dXfi4tLcXDw8NxQhKJhjGZTBafj23bttU7QYtEO8gzTRsxz96hB1rDtVu3bpSXlyvD4j09PVm5cmWz+5FxVQ89+V7KrqtWrVISppubG+Xl5a2WMC/luKqNTJo2UllZ6WgFq2ktV1dXV4qKinjjjTcAGDp0KP369aM5FzVkXNVDT76XoqsQggEDBjBkyBAA/vOf/1BWVmYxgYHaXIpxbS1k0rQRrc1W0Rit7frwww8rkx9s2LABJycnZf7IppBxVQ89+V5qrrt378bJyYl169YBkJmZySOPPKK2Wh0utbi2JjJp2khYWJijFazGEa6RkZEIIZgyZQpQM+H9tdde2+RZp4yreujJ91JxFUIwbtw4ZVrQyZMnI4Ro9Tq1Zi6VuDoCmTRtxDz7vx5wpOvHH3+svP/333+Pk5NToyV/ZFzVQ0++l4Lr9u3bcXJyYtmyZUDNoyXz589vPbF6uBTi6ihk0pS0GgkJCQghmDhxIlBTCislJYWqqioHm0kk9qeqqorU1FRSUlIAmDhxIkIIkpKSHGwmsQX5yImN5OTkEBQU5GgNq9CS69GjR2nfvr3y85dffmkx17CWXJtCT66gL1+9un7zzTfcdNNNyu8OHjxIx44dHaVWB73GVQvIM00bKS4udrSC1WjJtV27dgghmDFjBlDzLdxgMCgFa7Xk2hR6cgV9+erNNSsrC4PBoCTM559/HiGEphIm6C+uWkImTRsxV4HXA1p0nTlzJoWFhXh5eQE1A4dGjx7N6dOnHWxmPVqMa2PoyVcvrtXV1UyePFmpSmI0GsnPz2fWrFkONqsfvcQVtOcqk6bE4fj4+FBcXMzatWsBWL58Of369bNr1RSJRC0++ugjnJ2dlcdI1qxZw4ULF/Dz83OwmUQN5D1NGxFCaG5uxIbQi+tTTz3Fv/71L+XnjRs30rt3bwcaNY5e4mpGT75adt20aRN9+vRRfn7iiScs9lsto+W4XozWXOWZpo3s3LnT0QpWoxfXV155hY0bN9KjRw8A+vTpg8FgUMqQaQ29xNWMnny16Hry5EkMBoOSMJOTk7lw4QI333yzg82sR4txbQitucqkaSP2qh/ZGujJFWqebzt58qTyc1xcHOHh4eTk5DjQqi56i6uefLXkmpubS3h4ODExMcqykydPsmfPHoxGo6Zcm0K6thyZNG3E39/f0QpWo0fXqKgohBDKpM1nz54lODiYpKQkioqKHGj4P/QUV9CXrxZcCwsL6dy5M0FBQZw9exaALVu2IIQgKipKaacFV2uRri1H3tO0kZKSEmXkp9a5FFxXrVqlTHQNkJiYyPr16x36wdJTXEFfvo50LSgo4Oqrr7Yoqv7TTz8xfPjwetvLuKqD1lzlmaaN7Nmzx9EKVnMpuA4ePBghBEuXLgVqptgKCAggKipKecaztdFTXEFfvo5wPXfuHNHR0fj7+ysJc9myZQghGkyYIOOqFlpzlUlTokvGjh2LEILly5cDcOrUKSIjIzEYDOzdu9fBdhI9snfvXgwGA2FhYWRmZgLw3XffIYTguuuuc7CdRCvIpGkj7dq1c7SC1VyKriNHjkQIwZ9//qks69y5MwaDgcWLF6ulZ4Ge4gr68m0N1yVLlmAwGOjcubOybN26dQghGDNmjNX9yLiqg9ZcZdK0EZPJ5GgFq7mUXc1Frg8dOoSvry8AN954IwaDgbvvvpuKigo1NAF9xRX05auWa2VlJffffz8Gg4EbbrgBqJnF59ChQwghuPrqq5vdp4yrOmjNVSZNG9HTdG+Xg2uHDh0oKCigqKiIoUOHAvDhhx/i5uZGUFCQKvdH9BRX0JevvV3T09MJDg7G1dWVd955B4ChQ4dSWFjIhQsX6NChQ4v7vpzjqiZac5VJU3JJ4u3tzc8//4wQgrfffhuoec6uS5cuGAwGHn/88f/X3rkHRXWecfi3wi53Vm6yoAKGKAuKF0BBkwoq3jJWrUYkGksyxko6pmLsWDNOFDuNRZuovSSNZhRoItFYoP3DmIQoWg0YUVAhXBRFMCIicpfb4r79w+HU46647HJYtnmfmZ1xv33Pt8++fpz3XL5zjqR7n8zgobu7G5s2bYJMJkNQUJBwne/evXuh1Wrx9ddfw8nJycyWjKXAl5yYSHd3N6ytrc2tYRA/ddfy8nLMnj0bN2/eFLV/+eWXmD9/vtH9WlJeAcvyNcX1+PHjeOmll0RtPj4++OabbxAQENAfeiJ+KnkdaAabK+9pmoglzdT8qbs+//zzqKiogFarxd69e4X2l156CTKZDAEBASgsLOxzv5aUV8CyfPvqeuXKFQQEBEAmk4kK5vvvvw+tVovKykpJCibw/51XczLYXLlomshgO0ndG+z6CJlMhvXr14OIcP/+fbz88ssAHj0oePz48ZDJZAgJCTH4j9WS8gpYlq8hrkVFRQgJCYFMJsOECRNw9epVAMAvfvEL1NfXg4iwceNGyW/6/f+W18HCYHPlomkiPTM1LQF21cXV1RVHjx4FEaGsrEx4mkpBQYFw6Yqvry9Onjxpdtf+wpJ8n+Z64sQJ+Pn5QSaTITg4GAUFBQCAyZMn4+rVqyAiZGRkwMXFxeyugxF2NR4+p2ki7e3tsLOzM7eGQbCr4RQXF+ONN95Abm6uzme///3v8fbbbwu39jK3a1+xJN8e17a2NuzatUvvQ50jIiKQnJwMtVptBsP/YYl5tQQGmyvvaZrI4/elHOywq+EEBQUhJycHRITbt29jxYoVwmdbt26Fo6MjZDIZ1Go19uzZA61Wa0bbvmHu3BpCz55iz/lJBwcHUcFcsWIF7ty5AyJCbm6u2QsmYBl57YFdjYeLJsM8A29vbxw6dAhEBI1Gg3379sHR0REAUFZWhi1btsDKykq4pOEf//gHX87SRzQaDdLS0oRD4kOGDMHSpUuFR8PZ2dlh//796O7uBhHh0KFDUKlUZrZmfopw0TQRX19fcysYDLuajrW1NX71q1+hpaUFRITa2lq8+eabwuclJSWIi4uDQqGATCaDTCZDfHw8Ll++bEZrMYMht5cvX0Z8fLyQI4VCgZUrV4omXyUkJKCwsBBEhLa2NqxZswZWVlZmtO6dwZBXQ2FX45G0aL733nuYNm0a7O3tDX50ExEhMTER3t7esLOzQ1RUlM4sxs7OTrz11ltwd3eHg4MDFi5cKNxgeaB5+PChWb7XGNi1//Hw8MCWLVtARCAi1NXVYfv27bC3txdi9u3bh4kTJwoFQiaT4eWXX0ZGRoZZZgYOZG7b29uRmZmJ5cuXi37/xIkTsW/fPiHO0dERiYmJqK2tFXK5Z8+eAZ3IYyqWMmYBdjUFSYtmV1cXli1bJtoSfxa7du3C7t278be//Q15eXlQqVSYPXu26IHDCQkJyMzMxOHDh3H27Fm0trZiwYIFZkmuuYq1MbCrNDzu6ubmhq1bt+LBgwcgImi1WmRnZ+OVV14RLZOeno6lS5fCzs5OVEx+9rOf4Q9/+APOnz+P7u5uyX37A41Gg9zcXOzYsQORkZGi32Nvb48lS5bgiy++EC0TGxuL06dPQ6vVgojQ0tKCbdu2wcPDQ1JXKWFXaRhsrpLeZqHnxH1KSopB8USEvXv3YsuWLViyZAkAIDU1FZ6enkhLS8PatWvR1NSEAwcO4NNPP0V0dDQA4LPPPsPIkSPx7bffYu7cuZL8FoYxBplMhqioKERFRSEtLU1oLysrw+eff44vvvgCJSUlQvvZs2dx9uxZvPvuu3r78/HxwYQJExAYGIjAwEAEBATAz88Pnp6eGDKk/7aBtVot7t27h/LycpSXl6OsrAxXrlzBlStXhPOMhhAYGIiYmBgsW7YMQUFBkl8ryTBSMyCXnKSkpCAhIQGNjY29xt24cQP+/v7Iz8/HpEmThPZFixZh6NChSE1NxcmTJzFr1izU19eLDt1MmDABixcv1jslvbOzE52dnaI2Gxsb2NjYmPbD8GhvWqFQmNzPQMCu0tCfro2NjThx4gSysrJw5swZFBcX90u/UqJWqzFz5kzMmDEDM2bMgJubW7/1/VMdB1LDrsYzeG7oB6CmpgYA4OnpKWr39PREZWWlEKNQKHTOdXh6egrLP8kf//hHnWK6YcMGLF++HAAQEhKCkpIStLe3w8nJCaNGjRKmOfv6+kKr1Qpb1xMnTkR5eTlaW1vh4OCAhw8fCuelRowYASsrK8F1/PjxuHnzJpqbm2Fra4uxY8fi4sWLAB7NyLS1tcWNGzcAPHoG5I8//ojGxkYoFApMnDgR58+fBwCoVCo4OjqivLwcwKOt97t376K+vh7W1tYIDQ3F+fPnQUTw8PCAi4uLcFeUgIAA1NfX4969e2hpaUF0dDQuXLiAhw8fws3NDcOGDRP2dEaPHo3m5mbcvXsXABAeHo78/HxoNBq4uLjA29tbOL/s7++PtrY23LlzBwAQFhaGoqIidHR0QKlUwsfHR7glnZ+fH7q7u4XDLCEhISgtLUVbWxscHR3h7+8vTJTx8fEB8OguL87OzpgwYQKuX7+O1tZW2NvbQ61WIz8/X8i3tbW1cC/Z4OBgVFVVoampCba2thg3bhwuXLgAAPDy8oK9vT2uX78OABg7diyqq6vR0NAAuVyOkJAQfP/998JYcnZ2xrVr14R819bW4v79+7CyskJYWBjy8vKg1Wrh4eGBhoYG4VDqmDFj0NDQgHv37kEmk2HKlCm4ePEiuru74erqCk9PTyHfzz//PFpbW4VxO2XKFFRUVGDEiBHYsGEDPvjgA+GpLM899xw6Ojpw+/Zt3L9/HwqFAidPnsSNGzdQV1eHe/fuobKyEs3NzXr/BvqCi4sL3NzcMHz4cEyaNAlyuRwjR47Eiy++iOHDh6OiokLI961bt9DY2AgbGxuMHz8eeXl5KC8vh0qlgoODg5DvoKAg1NTUoL6+Xiffw4YNg1KpFPKtVqtRV1eHuro6Ycz25Nvd3R3u7u4oLS0F8GjMNjU1oba2FoB4zLq6ukKlUgkbHv7+/njw4IGQ78mTJ+PKlSvo7OzE0KFDMXLkSGHMjho1Cl1dXbh9+zYAw9YRP/zwA5ydnXXWEWPGjBFuvjBY1hH19fXC6azH1xFDhgzB5MmTB9U6oqOjA+Hh4TrriKqqKgDol3VEz01NDIL6yLZt2whAr6+8vDzRMsnJyaRUKp/Z93fffUcAqLq6WtT+xhtv0Ny5c4mI6NChQ6RQKHSWjY6OprVr1+rtt6Ojg5qamkSvjo4OA39x75w7d65f+hkI2FUaLMmVyLJ82VUa2NV4+rynuW7dOsTGxvYa4+fn19duAUC47qqmpgZeXl5Ce21trbD3qVKp0NXVhYaGBtHeZm1tLaZNm6a33/46FKuPnuv1LAF2lQZLcgUsy5ddpYFdjafPRbPn8IgUjBo1CiqVCllZWcI5za6uLpw+fRo7d+4EAISGhkIulyMrKwsxMTEAgDt37qCoqAi7du2SxKs3THlo7UDDrtJgSa6AZfmyqzSwq/FIeslJVVUVLl26hKqqKjx8+BCXLl3CpUuX0NraKsSo1WpkZmYCeDTTMCEhATt27EBmZiaKiorw2muvwd7eXriNmVKpxOrVq7Fx40acOHECBQUFePXVVxEcHCzMph1ILl26NODfaSzsKg2W5ApYli+7SgO7Go+kE4G2bt2K1NRU4X3P3mN2djaioqIAPJp639TUJMRs2rQJ7e3t+PWvf42GhgaEh4fjm2++ET1Zfc+ePbC2tkZMTAza29sxa9YspKSkDOq7hTAMwzCWDz/lxESqq6vh7e1tbg2DYFdpsCRXwLJ82VUa2NV4+N6zJtKfF5RLDbtKgyW5Apbly67SwK7GM7hsLJCe660sAXaVBktyBSzLl12lgV2Nh4smwzAMwxgIn9M0kcH2VPHeYFdpsCRXwLJ82VUa2NV4eE/TRHpuKWYJsKs0WJIrYFm+7CoN7Go8XDRN5PFHlg122FUaLMkVsCxfdpUGdjUeLpomMpgOGzwLdpUGS3IFLMuXXaWBXY2Hz2maiEajgVwuN7eGQbCrNFiSK2BZvuwqDexqPLynaSI9j6CxBNhVGizJFbAsX3aVBnY1Hi6aDMMwDGMgXDRNoLOzE8ePH0dnZ6e5VZ4Ju0qDJbkCluXLrtLArqbB5zRNoLm5GUqlEk1NTXB2dja3Tq+wqzRYkitgWb7sKg3sahq8p8kwDMMwBsJFk2EYhmEMhIsmwzAMwxgIF00TsLGxwbZt22BjY2NulWfCrtJgSa6AZfmyqzSwq2nwRCCGYRiGMRDe02QYhmEYA+GiyTAMwzAGwkWTYRiGYQyEiybDMAzDGAgXzV547733MG3aNNjb22Po0KEGLUNESExMhLe3N+zs7BAVFYUffvhBFNPZ2Ym33noL7u7ucHBwwMKFC/Hjjz+a5NrQ0IBVq1ZBqVRCqVRi1apVaGxs7HUZmUym9/WnP/1JiImKitL5PDY2dsBdX3vtNR2PiIgIUYwUeTXGV6PR4He/+x2Cg4Ph4OAAb29v/PKXv0R1dbUorj9y+9FHH2HUqFGwtbVFaGgozpw502v86dOnERoaCltbWzz33HP4+OOPdWLS09MRFBQEGxsbBAUFITMzs09O/eGakZGB2bNnw8PDA87Ozpg6dSq+/vprUUxKSore8dvR0TGgrqdOndLrUVpaKoobDHnV93ckk8kwduxYIUaqvP7nP//Bz3/+c3h7e0Mmk+Ff//rXM5cx53h9KsQ8la1bt9Lu3bvp7bffJqVSadAySUlJ5OTkROnp6VRYWEjLly8nLy8vam5uFmLi4+Np+PDhlJWVRfn5+TRjxgyaMGECdXd3G+06b948GjduHOXk5FBOTg6NGzeOFixY0Osyd+7cEb0OHjxIMpmMrl+/LsRERkbSmjVrRHGNjY1GexrrGhcXR/PmzRN53L9/XxQjRV6N8W1sbKTo6Gg6cuQIlZaWUm5uLoWHh1NoaKgoztTcHj58mORyOX3yySdUXFxM69evJwcHB6qsrNQbf+PGDbK3t6f169dTcXExffLJJySXy+mf//ynEJOTk0NWVla0Y8cOKikpoR07dpC1tTWdO3fOYK/+cF2/fj3t3LmTzp8/T1evXqV33nmH5HI55efnCzHJycnk7OysM45Npa+u2dnZBIDKyspEHo+Pu8GS18bGRpHjrVu3yNXVlbZt2ybESJXXL7/8krZs2ULp6ekEgDIzM3uNN+d47Q0umgaQnJxsUNHUarWkUqkoKSlJaOvo6CClUkkff/wxET0atHK5nA4fPizE3L59m4YMGUJfffWVUX7FxcUEQDRQcnNzCQCVlpYa3M+iRYto5syZorbIyEhav369UV76MNY1Li6OFi1a9NTPpcirKb5Pcv78eQIgWpmZmtspU6ZQfHy8qE2tVtPmzZv1xm/atInUarWobe3atRQRESG8j4mJoXnz5oli5s6dS7GxsUZ7GuOqj6CgINq+fbvw3tC/y77SV9eeotnQ0PDUPgdrXjMzM0kmk9HNmzeFNqny+jiGFE1zjtfe4MOz/UhFRQVqamowZ84coc3GxgaRkZHIyckBAFy8eBEajUYU4+3tjXHjxgkxfSU3NxdKpRLh4eFCW0REBJRKpcF93r17F8eOHcPq1at1Pjt06BDc3d0xduxY/Pa3v0VLS4tRnqa6njp1CsOGDcOYMWOwZs0a1NbWCp9JkVdTfR+nqakJMplM5zC/sbnt6urCxYsXRb8XAObMmfNUr9zcXJ34uXPn4sKFC9BoNL3GmJJDY1yfRKvVoqWlBa6urqL21tZW+Pr6YsSIEViwYAEKCgqM9jTVddKkSfDy8sKsWbOQnZ0t+myw5vXAgQOIjo6Gr6+vqL2/82oM5hqvz8Jasp5/gtTU1AAAPD09Re2enp6orKwUYhQKBVxcXHRiepY35nuHDRum0z5s2DCD+0xNTYWTkxOWLFkial+5ciVGjRoFlUqFoqIivPPOO7h8+TKysrIG1HX+/PlYtmwZfH19UVFRgXfffRczZ87ExYsXYWNjI0leTfF9nI6ODmzevBkrVqwQPanBlNzW1dXh4cOHesfa07xqamr0xnd3d6Ourg5eXl5PjTElh8a4PskHH3yABw8eICYmRmhTq9VISUlBcHAwmpub8ec//xkvvPACLl++jNGjRw+Yq5eXF/bv34/Q0FB0dnbi008/xaxZs3Dq1ClMnz4dwNNzb8683rlzB8ePH0daWpqoXYq8GoO5xuuz+MkVzcTERGzfvr3XmLy8PISFhRn9HTKZTPSeiHTankRfjKGu+r7T0O/t4eDBg1i5ciVsbW1F7WvWrBH+PW7cOIwePRphYWHIz89HSEjIgLkuX75c5BEWFgZfX18cO3ZMp9Ab0u9A5Vaj0SA2NhZarRYfffSR6DNDc9sbfR1r+uKfbDdm/BqCsf1+/vnnSExMxL///W/RBkxERIRoMtgLL7yAkJAQ/PWvf8Vf/vKXAXMNCAhAQECA8H7q1Km4desW3n//faFo9rVPqVwfJyUlBUOHDsXixYtF7VLmta+Yc7w+jZ9c0Vy3bt0zZyj6+fkZ1bdKpQLwaAvJy8tLaK+trRW2hlQqFbq6utDQ0CDaK6qtrcW0adOMcr1y5Qru3r2r89m9e/d0tsL0cebMGZSVleHIkSPPjA0JCYFcLse1a9dEK/aBcu3By8sLvr6+uHbtGoC+5XWgfDUaDWJiYlBRUYGTJ08+83mAT8utPtzd3WFlZaWzRf34WHsSlUqlN97a2hpubm69xvTl/6Y/XHs4cuQIVq9ejaNHjyI6OrrX2CFDhmDy5MnCmBho18eJiIjAZ599JrwfbHklIhw8eBCrVq2CQqHoNbY/8moM5hqvz0Sys6X/R/R1ItDOnTuFts7OTr0TgY4cOSLEVFdX98tEoO+//15oO3funMGTVeLi4nRmdj6NwsJCAkCnT582i2sPdXV1ZGNjQ6mpqUQkTV5N8e3q6qLFixfT2LFjqba21qDv6mtup0yZQm+++aaoLTAwsNeJQIGBgaK2+Ph4nYkV8+fPF8XMmzevXyas9MWViCgtLY1sbW2fOWGkB61WS2FhYfT666+bomqU65MsXbqUZsyYIbwfTHkl+t/kpcLCwmd+R3/l9XFg4EQgc43X3uCi2QuVlZVUUFBA27dvJ0dHRyooKKCCggJqaWkRYgICAigjI0N4n5SUREqlkjIyMqiwsJBeeeUVvZecjBgxgr799lvKz8+nmTNn9sslJ+PHj6fc3FzKzc2l4OBgncsinnQlImpqaiJ7e3v6+9//rtNneXk5bd++nfLy8qiiooKOHTtGarWaJk2aNKCuLS0ttHHjRsrJyaGKigrKzs6mqVOn0vDhwyXPqzG+Go2GFi5cSCNGjKBLly6Jpu13dnYSUf/ktudygwMHDlBxcTElJCSQg4ODMBNy8+bNtGrVKiG+Zwr/hg0bqLi4mA4cOKAzhf+7774jKysrSkpKopKSEkpKSurXSyMMdU1LSyNra2v68MMPn3pJTmJiIn311Vd0/fp1KigooNdff52sra1FGzgD4bpnzx7KzMykq1evUlFREW3evJkAUHp6uhAzWPLaw6uvvkrh4eF6+5Qqry0tLcI6FADt3r2bCgoKhBnlg2m89gYXzV6Ii4sjADqv7OxsIQYAJScnC++1Wi1t27aNVCoV2djY0PTp03W25trb22ndunXk6upKdnZ2tGDBAqqqqjLJ9f79+7Ry5UpycnIiJycnWrlypc4U+CddiYj27dtHdnZ2eq8PrKqqounTp5OrqyspFAry9/en3/zmNzrXR0rt2tbWRnPmzCEPDw+Sy+Xk4+NDcXFxOjmTIq/G+FZUVOgdN4+Pnf7K7Ycffki+vr6kUCgoJCREtJcaFxdHkZGRovhTp07RpEmTSKFQkJ+fn96NpaNHj1JAQADJ5XJSq9Wilb8p9MU1MjJSb/7i4uKEmISEBPLx8SGFQkEeHh40Z84cysnJGXDXnTt3kr+/P9na2pKLiwu9+OKLdOzYMZ0+B0NeiR4dlbGzs6P9+/fr7U+qvPbs3T7t/3SwjdenwY8GYxiGYRgD4es0GYZhGMZAuGgyDMMwjIFw0WQYhmEYA+GiyTAMwzAGwkWTYRiGYQyEiybDMAzDGAgXTYZhGIYxEC6aDMMwDGMgXDQZhmEYxkC4aDIMwzCMgXDRZBiGYRgD4aLJMAzDMAbyX1TtQOIj33N5AAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "
"
       ]
@@ -1949,7 +3107,7 @@
     }
    ],
    "source": [
-    "gp.plot_eigenvalues(model);"
+    "gp.plot_eigenvalues(model, linearize_model_kwargs={\"steady_state\": ss_res});"
    ]
   },
   {
@@ -1962,13 +3120,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 35,
    "id": "60235bd0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "
"
       ]
@@ -1978,11 +3136,9 @@
     }
    ],
    "source": [
-    "irf = model.impulse_response_function(shock_size=0.01, simulation_length=40)\n",
-    "\n",
+    "irf = ge.impulse_response_function(model, T=T, R=R, shock_size={\"epsilon_A\": 1.0})\n",
     "gp.plot_irf(\n",
     "    irf,\n",
-    "    shocks_to_plot=[\"epsilon_A\"],\n",
     "    vars_to_plot=[\"Y\", \"C\", \"I\", \"K\", \"w\", \"r\"],\n",
     "    n_cols=4,\n",
     "    figsize=(12, 5),\n",
@@ -2049,12 +3205,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 36,
    "id": "0b678580",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
       "Model Building Complete.\n",
@@ -2069,16 +3225,15 @@
       "\t\t 1 / 1 have a defined prior. \n",
       "\t6 parameters\n",
       "\t\t 4 / 6 has a defined prior. \n",
-      "\t0 calibrating equations\n",
-      "\t0 parameters to calibrate\n",
-      " Model appears well defined and ready to proceed to solving.\n",
+      "\t0 parameters to calibrate.\n",
+      "Model appears well defined and ready to proceed to solving.\n",
       "\n"
      ]
     }
    ],
    "source": [
     "file_path = \"../GCN Files/RBC_priors.gcn\"\n",
-    "model = ge.gEconModel(file_path, verbose=True)"
+    "model = ge.model_from_gcn(file_path, verbose=True)"
    ]
   },
   {
@@ -2091,7 +3246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 37,
    "id": "6a369e5f",
    "metadata": {},
    "outputs": [
@@ -2106,13 +3261,13 @@
        " 'sigma_L': 2.0}"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model.free_param_dict"
+    "model.parameters()"
    ]
   },
   {
@@ -2120,42 +3275,48 @@
    "id": "3708b65a",
    "metadata": {},
    "source": [
-    "Priors are stored in two places. Parameters are in `model.param_priors`, while the shocks are in `model.shock_priors`"
+    "Priors are stored as a tuple in `model.priors`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 38,
    "id": "45e6fdaf",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'sigma_C': ,\n",
-       " 'sigma_L': ,\n",
-       " 'alpha': ,\n",
-       " 'rho_A': }"
+       "({'sigma_C': ,\n",
+       "  'sigma_L': ,\n",
+       "  'alpha': ,\n",
+       "  'rho_A': },\n",
+       " {'epsilon_A': },\n",
+       " {sigma_epsilon: (epsilon_A_t,\n",
+       "   'sd',\n",
+       "   )})"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model.param_priors"
+    "model.priors"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 39,
    "id": "ebda7d3d",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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UrfpButSDFjUB0uhSdILm+PHjcg9B0ZA/60Pe8EP+BEN+8EP+rA95w49a/HE6nfj4xz+OX/7yl0hJSRHtOGrxI1xIl3rQoiZAu7oiRat+kC71oEVNgDS6FJ2gIQiCIAiCEJtbb70VV1xxBS655JINt3W73XA4HEE/fCtuEARBEARBhIoxmhfv27cPX//61/HFL34RDz74oEBDWmbr1q2C71NLkD/rQ97wQ/4EQ37wQ/6sD3nDjxr8eeKJJ9DW1obm5uaQtt+3bx/uvffeoMduu+02XH/99QCAuro6dHZ2YmFhATabDUVFRTh8+DAAIDU1FYODg+jv7wcA1NTUoLu7G06nE4mJiSgtLcXBgwcBAHl5eTAYDDhz5gwAoLq6Gr29vXA4HLBYLKisrERraysAICcnBxaLBadPnwYAVFVVYWBgANPT0zCZTKipqcGBAwcAAFlZWbBareju7gYAVFRUYGRkBJOTkzAajaivr8eBAwfAMAzS09ORkpKCrq4uAEBZWRkmJycxNjYGvV6PnTt3oqWlBYuLi+ju7kZGRgY6OzsB+M+9w+HAyMgIAGDXrl1oa2vD0tISUlJSkJOTg6NHjwIAiouLMT8/j6GhIQD+pe47OjrgcrmQlJSEgoICHDlyBABQWFgIj8eDgYEBzu/jx49jfn4eVqsVxcXFOHToEACgoKAAANDX1wcA2LFjB06dOgWn04mEhASUl5ejra2N89toNKK3txcAsH37dphMJjQ1NcFisaCqqgotLS0AgOzsbCQkJODUqVMAgMrKSgwODmJqagpxcXGoq6tDU1MTACAzMxN2ux0nT57k/B4dHcXExAQMBgMaGhrQ3NwMn8+H9PR0pKam4sSJEwCA0tJSTE1NYWxsDDqdDo2NjWhtbYXH40FqaioyMzM5v0tKSuB0OjE8PAwAaGxsRHt7OxYXF5GcnIy8vDx0dHRgcXERY2NjcLlcGBwcBADU19fj6NGjcLlcsNvtKCws5GJ28+bN8Hq9nN+1tbXo6urC3NwcrFYrSkpK0N7eDgDIz8+HXq8Pitmenh7Mzs4iPj4eFRUVnN+5ubkwmUzo6enh/O7v78f09DTMZjOqq6u5/5NZWVlITEzk/N62bRuGh4cxOTnJ+b20tISmpiZkZGQgKSmJ87u8vBzj4+MYHx/nYjYWUMO1NxJIl3rQoiZAGl0RL7Pd3NyMD3/4w7Db7bjoootESdD09PSgqKhI8P1qBfJnfcgbfsifYMgPfsif9SFv+FG6P/39/WhoaMALL7yAHTt2AAB2796NmpqadT/XuN3ucypmzGYzzGbzhsdTuh+RQrrUgxY1AdrVFSla9YN0qQctagKk0RXRFCep5mqPjo6Ktm8tQP6sD3nDD/kTDPnBD/mzPuQNP0r3p7W1FaOjo6ivr4fRaITRaMRrr72GH//4xzAajfB6vee8xmw2w263B/2EkpwBlO9HpJAu9aBFTYB2dUWKVv0gXepBi5oAaXRFlKAJZ642QRBEtNDStwRBiMGePXtw5MgRtLe3cz8NDQ34+Mc/jvb2dhgMBrmHSBAEQRBEDBH2FKcnnngC3/nOd9Dc3AyLxSJqKTBBEIQU0ykJgiBYNvpcQxAEsZrXX38dDzzwAFpbWzE0NISnnnoK11xzzZrb3nLLLfjFL36BH/7wh3TjiSCIcwirSXB/fz+++MUv4oUXXoDFYgnpNdE00/N4PCgqKtJcMz2v14u0tLSom+l1dnYiOTlZMc30+vr6MDMzo4hmejMzM7jsssuibqYHAFu2bNFcM70XXngBSUlJim+mt3I65f333y/acdra2lBXVyfa/tUO+bM+5A0/5E8wWvWDdKkHLWoC5Nc1NzeHHTt24KabbsIHP/jBdbd7+umn0dTUhJycHFHHI7cfYkG61IMWNQHS6Aqrgubpp5/GBz7wgaCSX6/XC51OB71eD7fbfU45cDQVNE1NTdi1a1eow4s5lOCP18fgYN8UXEs+mIx61BYkI84g/+rtSvBGyajFnxtuuAGpqan44Q9/KGq1nlr8kAvyZ21cS1688EYT3n/x+XIPRbFQ7ASjVT+0pMvnY3BoYBpzbi+6TnTiY/90Pixx2pnqpqVztRIl6dLpdGtW0Jw9exa7du3C888/jyuuuAJf+tKXRKugUZIfQkK61IPaNfl8DE6Pz2HLpkTo9TrucSl0hVVBw87VXslNN92E8vJy3HHHHWvO1Y5mOlNqampEr4sVlODPT1/pxn++2MX9/c/vLsI3rtgm44j8KMEbJaMGf6Rc+jYuLk6TS98KVa3ndDrR1NRE1XqrqvXue/40XjnjRmbhBIxTvVStt0a13tzcHC19uwI1XHsjQSu65hc9+MIfDuKlzuUmkI+deAOP3tiIgrQEGUcmHFo5V6tRui6fz4dPfvKT+MpXvoLKykrRj6d0PyKFdKkHtWt6uv0sbv+fQ/jypaX4/J7lpbWl0BXxMtssYs7Vnp2dhc1mE3y/WkEJ/vzzb1vw4rER2MxGzLo9eNeWVDzxL+fJOiZAGd4oGaX7I/XSt0r3Q27In3NhGAZ1972IqfklxSSmlQjFTjBa9UMLukZnXbj50RYcOTsDk1GPLZsSMTSzgJkFD9ISTfjVDQ2oLRBv5VKp0MK5Wgsl6Vqrgmbfvn145ZVX8Pzzz0On06GwsHDDChr6XHMupEs9qF3TN546gt839SE3OR5v3nERdDp/FY0UusKqoJGaY8eOqbo0SmyU4M/g9AIA4BPnbcZDr57C4LRL1vGwKMEbJaN0f1Yufcvi9Xrx+uuv4yc/+cma0ymjqdZTuh9yQ/6cy6mxOUzNLwEAWs5MyTwa5UKxE4xW/VC7LoZh8IU/HMSRszNITTThl59qQP3mFPz91bfxk8NeHB104ObftOCVL+9GUkKc3MONCrWfq/VQsq7W1lb86Ec/QltbG/clLxSiqQx2uVzYunWr5iqDx8fHUVJSEnVl8NDQEAAopjK4qakJNptN9srgsbEx6HQ6NDY2Rt3Hc2pqCvX19aqtDO7snQEAnJ1ewOPPv4WKjATU1dXhrbfeQkpKiqiVwVFX0IiJ2ueuiY0S/Kn99guYml/Cr29swKcfbUGcQYcT9+0NmqsnB0rwRsko3Z/Z2VnuosuycjplVVWVoMdTuh9yQ/6cyx+b+3DHn/0f4uIMOhy555801adCKCh2gtGqH2rX9bfDg/jc4wdhNurxf198N4rTrQD8uqpq6nHNf7+Fk6NO3Hh+Ie55v/jTU8RE7edqPZSka3UFzYMPPojbb78dev1yj0av1wu9Xo/8/Hzui/pqqLfeuZAu9aB2TZc/+DqOD88CAG557xZ8bW8FAGl0yd/NlYfi4mK5h6Bo5PZnftHD3UGuzU+BXgcseRmMO90bvFJ85PZG6SjdH5vNhqqqqqCfxMREpKWlCZ6cAZTvh9yQP+fSuqJqZsnL4PDAjIyjUS4UO8Fo1Q8161pY9OK7z/rvDH/mvcVccgbw60o0G3H3Vf6kzO/2n0HXyKws4xQKNZ8rPpSs65Of/CQOHz6M9vZ27icnJwdf+cpX8Pzzz6/7OrPZDLvdHvQTaqWwkv2IBtKlHtSuaWhmeVbI/x0ZAlvTIoUuRSdo5ubm5B6CopHbH3Y6k81sREqiCVl2/9LrA4FpT3IitzdKh/wJhvzgh/w5F3Zak91sCPw9KedwFAvFTjBa9UPNun722ikMzriQk2TBZ94b/MGb1XXh1k24bFsmvD4G9z5zFAouPt8QNZ8rPuTW5XQ6ueQLAPT09KC9vR19fX3czaWVP3FxccjKykJZWZko45HbD7EgXepBzZrmFz2YWfAXIZgMevRPLuDooAOANLoUnaBh57YRayO3P2z/mZzk+KB/BxWQoJHbG6WjRn9effVVUZqRA+r0Q0rIn2Am5xZxesz/Br2n0AQAaO2lPjRrQbETjFb9UKuuhUUvfv2Wvy/B195XgXhT8DTFlbq+ecU2mAx6vNU9gSNn1Vsxp9ZztRFy62ppaUFtbS1qa2sBALfffjtqa2vxrW99S5bxyO2HWJAu9aBmTWwRQqLJgD0VGQD8VTSANLoU3SSYUDbLCRpL4N944MyUIhI0BEEQYsFObyrJsKI+y4CnTiygtW8KPh8je/8tgiBC59kjQ5h1eZCXEo8rtmfzbluQloD3bc/C0+2D+MOBPlTnJUszSEIV7N69O6zKqvX6zhAEIT/DgelN2cnxuLg8A3/vGMbBvmnJjq/oCppouh/HAnL7c3bdChr5V3KS2xulQ/4EQ37wQ/4Ew05naticguv2vAtmox7T80s4Pa7ecl6xoNgJRqt+qFXXHw74V1z5aGPBmsnV1bo+2uhfqeUv7YNwuj3iD1AE1HquNkKruiJFq36QLvWgZk2DM/7vuNlJFtgs/noWj88HQBpdik7QsMtuEWsjtz9sgiY3JT7o37MKqKCR2xulQ/4EQ37wQ/4E0xaooKnbnILjxzqwIz8ZANBKfWjOgWInGK36oUZdXSOzaD0zBYNehw/V5625zWpdjUWp2JKeiPlFL/7aPijFMAVHjecqFLSqK1K06gfpUg9q1jQUKDbISYqHXudP3nt9/go5KXQpOkGzemk5Ihi5/WGnMuUGKmdyA1OdlDDFSW5vlA75Ewz5wQ/5E8zJUScAYHtuEtxuN6pzk/yPjzjlHJYiodgJRqt+qFEXWz2zpzwDGYFFDlazWpdOp8NHdxYEvV5tqPFchYJWdUWKVv0gXepBzZqG2AqaZAuXoAnkZyTRpegETXJystxDUDRy+8NOZVJik2C5vVE65E8w5Ac/5M8yS14fpuf9nf0zbGYkJycj3eZf9nRyblHOoSkSip1gtOqH2nQteX146uBZAMBHdxWsu91auj5YnweTQY8jZ2dwLLCqh5pQ27kKFa3qihSt+kG61IOaNQ3OLFfQGPRsgsafoZFCl6ITNPn5+XIPQdHI6Y/Px3DZxdUJmqn5Jcwvyjs3m2KHH/InGPKDH/Jnmal5fxJGpwOSE0zIz89HaqJ/JacJStCcA8VOMFr1Q226DvRMYnp+CWmJJrxna/q6262lKzXRhPeW+V/zwjH1rVKitnMVKlrVFSla9YN0qQc1axqaXlFBow+e4iSFLkUnaI4cOSL3EBSNnP6MO91Y8jLQ64DMwN1juyUONrO/kZLcVTQUO/yQP8GQH/yQP8tMOP1JmNQEEwx6HY4cOYJNVv81cGJOveW8YkGxE4xW/VCbrhePjQAA9lRkcHdH12I9XZduywzaj5pQ27kKFa3qihSt+kG61IOaNQ2xqzglxYN9i2ATNFLoUnSChlAubCPgLLsFRsNyGLFVNGcVsJITQRCE0LDTmNiqmZW/TzqpgoYglA7DMFxi5dJtWRHtY095BnQ64OigQ/YbUgRBEIRwzLqWuFX6spMsMAR60ARmOEmCohM0RUVFcg9B0cjpz+r+Myw5CmkUTLHDD/kTDPnBD/mzzLjTXyWTZvUnZYqKirgEzfjcIhgp38FVAMVOMFr1Q026OodmcXZ6AZY4PS4s2cS77Xq60qxm1BekAABe6lRXFY2azlU4aFVXpGjVD9KlHtSqia2esVuMSDQbl6c4BT7fSaFL0QmaxUW6G8mHnP6wCZhzEzTKaBRMscMP+RMM+cEP+bMMW0GTluif1rS4uMglaxY9PswtemUbmxKh2AlGq36oSRdbPXNhSTriTQbebfl0qXWak5rOVThoVVekaNUP0qUe1Kpp9XdcrklwYIqTFLoUnaA5e/as3ENQNHL6c3aDBM1ZmRM0FDv8kD/BkB/8kD/LsD1o2KTM2bNnkWAyIj7OEHie+tCshGInGK36oSZdbMXLpdsyNtyWT9clgQTN/tMTmHUtCTM4CVDTuQoHreqKFK36QbrUg1o1Lfef8c8K4XrQBCpopNCl6AQNoVzY7GJuYEoTS65CKmgIgiDEYGKNHjTAcsKGVnIiCOUyPOPCkbMz0OmAi8szo9pXcboVW9ITseRl8HrXuEAjJAiCIORkeQUn/3davS54mW0pUHSCpq6uTu4hKBo5/Rmd9d8lzrQHJ2jYv0cd8t5Fptjhh/wJhvzgh/xZZnKO7UHjn+LEepNGjYLXhGInGK36oRZdb5/yJ1Kqc5OQHliBko+NdF1U5q/CebNbPQkatZyrcNGqrkjRqh+kSz2oVdNgoIImJ1BBszzFyf+8FLoUnaDp7OyUewiKRk5/Zhb85bzJCcF3kZMT4oKelwuKHX7In2DID37In2W4KU6BhAzrTRottb0mFDvBaNUPteh6+9QEAOBdxWkhbb+RrvO2+Pez//REdAOTELWcq3DRqq5I0aofpEs9qFXTWKAIIcPGTnEKNAkO9KCRQpeiEzQLCzRNhg85/WETMEnxcUGPs3/PLCzJupoJxQ4/5E8w5Ac/5M8yy02C/Qka1ht2yhNNcQqGYicYrfqhFl3vBBI0bGJlIzbS1bglFXod0DM+h+HAXVelo5ZzFS5a1RUpWvWDdKkHtWqaXDWVffUUJyl0KTpBY7PZ5B6CopHLH4ZhNkzQeHwM5mVczYRihx/yJxjygx/yZ5nVy2yz3nA9aGiKUxAUO8Fo1Q816OqfnMfZ6QUY9TrsLEwN6TUb6bJb4lCVmwQAeOe0OqY5qeFcRYJWdUWKVv0gXepBrZq4BE3gcx03xSmQoJFCl6ITNGpdP10q5PLH6fZwZV7slCaWBJMBcQZ/IE/LOM2JYocf8icY8oMf8sfPoscHh8sDYHmZbdYbrgcNVdAEQbETjFb9UIMutnpmR34yEs3GkF4Tiq7zAtOl3u5WxzQnNZyrSNCqrkjRqh+kSz2oVdPqSmlDIFvCfveVQpeiEzSHDx+WewiKRi5/2OoZk1EPS2BpWRadTrc8zWlevgQNxQ4/5E8w5Ac/5I+fqXn/m7ZBv3ydY71hEzbjtMx2EBQ7wWjVDzXoeud0eNObgNB0sft7RyV9aNRwriJBq7oiRat+kC71oEZNC4teLCz5Z4CwU5x0q3rQSKFL0QkaQpmsN72JxR6vjEbBBEEQQsJOX0pJMEEfKHllYUthqYKGIJQHwzDL/WdCbBAcKjsLU2HU6zAwtYD+yXlB900QBEFIB7vQg8mghzVQaWkIJGikbK2q6ATN5s2b5R6CopHLn40SNEkKSNBQ7PBD/gRDfvBD/vhh37jZsldg2ZtNgQoa6kETDMVOMFr1Q+m6eifmMexwwWTQo35zSsivC0VXotmI6jy2D43yq2iUfq4iRau6IkWrfpAu9aBGTSsbBLOVM2wPGm8gQyOFLkUnaHzsguPEmsjlDzt1KXmdBE0yl6CR74sKxQ4/5E8w5Ac/5I8fbl6ydTlBw3qzsoJGzhXslAbFTjBa9UPpupoCiZOa/ORzpmbzEaoutiqn6fRk+IOTGKWfq0iRW9frr7+Oq666Cjk5OdDpdHj66ae555aWlnDHHXdg+/btSExMRE5ODj71qU9hcHBQtPHI7YdYkC71oEZNE6tWcAKAQJ6Gm+IkhS5FJ2j6+/vlHoKikcsfNVTQUOzwQ/4EQ37wQ/74GXee+8bNesNW1Sx6fZh1e6QfnEKh2AlGq34oXdfBvmkAQH1h6NUzQOi62Kqcg/1TYe1fDpR+riJFbl1zc3PYsWMHfvKTn5zz3Pz8PNra2nDXXXehra0NTz75JLq6uvD+979ftPHI7YdYkC71oEZNU2vciFu9ipMUukJrY08QK1BDgoYgCEJoJgNTnDZZzec8Z4kzINFkwNyiF5PORdgta18fCYKQnrY+f+KkriC8BE2o1Ob793t6bA7T84tITjBt8ApCa+zduxd79+5d87mkpCS8+OKLQY/913/9FxobG9HX14eCggIphkgQxAawldIpK67hbA8aH/Wg8VNTUyP3EBSNXP6wiRe7ghM0FDv8kD/BkB/8kD9+JtaooFnpDTvNie1VQ1DsrEarfihZ18zCEk6OOgEAtQXJYb02VF0piSZs2ZQIYLlaR6ko+VxFg9p0zczMQKfTITk5ed1t3G43HA5H0I/bHdr7i9r8CBXSpR7UqGmtKU7sohDsFCcpdCm6gqa7uxuVlZVyD0OxyOXPdCDxkpywToImkHWclnGZbYodfsifYMgPfsgfPxNrlL6u9CYt0Yz+yQVqFLwCip1gtOqHknW1908DADanJaxZ/cZHOLpqC1JwenwOB/umcFF5RrjDlAwln6toUJMul8uFO++8Ex/72Mdgt9vX3W7fvn249957gx677bbbcP311wMA6urq0NnZiYWFBdhsNhQVFXFLAOt0OuTl5XHTMWpqatDd3Q2n04nExESUlpbi4MGDAIC8vDwYDAacOXMGAFBdXY3e3l44HA5YLBZUVlaitbUVAJCTkwOLxYLTp08DAKqqqjAwMIDp6WmYTCbU1NTgwIEDAICsrCxYrVZ0d3cDACoqKjAyMoLJyUkYjUbU19fjwIEDYBgG6enpSElJQVdXFwCgrKwMk5OTGBsbg16vx86dO9HS0oKpqSkUFRUhIyMDnZ2dAICtW7fC4XBgZGQEALBr1y60tbVhaWkJKSkpyMnJwdGjRwEAxcXFmJ+fx9DQEACgoaEBHR0dcLlcSEpKQkFBAY4cOQIAKCwshMfjwcDAAOf38ePHMT8/D6vViuLiYhw6dAgAuCqovr4+AMCOHTtw6tQpOJ1OJCQkoLy8HG1tbZzfRqMRvb29AIDt27ejpaUFZrMZFosFVVVVaGlpAQBkZ2cjISEBp06dAgBUVlZicHAQU1NTiIuLQ11dHZqamgAAmZmZsNvtOHnyJOf36OgoJiYmYDAY0NDQgObmZvh8PqSnpyM1NRUnTpwAAJSWlmJqagpjY2PQ6XRobGxEa2srPB4PUlNTkZmZyfldUlICp9OJ4eFhAEBjYyPa29uxuLiI5ORk5OXloaOjAw6HAzU1NXC5XFy/pfr6ehw9ehQulwt2ux2FhYVczG7evBler5fzu7a2Fl1dXZibm4PVakVJSQna29sBAPn5+dDr9UEx29PTg9nZWcTHx6OiooLzOzc3FyaTCT09PZzf/f39mJ6ehtlsRnV1NZqbm/3nb9i/xPb81CiampzYtm0benvPcv//fD4Gb7zxBmw2GzIyMpCUlMT5XV5ejvHxcYyPj3MxGymKTtA4nU65h6Bo5PJHDVOcKHb4IX+CIT/4IX/8TDjPXcVppTfs4xO01DYHxU4wWvVDybrazvinN9XmJ4f92nB01RYk489tA2hTeAWNks9VNKhF19LSEj7ykY/A5/Phpz/9Ke+2X/va13D77bcHPWY2m2E2Lycaq6urg57ftWsXAKCpqQk5OTnIycnhnludwGK3ZcnKyuJ+r6io4N02PT2d+72srIx327S05aXtVyekGhsb131tcnIytmzZwv3d0NCApqYmlJSUnLNtamoqCgsLub/r6up4x7RyWtmOHTt4t83NzeV+3759O++22dnZ3O8b+Z2Zmcn9bjabg55fve2mTZu430tLS3n3u/Jvu93O+QXgnKTBym1TUlKC/K6vr19327S0tKDVjGpra8/ZtqmpiYuT/Px87rlw/K6qquLddmXMbtu2jXfbjIzlxPl6MfvL4/7E2I7yLdi1y69va3ExAH+CzMswsNls656rpKQkFBcXI1oUnaBJTEyUewiKRi5/HCEmaBwyJmgodvghf4IhP/ghf/wsr+K0/OF4pTdpK1ZyIvxQ7ASjVT+UrIvrPxPG8tos4ehi+9u090/D62O4xpJKQ8nnKhrUoGtpaQkf/vCH0dPTg5dffpm3egY4NxkTDmrwIxJIl3pQoya212Ba0BSn5ed9DCOJLkX3oFmdISSCkcsfNVTQUOzwQ/4EQ37wQ/74WasHzUpvUhP9H6THndSDhoViJxit+qFUXT4fw01xiqRBcDi6yrJsSDQZ4HR7cHJ0NuxjSYVSz1W0KF0Xm5w5efIkXnrppaCqEjFQuh+RQrrUgxo1TQXac7Cf5wAEJdt9Pml0KTpBw86NJNZGLn/Y3jLr9aBhH5+WMUFDscMP+RMM+cEP+QO4PV5u+exNK964V3qziW0STD1oOCh2gtGqH0rVdWrMiVmXB/FxBpRn2cJ+fTi6DHoddgSmUSm5UbBSz1W0yK3L6XSivb2d65HR09OD9vZ29PX1wePx4LrrrkNLSwt+//vfw+v1Ynh4GMPDw1hcFOf9Qm4/xIJ0qQc1amKnsgc1CdYtJ2i8DCOJLkUnaAhlEmoFjWNhCT4p1yQjCIIQCfa6p9cBNsvas4PZpXXlrB4kCGIZdnpTdV4SjAbxP/KyVTps3xsidmhpaUFtbS3Xi+P2229HbW0tvvWtb2FgYAB//etfMTAwgJqaGmRnZ3M/b7/9tswjJwgCAJa8Pjhc/htxaeskaHyMNN9rFd2DJi8vT+4hKBo5/PH5GDhcoS2z7WMA56IHdsva24kJxQ4/5E8w5Ac/5A/gWPC/aVvNRm7JRSDYGzZxw14jCYqd1WjVD6Xqau+fAQDUhLm8Nku4uthlvA8NTEd0PClQ6rmKFrl17d69GwzPlze+58RAbj/EgnSpB7Vpmgr0D9TrgosQgqc4MZLoUnQFjcFgkHsIikYOf2bdHrDvMetV0FjiDDAZ/aE1I9NS2xQ7/JA/wZAf/JA/wOw6iemV3rDJ6NnAHRiCYmc1WvVDqbo6zvoTNNW5yRG9Plxd23OTAADdo07MLyrzOqDUcxUtWtUVKVr1g3SpB7VpYlfgTEkwBd2IW9nv3etjJNGl6AQNu7Y5sTZy+MMmXOLjDDAb1w/QZJkbBVPs8EP+BEN+8EP+gCt7ta2qCFzpDVdBQ1OcOCh2gtGqH0rU5fZ4cXzYAcA/xSkSwtWVYbcg026GjwGODToiOqbYKPFcCYFWdUWKVv0gXepBbZrYFThX9p8BAJ1OB3aWk5dhJNGl6AQNoTw26j/DooSVnAiCIISCraBZr/8MQBU0BKEkuoadWPIySIqPQ15KvGTHZatojgSqdwiCIAjlM7FOggYADIEMjVQzFRWdoKmurpZ7CIpGDn/UkqCh2OGH/AmG/OCH/FnuQbO6p9ZKb+zx/uTNwpIXS16fdINTMBQ7wWjVDyXqYhMk23OToFvR5DEcItFVxSZoBpSZoFHiuRICreqKFK36QbrUg9o0sT1o0qznJmjYKU9eHyOJLkUnaHp7e+UegqKRwx+1JGgodvghf4IhP/ghf1b0oFlVQbPSG6vZuGJ7qqIBKHZWo1U/lKjryNlpAMD2CKc3AZHpYqdTKbWCRonnSgi0qitStOoH6VIPatPEV0HD9qHx+hhJdCk6QeNwKHP+rlKQw5/pBX/wJiVskKAJPD8tU5Ngih1+yJ9gyA9+yB+su3rdSm+MBj0STf7eXNSHxo/SY+ehhx5CdXU17HY77HY7zjvvPPz9738X7XhK9yNSlKhrZQVNpESii62gOTXmxJxbeYlaJZ4rIdCqrkjRqh+kSz2oTdPknBsAkJpoPuc5doqTj2Ek0aXoBI3FYpF7CIpGDn/UUkFDscMP+RMM+cEP+bNcEbO6B81qb2zUhyYIpcdOXl4evve976GlpQUtLS24+OKLcfXVV+Po0aOiHE/pfkSK0nS5PV6cGJ4FEF2CJhJdGbYVjYKHlPcFRWnnSii0qitStOoH6VIPatPENQleowiBneLkY6TRpegETWVlpdxDUDRy+KOWBA3FDj/kTzDkBz/kz3JFzOoeNKu9YfvQsBU3sY7SY+eqq67C+973PpSWlqK0tBTf+c53YLVasX//flGOp3Q/IkVpuk4Mz2LJyyA5IboGwZHq2h5Y1luJfWiUdq6EQqu6IkWrfpAu9aA2TRPOQILGem4FjV633INGCl2KTtC0trbKPQRFI4c/jjATNHKV+VPs8EP+BEN+8EP+rF9Bs9qb5QoaStAA6oodr9eLJ554AnNzczjvvPNEOYaa/AgHpekSokEwELkuJa/kpLRzJRRa1RUpWvWDdKkHtWmamg80CV5rFSf98hQnKXSFlaCReq42oTzYnjLJG/SgYZ9ne9YQBEGoGTZBs7oHzWrYJsLsqk+E8jly5AisVivMZjM+85nP4KmnnsK2bdvW3d7tdsPhcAT9uN1uCUdMbERHIDFSFcX0pmjYnmcHABwemJbl+ARBEER4TPI2CV5O0EiBceNNlmHnapeUlAAAfvOb3+Dqq6/GwYMHRSn3ycnJEXyfWkIOf9QyxYlihx+l+/PQQw/hoYce4jqlV1ZW4lvf+hb27t0ryvGU7ofckD/LU5ZWV9Cs9oatoKEpTn7UEDtlZWVob2/H9PQ0/vznP+OGG27Aa6+9tm6SZt++fbj33nuDHrvttttw/fXXAwDq6urQ2dmJhYUF2Gw2FBUV4fDhwwCAhIQEDA4Oor+/HwBQU1OD7u5uOJ1OJCYmorS0FAcPHgTg/8xlMBhw5swZAP4lS3t7e+FwOGCxWFBZWcndycvJyYHFYsHp06cBAFVVVRgYGMD09DRMJhNqampw4MABAEBWVhasViu6u7sBABUVFRgZGcHk5CSMRiPq6+tx4MABMAyD9PR0pKSkoKuri/NqcnISY2Nj0Ov12LlzJ1paWrCwsIDu7m5kZGSgs7MTALB161Y4HA6MjIwAAHbt2oW2tjYsLS0hJSUFOTk5XK+f4uJizM/PY2hoCADQ0NCAjo4OuFwuJCUloaCgAEeOHAEAFBYWwuPxYGBggPP7+PHjmJ+fh9VqRXFxMZq6/PvJTfBhaGgIfX19AIAdO3bg1KlTcDqdSEhIQHl5Odra2ji/jUYj976zfft2AEBTUxMsFguqqqrQ0tICAMjOzkZCQgJOnToFwP8eNTg4iKmpKcTFxaFqqz92To/NofPkaWSmJePkyZOc36Ojo5iYmIDBYEBDQwOam5vh8/mQnp6O1NRUnDhxAgBQWlqKqakpjI2NQafTobGxEa2trfB4PEhNTUVmZibnd0lJCZxOJ4aHhwEAjY2NaG9vx+LiIpKTk5GXl4eOjg4sLCxgbGwMLpcLg4ODAID6+nocPXoULpcLdrsdhYWFXMxu3rwZXq+X87u2thZdXV2Ym5uD1WpFSUkJ2tvbAQD5+fnQ6/VBMdvT04PZ2VnEx8ejoqKC8zs3Nxcmkwk9PT2c3/39/ZienobZbEZ1dTWam5u5mE1MTOT83rZtG4aHhzE5OYm4uDjU1dVhYWEBTU1NyMjIQFJSEud3eXk5xsfHMT4+zsVsLKCGa28kkC71oCZNPh+DqUARwloJGkOgpMXrYyTRpWOY6FJBqampeOCBB3DzzTcLNSaOsbExpKenC75frSCHP1f8+A0cHXTgkZt24qKyjHW3az0ziQ8+9A7yU+PxxlcvlnCEfih2+FG6P8888wwMBkNQMviBBx4QLRmsdD/khvwBLvjeyzg7vYC/3HoBduQnc4+v9uabTx/BY/v78IU9W3H7paUyjFRZqDF2LrnkEhQXF+PnP//5ms+73e5zKmbMZjPM5nPnra9GjX6EgpJ0ebw+bLv7eSx6fHj133ajcFNixPuKRlfD/S9h3OnGU589H7UFKRGPQWiUdK6ERKu6IkWrfpAu9aAmTdPzi6j59osAgK7798JkDJ5kdN6+f2BoxoVnPnchssyLouuKuAdNqHO1oykFZu8EEWsjhz9hV9DItMw2xQ4/SvdH6sadSvdDbsif5X5aqytoVntDPWiCUWPsMAzD+znFbDZzU73Zn1CSM4A6/QgFJek6PT6HRY8PiSYDClITottXFLq25finOSltJSclnSsh0aquSNGqH6RLPahJEzu9yWY2npOcAVY0CWYYSXSFNcUJ8M/VPu+88+ByuWC1Wjecqx1NKTBbfqm1UmCv14u0tLSoS4GnpqbQ1NQUdSnwoUOHAAAFBQUAwFsKPOl0+c+NYxIjI4tBpcB9fX2YmZmBxWJB5uatAACHy4PeM2dgXVGauroUuK6uDk1NTQCAzMxM2O32qEuBp6amACDqUmAA2LJli+ZKgdnYUUMpsNfrxZ/+9CdRG3cSBB8+HwPnYqg9aNgG6dSDRg18/etfx969e5Gfn4/Z2Vk88cQTePXVV/Hcc8/JPTQiQo4N+hMi5dl2bmlUOajItuH1rjFuPARBEIQyYRsEp6wxvQkIbhIsBWFPcVpcXERfXx83V/tXv/oV71ztaEqB5+bmkJgYeWmq1pHaH4ZhsOXr/weGAQ58Yw8ybOuvA+9a8qL8Lv8H3CP3XMbdVZYKih1+1ODP6mTw448/jve9733rbk/XGvGIdX9mFpaw494XAAAn7r8cZqOBe261N4/tP4NvPt2By7Zl4hefapB8rEpD6bFz88034x//+AeGhoaQlJSE6upq3HHHHbj00ktFOZ7S/YgUJena93+d+Pnrp/HJd23GfddURbWvaHT9pf0svvhEO+oKkvHkZy+IahxCoqRzJSRa1RUpWvWDdKkHNWl64egw/uV3rajJT8bTt557vb7oP15Fz/gc/vcz56Ei3Sy6rrAraEwmE9cXgq1m+NGPfrTuXO1QvyCtxcDAAMrKyiJ6bSwgtT9zi16w6TybmT/hYjbqYdTr4PExmHV5JE/QUOzwowZ/pGzcaTAYkJOTQ9V661TrnTx5ElarVdJqPb7GnSur9cJp3BlptZ7XkgwAiNMD7a0tQdV6TqcTe/bs4ar15qb9b6sDo5NoamqK+Wq9/fv3w2q1KrZa7+GHH5b0eGq49kaCknSxU4rYKUbREI2uysDxjw/PwutjuDuwcqOkcyUkWtUVKVr1g3SpBzVpYito1moQDACBGU7w+hhJdEXdJHjPnj3Iz8/Ho48+KtCQlmlqasKuXbsE369WkNqf4RkX3rXvHzDqdTj5nb3Q6fg/bNR++wVMzS/hhdveg9JMm0Sj9EOxw48a/RGzcaca/ZCSWPfn2KAD7/vxG9hkNaPlm5cEPbfam1dOjOKmR5pRmWPHs194t9RDVRyxHjur0aofStHFMAwa7n8JE3OL5zT0joRodHl9DCrvfg6uJR9e/vJ7sSXdGtVYhEIp50potKorUrTqB+lSD2rS9NNXu/H9507guvo8/MeHdpzz/KU/eA0nR514/J93wTB+SnRdYVXQSD1X22RaO4tF+JHaH7bppdVi3DA5w243Nb8kS7NMih1+1OhPKI07I63WU6MfUhLr/rDXMLvl3LfM1d6w29Ay235iPXZWo1U/lKJrdNaNiblF6HVAWVb0N4ai0WXQ61CWZceh/mkcG3IoJkGjlHMlNFrVFSla9YN0qQc1aZqa46+gYSsgGUYaXWElaEZGRvDJT34yaK72c889J9pc7ZqaGlH2qxWk9mfW7W96uXoVk/XwT4NawKxL+maZFDv8KN0fqZPBSvdDbmLdH/YaZlujQfBqb+zcKk7UJBig2FmNVv1Qii62IW9xuhWWOMMGW29MtLq2ZQcSNIMOXFmdE/V4hEAp50potKorUrTqB+lSD2rSNBFI0KQkrDfFKbCKk4+RRFdYy2w//PDD6O3thdvtxujoKF566SXRkjMAuN4MxNpI7Q/7hcO6Qf8ZFmsgkeN0S/9FhWKHH6X7wyaDy8rKsGfPHjQ1NYmaDFa6H3IT6/44eCpoVntjW5GgiXIGsSaI9dhZjVb9UIouIfvPANHrUuJS20o5V0Ijt67XX38dV111FXJycqDT6fD0008HPc8wDO655x7k5OQgPj4eu3fv5vq9iYHcfogF6VIPatLEVtCkrVtB4//XyzCS6Aq7STARuzhd4VXQsF9m6E4yES5SN+4kCD7Ya5g9hGbn9nj/dc/rYzC/6EWimd5mCUIq2AqabdnCJGiihR0HLbWtfebm5rBjxw7cdNNN+OAHP3jO89///vfxgx/8AI8++ihKS0tx//3349JLL8WJEydgs0nbp5EgiGAm5/034tZdZlvHTnGS5saboj85ZmVlyT0ERSO1P2wfBluIXzisge2cMiRoKHb4IX+CIT/4iXV/HAuBa98ayenV3sTHGWDQ6+ANrGAX6wmaWI+d1WjVD6Xo6gxUqlQIlKCJVld5lg06nb83zrjTjU3WyPqkCYlSzpXQyK1r79692Lt375rPMQyDBx98EN/4xjdw7bXXAgB+85vfIDMzE48//jhuueUWwccjtx9iQbrUg5o0Tc75e1yu14NGr2enOEmjK6wpTlJjtSqjoZpSkdofdqqSNdQeNFypv/TNMil2+CF/giE/+Il1f9j+W/Y1etCs9kan01Gj4BXEeuysRqt+KEHXwqIXPRNzAIRL0ESrK9FsxObUBADAieFZIYYUNUo4V2KgZF09PT0YHh7GZZddxj1mNpvx3ve+F2+//fa6r3O73XA4HEE/fIslrETJfkQD6VIPatI0Nef/vLZugmZFDxopdCn61l53dzfS0tLkHoZikdqf2TCnOLGJnFkZetBQ7PBD/gRDfvAT6/5wFTRrVMOs5Y3NEifbCnZKI9ZjZzVa9UMJurpGZsEw/h4C6TZhKlWE0FWWZUPvxDw6hxy4oGSTIOOKBiWcKzFQsq7h4WEAQGZmZtDjmZmZOHPmzLqv27dvH+69996gx2677TZcf/31AIC6ujp0dnZiYWEBNpsNRUVFOHz4MADA5XJh69at6O/vB+Bv2Nrd3Q2n04nExESUlpbi4MGDAIC8vDwYDAZuLNXV1ejt7YXD4YDFYkFlZSVaW1sBADk5ObBYLDh9+jQAoKqqCgMDA5ienobJZEJNTQ3XoyMrKwtWqxXd3d0AgIqKCoyMjGBychJGoxH19fU4cOAAGIZBeno6UlJS0NXVBQAoKyvD5OQkxsbGoNfrsXPnTrS0tGB8fBwlJSXIyMhAZ2cnAGDr1q1wOBwYGRkBAOzatQttbW1YWlpCSkoKcnJyuH4/xcXFmJ+fx9DQEACgoaEBHR0dcLlcSEpKQkFBAY4cOQIAKCwshMfjwcDAAOf38ePHMT8/D6vViuLiYhw6dAgAUFBQAADo6+sDAOzYsQOnTp2C0+lEQkICysvL0dbWxvltNBrR29sLANi+fTsOHjwIm80Gi8WCqqoqtLS0AACys7ORkJCAU6dOAQAqKysxODiIqakpxMXFoa6uDk1NTVw82e12nDx5kvN7dHQUExMTMBgMaGhoQHNzM3w+H9LT05GamooTJ04AAEpLSzE1NYWxsTHodDo0NjaitbUVHo8HqampyMzM5PwuKSmB0+nk4rqxsRHt7e1YXFxEcnIy8vLy0NHRgampKdTX18PlcmFwcBAAUF9fj6NHj8LlcsFut6OwsJCL2c2bN8Pr9XJ+19bWoqurC3Nzc7BarSgpKUF7ezsAID8/H3q9Pihme3p6MDs7i/j4eFRUVHB+5+bmwmQyoaenh/O7v78f09PTMJvNKNtWyRUhzE2OYBx2zu9t27ZheHgYc05/gt3HMGhubkZKSgoyMjKQlJTE+V1eXo7x8XGMj49zMRspik7QEMoi3CbBNupBQxCEBuB60KxRQbMWbB8axwJd+whCKo4P+6c3lWcrq59HeZYdzx8dwXGFVNAQ8sGuBMPCMMw5j63ka1/7Gm6//fagx8xmM8zm5QRkdXV10PO7du0CADQ1NSEnJwc5Ocurh1VWVq65LcvKqRsVFRW826anp3O/l5WV8W67MnFmtwdXtzU2Nq772uTkZGzZsoX7u6GhAU1NTSgpKTln29TUVBQWFnJ/19XV8Y6JTagA/mQK37a5ubnc79u3b+fdNjs7m/t9I79XJuxsNlvQ86u33bRpOblbWlrKu9+Vf9vtds4vAOckDVZum5KSEuR3fX39utumpaVh8+bN3N+1tbXnbNvU1MTFSX5+PvdcOH5XVVXxbrsyZrdt28a7bUZGBvf7ypgdnnEB8C+lXbm1CDqdLshvm82G5KRxYGISPoZBWkrKuucqKSkJxcXFiBZFJ2hWXxyIYKT2x+levw/DWthk7EFDscMP+RMM+cFPrPvDTlVa69q3lje2QBKbpjhR7KxGq34oQRebACnPEq5BsBC6KgIJI6VMcVLCuRIDJetiv0QODw8HfYEfHR09p6pmJauTMeGgZD+igXSpB7Vomgj0n0lJMK2bMF05xUkKXYruQcOWqhFrI7U/4U5xYnvQyLHMNsUOP+RPMOQHP7Huj4O79p1bQbOWNzauBw1V0MR67KxGq34oQdfxIX8CpCxLuAoaIXSVBRJGXSOz8Hh9Ue8vWpRwrsRAybqKioqQlZWFF198kXtscXERr732Gs4//3xRjqlkP6KBdKkHtWha7j+zfpW0IdAk2McwkuhSdAXN5OSk3ENQNFL7wyZaQu5BY2anOEl/F5lihx/yJxjyg59Y94e9htnXuPat5Q07FYp60FDsrEarfsiti2EYbopThYAVNELoKkhNQHycAQtLXvROzKMkQ97GmXKfK7GQW5fT6eR6rQD+xsDt7e1ITU1FQUEBvvSlL+G73/0utm7diq1bt+K73/0uEhIS8LGPfUyU8cjth1iQLvWgFk2T84sA1m8QDKysoJFGl6ITNEajoocnO1L74wizB42cTYIpdvghf4IhP/iJdX/YXjJrVdCs5Q1XQUM9aGI+dlajVT/k1jU668bU/BL0OmBrpnAJECF0GfQ6lGbZcKh/GseHHbInaOQ+V2Iht66WlhZcdNFF3N9s75gbbrgBjz76KL761a9iYWEBn/3sZzE1NYVdu3bhhRdegM0mTs8kuf0QC9KlHtSiadLJv8Q2EFxBI4UuRTu3ujkREYzU/jh5+jCshZxNgil2+CF/giE/+Il1f7gKmvhzr31reWO3UAUNS6zHzmq06ofcutj+M0WbEmGJMwi2X6F0VQQSNCeGZ3Fl9cbbi4nc50os5Na1e/duMAyz7vM6nQ733HMP7rnnHknGI7cfYkG61INaNE3O8y+xDQCB/Ax8Pgb1jeLrUnQPGnaZNmJtpPZneRWnUJsEB3rQyJCgodjhh/wJhvzgJ5b9cXu8cHv8fSPWqqBZyxvqQbNMLMfOWmjVD7l1HR8KrOAk4PQmQDhdbF+cziH5GwXLfa7EQqu6IkWrfpAu9aAWTZOBJsGpCSFMcWIYSXQpOkHDl4kmpPcn3B407HYLS14sSdwYj2KHH/InGPKDn1j2h01M63TLK9OtZC1vqAfNMrEcO2uhVT/k1rW8gpOw00WE0sUmjtg+OXIi97kSC63qihSt+kG61INaNLFNglNCmuIkjS5FJ2jYtdOJtZHSH4/Xh/lFL4C17yKvhXVFImdO4j40FDv8kD/BkB/8xLI/jgX/G7fVZIRef+7yi2t5Y+d60FCCJpZjZy206ofcurgETbawFTRC6WITRwNTC7InbuU+V2KhVV2RolU/SJd6UIsmdplt3ilObILGx0iiS9EJmpSUFLmHoGik9GfO7eV+D3WKU5xBD0ucP8Sk7kNDscMP+RMM+cFPLPsz6+KvHFzLG7YHDU1xiu3YWQut+iGnriWvD92j4lTQCKUrJdGETLsZAHBiWN5pThSDsYFW/SBd6kEtmpaX2Q5lFSdGEl2KTtB0dXXJPQRFI6U/s25/8JqNepiMoYcNu+KT1Akaih1+yJ9gyA9+Ytkf9trFTltazVresFWGcvTfUhqxHDtroVU/5NTVMz6HJS+DRJMBucnxgu5bSF1lgWlOJ0bkTdBQDMYGWvWDdKkHtWgKZZltA9skmGEk0aXoBA2hHDa6i7webKm/U4altgmCIKKFnY4QauUgsDy9k657BCE+bEVKaZZtzWmISoGt7umSuYKGIAiC8MMwDKbm/AmaFL4mwSuW2ZYCRSdoysrK5B6CopHSn+UGwaH1n2GxckttSzvnmmKHH/InGPKDn1j2Z3aD5uhreWNbkaDx+tTRJE8sYjl21kKrfsipi03QlGUKO70JEFZXaWB8x2VO0FAMxgZa9YN0qQc1aJpZWIIn8DktzRrKFCdpdCk6QTM5OSn3EBSNlP5EchcZCP6iIiUUO/yQP8GQH/zEsj9s9aB1neT0Wt6svE7OLcZ2FU0sx85aaNUPOXWxCY8ygfvPAMLqYitoTozMyrq6CcVgbKBVP0iXelCDpnGnv3rGZjHCbDSsu51Bt1xBI4UuRSdoxsbG5B6CopHSH+5LSpgJGnZ7qZtlUuzwQ/4EQ37wE8v+ODeY3rmWN5Y4A0wGeRqkK41Yjp210KofcurqGhEvQSOkrpIMK/Q6YHp+CWOzbsH2Gy4Ug7GBVv0gXepBDZomnP5r8SarmXc7doqT18dIokvRCRq9XtHDkx0p/Ym0B41czTIpdvghf4IhP/iJZX/Y6kHbOsnp9byRa3qn0ojl2FkLrfohl645twd9k/MAxJniJKQuS5wBhWmJAOSd5kQxGBto1Q/SpR7UoGki0H8mjadBMAAE7rnBxzCS6FK0czt37pR7CIpGSn/YKUrWMBM0bAWN0y3tlxSKHX7In2DID35i2R/nBj1o1vOGm94Z4xU0sRw7a6FVP+TSdXLUCcB/9zNtgzugkSC0LrbKp0vGlZwoBmMDrfpButSDGjSxFTR8/WeA5R40Ph8jiS5FJ2haWlrkHoKikdIf9i6wPcwmwTbuLrK0X1Iodvghf4IhP/iJZX82mt65njfs9rE+xSmWY2cttOqHXLpODDsALPd3ERqhdbEJGjkraCgGYwOt+kG61IMaNLE9aDac4sQ2CWYYSXQpOkHj9XrlHoKikdIfZ4Q9aOS6i0yxww/5Ewz5wU8s+zO7wQp263nDJadjfKntWI6dtdCqH3LpOjHsr6ApFWF6EyC8LnYalpwVNBSDsYFW/SBd6kENmsa5Chr+BI1Bv7yKkxS6FJ2gSUtLk3sIikZKfyLtQWM1+7/USN0kmGKHH/InGPKDn1j2h1vBbp1r33resNe+WO9BE8uxsxZa9UMuXSdGxK2gEVrXyilOXp88KzlRDMYGWvWDdKkHNWia4CpoNpri5P+XYRhJdCk6QZORkSH3EBSNlP7MRtiDZnmZbWm/pFDs8EP+BEN+8BPL/my0itN63tipBw2A2I6dtdCqH3LpYitoxFjBCRBe1+a0RJiNeriWfOgPNDeWGorB2ECrfpAu9aAGTRNzgQqaxNBXcZJCl6ITNJ2dnXIPQdFI6U+kU5ysMvWgodjhh/wJhvzgJ5b9Ya9d6/XfWs8bufpvKY1Yjp210KofcuiacLox7nRDpwO2ZlpFOYbQugx6HTdWufrQUAzGBlr1g3SpBzVoYitoNmoSbFjRg0YKXYpO0BDKYdYdWZNg7i5yjPdhIAhCnXAr2EWYnKZrH0GIw4lAH5f8lAQkmML7/yknZZl2APL2oSEIgiCWe9BsNMWJ7UHDSDQzVdEJmq1bt8o9BEUjpT9cBU2EPWikLvOn2OGH/AmG/OAnVv3x+pgNl9lezxu2qbAjxnvQxGrsrIdW/ZBD14lABYpY05sAcXSVZfkraE7IVEFDMRgbaNUP0qUelK5p0ePjeqRutIqTTrc8xUkKXYpO0DgcDrmHoGik9CfSJsFylflT7PBD/gRDfvATq/7MLS5ft9ZLTq/nDVtxE+s9aGI1dtZDq37IoYutQBGrQTAgjq6yLH8FzQmZKmgoBmMDrfpButSD0jVNzvmnNxn1ug1niKyc4iSFLkUnaEZGRuQegqKR0p/ZKMv8F70+uD3SLbdGscMP+RMM+cFPrPrDJpZNRj3MRsOa26znDfWg8ROrsbMeWvVDDl1sBYpYS2wD4uhil9ruGZ+T9HMRC8WgPHg8Hnzzm99EUVER4uPjsWXLFnz729+Gz+cT5XhK9yNSSJd6ULomdnpTaqKJawK8HoZAxsTnYyTRpZ5Ju4RsuD1eLHr8byC2MHvQWFfMC591eWC2rv0lhyAIQmmwS2TbwkxMAytXsIvtBA1BiAHDMOgaEXcFJ7HItJthtxjhcHlwanQO23Lscg+JkIB///d/x89+9jP85je/QWVlJVpaWnDTTTchKSkJX/ziF+UeHkHEHGyCJm2D6U3A8ipOPoma0Ci6gmbXrl1yD0HRSOXPyhL9cCto9Hod9xop7yRT7PBD/gRDfvATq/5stMQ2sL43bDJ7NsZ70MRq7KyHVv2QWtfZ6QU43R7EGXQo2pQo2nHE0KXT6VCeJV+jYIpBeXjnnXdw9dVX44orrkBhYSGuu+46XHbZZWhpaRHleEr3I1JIl3pQuiZ2BaeNGgQDgJ7rQSONLkUnaNra2uQegqKRyh82sZJoMnBdrMOBu5MsYYKGYocf8icY8oOfWPVnNoTm6Ot5w/WgifEKmliNnfXQqh9S62KnNxWnWxFnEO+jrFi6SrPkW2qbYlAeLrzwQvzjH/9AV1cXAODQoUN488038b73vW/d17jdbjgcjqAft9sd0vGU7kekkC71oHRNE3OBCprEjRM0bA8aH8NIokvRU5yWlmL7zuNGSOVPKF9S+FiuoJHufFLs8EP+BEN+8BOr/rC9t2zm9ad2rucNm5h2xHgPmliNnfXQqh9S62Ib7IrZfwYQTxfbh0aOChqKQXm44447MDMzg/LychgMBni9XnznO9/BRz/60XVfs2/fPtx7771Bj9122224/vrrAQB1dXXo7OzEwsICbDYbioqKcPjwYQCAy+XC4OAg+vv7AQA1NTXo7u6G0+lEYmIiSktLcfDgQQBAXl4eDAYDzpw5AwCorq5Gb28vHA4HLBYLKisr0draCgDIycmBxWLB6dOnAQBVVVUYGBjA9PQ0TCYTampqcODAAQBAVlYWrFYruru7AQAVFRUYGRnB5OQkjEYj6uvrceDAATAMg/T0dKSkpHAJrLKyMkxOTmJsbAx6vR47d+5ES0sLxsfH0d3djYyMDHR2dgLwrxbkcDi43iC7du1CW1sblpaWkJKSgpycHBw9ehQAUFxcjPn5eQwNDQEAGhoa0NHRAZfLhaSkJBQUFODIkSMAgMLCQng8HgwMDHB+Hz9+HPPz87BarSguLsahQ4cAAAUFBQCAvr4+AMCOHTtw6tQpOJ1OJCQkoLy8nPtin5eXB6PRiN7eXgDA9u3bMTk5iaamJlgsFlRVVXGVVdnZ2UhISMCpU6cAAJWVlRgcHMTU1BTi4uJQV1eHpqYmAEBmZibsdjtOnjzJ+T06OoqJiQkYDAY0NDSgubkZPp8P6enpSE1NxYkTJwAApaWlmJqawtjYGHQ6HRobG9Ha2gqPx4PU1FRkZmZyfpeUlMDpdGJ4eBgA0NjYiPb2diwuLiI5ORl5eXno6Ojg9sfGIgDU19fj6NGjcLlcsNvtKCws5GJ28+bN8Hq9nN+1tbXo6urC3NwcrFYrSkpK0N7eDgDIz8+HXq8Pitmenh7Mzs4iPj4eFRUVnN+5ubkwmUzo6enh/O7v78eRk2cBAGlWE+dhVlYWEhMTOb+3bduG4eFh9Pf7t/X6GIyOjqKpqQkZGRlISkri/C4vL8f4+DjGx8e5mI0UHcNItaJ3+HR1daG0tFTuYSgWqfx5u3scH/tVE0oyrHjp9veG/fprf/oW2vqm8bNP1OPyqiwRRnguFDv8kD/BkB/8xKo/v286g2881YFLt2Xil59qWHOb9byZmV/Cjm+/AAA4cf/l6zYZ1jqxGjvroVU/pNb1pScO4un2QXzln8pw60Uloh1HLF0Heibx4Z+/g9zkeLx158WC758PikF5eOKJJ/CVr3wFDzzwACorK9He3o4vfelL+MEPfoAbbrhhzde43e5zKmbMZjPM5o17Zijdj0ghXepB6Zq+/D+H8Oe2AdxxeTn+dXcx77aPvNWDe585hiurs/GFBqvouhRdQZOTkyP3EBSNVP44Ilxim0WOXgwUO/yQP8GQH/zEqj+h9KBZz5uVFYfOGG6QHquxsx5a9UNqXezUoDKRK2jE0lWa6Z/idHZ6AbOupbAXYIgGikF5+MpXvoI777wTH/nIRwD47+KfOXMG+/btWzdBE2oyZi2U7kekkC71oHRN3BSnEHrQGFY0CZZCl6J70LDlaMTaSOUPt5JJhB8g5FhulmKHH/InGPKDn1j1h71m8a3itJ43Br0OCSZ/UiaW+9DEauysh1b9kFLXkteH02NzAMRfwUksXckJJmTa/V+82dWopIJiUB7m5+eh1wd/7TIYDKIts610PyKFdKkHpWtiV3EKr0kwI4kuRSdoCGUwK1gFTex+SSEIQn2wiRU1JacJQuucmZjDoteHBJMBucnxcg8nYkpl7ENDSM9VV12F73znO3j22WfR29uLp556Cj/4wQ/wgQ98QO6hEURMwq7ilJa4cZXacgWNqEPiUPQUp+Ji/vlgsY5U/rBfLuwRJmjsFumbBFPs8EP+BEN+8BOr/jgC1yy+Bul83ljNRozAHdMJmliNnfXQqh9S6mKnN23NtEEfwcqS4SCmrvIsG944Oc6tSCUVFIPy8F//9V+466678NnPfhajo6PIycnBLbfcgm9961uiHE/pfkQK6VIPStbEMMxygiakChr/vz4fI4kuRSdo5ufn5R6CopHKH6dbmClOUpb5U+zwQ/4EQ37wE6v+hNKDhs8bOfpvKY1YjZ310KofUupiExrlIvefAcTVxVbQHB92iHaMtaAYlAebzYYHH3wQDz74oCTHU7ofkUK61IOSNc26PVj0+qcXbrJuXEHDTXFiGEl0KXqKE7sEGrE2UvkTSh8GPuSY4kSxww/5Ewz5wU+s+rM8vXP95DSfN3Ikp5WG0mNn37592LlzJ2w2GzIyMnDNNddwy46KgdL9iBQpdXENgkXuPwOIq6s8yw7An3CSckFVisHYQKt+kC71oGRNbPVMoskAS9zGiziwU5y8PkYSXYpO0BDKIPoeNP7XOWL4LjJBEOqD60ETcXKaetAonddeew233nor9u/fjxdffBEejweXXXYZ5ubm5B4asQ5cBY0ECRox2ZpphV4HTM0vYWzWvfELCIIgCEEYdbgAABl2S0jbsxU0UuXSw0rQSH2nqaGhQbR9awGp/HFEuYqT1Sz9lxSKHX7In2DID35i1Z/lFezWT9DweWMz+6+ZsVxBo/TYee6553DjjTeisrISO3bswCOPPIK+vj60traKcjyl+xEpUumac3vQN+kvL5eigkZMXZY4AwrTEgEsVwVJAcVgbKBVP0iXelCyppFAUjzdFtoy9voVFTRS6AorQSP1naaOjg5R9qsVpPJHuFWcpKugodjhR+n+SJ0MVrofchOr/rDXPr4mwXzeWKl6UHWxMzMzAwBITU1ddxu32w2HwxH043aHVgGhNj9CRSpdJ0f9S1JvspqRFkLfgGgRWxebZJJyJSeKwdhAq36QLvWgZE1sBU1miBU0hhU9aKTQFdY37ueeey7o70ceeQQZGRlobW3Fe97zHkEHBgAul0vwfWoJqfyZjbKCRo4yf4odfpTuD5sM3rlzJzweD77xjW/gsssuw7Fjx5CYmCj48ZTuh9zEqj+zISyzzecN14Mmhqc4qSl2GIbB7bffjgsvvBBVVVXrbrdv3z7ce++9QY/ddtttuP766wEAdXV16OzsxMLCAmw2G4qKinD48GEAfj8GBwfR398PAKipqUF3dzecTicSExNRWlqKgwcPAgDy8vJgMBhw5swZAEB1dTV6e3vhcDhgsVhQWVnJVfrk5OTAYrHg9OnTAICqqioMDAxgenoaJpMJNTU1OHDgAAAgKysLVqsV3d3dAICKigqMjIxgcnISRqMR9fX1OHDgABiGQXp6OlJSUtDV1QUAKCsrw+TkJMbGxqDX67Fz5060tLRgfHwciYmJyMjIQGdnJwBg69atcDgcGBkZAQDs2rULbW1tWFpaQkpKCnJycnD06FEA/tU+5ufnubn9DQ0N6OjogMvlQlJSEgoKCnDkyBG83OuPp8KUODQ1NXF+Hz9+HPPz87BarSguLsahQ4cAAAUFBQCAvr4+AMCOHTtw6tQpOJ1OJCQkoLy8HG1tbZzfRqMRvb29AIDt27djbGwMTU1NsFgsqKqqQktLCwAgOzsbCQkJOHXqFACgsrISg4ODmJqaQlxcHOrq6rjxZWZmwm634+TJk5zfo6OjmJiYgM23AAB4s6MH201jSE9PR2pqKndDorS0FFNTUxgbG4NOp0NjYyNaW1vh8XiQmpqKzMxMzu+SkhI4nU4MDw8DABobG9He3o7FxUUkJycjLy8PHR0dmJqaQk5ODheLAFBfX4+jR4/C5XLBbrejsLCQi9nNmzfD6/ViYGAAAFBbW4uuri7Mzc3BarWipKQE7e3tAID8/Hzo9fqgmO3p6cHs7Czi4+NRUVHB+Z2bmwuTyYSenh7O7/7+fkxPT8NsNqO6uhrNzc1czCYmJnJ+b9u2DcPDw5icnOT8HhoagsvlQkZGBpKSkji/y8vLMT4+jvHxcS5mYwE1XXvDgXSpByVrYqeVZoRYQWMIlLT4fIwkunRMFJ3Juru7sXXrVhw5cmTdDzNut/ucO0tmsxlm88aGHD9+HOXl5ZEOT/NI5U/jd17C6Kwbf/v8hajKTQr79X0T83jPA68gPs6AzvsuF2GE50Kxw4/a/BkbG0NGRgZee+01UZLBavNDamLRH7fHi7Jv+m9KHLr7MiTFr52k4fPmV2+cxv3PduL9O3Lw44/WijZWJaOm2Ln11lvx7LPP4s0330ReXt6629HnmnORSte9zxzFI2/14uYLi3DXldtEP57Yup7rGMJnHmvD9twkPPP5C0U7zkooBmMDrfpButSDkjV96YmDeLp9EF/bW45b3rvxstnPHx3GLb9rRV1BMr57cZrouiJeZluKO01ZWVmavNPk9XqRlpYW9Z2miYkJNDU1rXunCQAKCwvh8Xi4Ox+R3Gmamfd/ELUYwd0RWutOU19fH2ZmZs6505SQkg4AWFjy4q139mPH9qqI7jQZDAY0NDSgubkZPp+P906Tz+dfOi3aO00AsGXLFs3daZqcnERTU5Nq7jSFOu0g0i9NbOwTaxOL/qyserHyNAnm88ZuoR40aomdz3/+8/jrX/+K119/nTc5A4R+XVkLtfgRLlLpOiHhCk6A+LrKAis5dY3MwutjuJVCxIRiMDbQqh+kSz0oWdNooIIm/ClO0uiKuIJGijtNTU1N2LVrVyTDiwmk8GfJ68PWb/wdANB216VITTRFtY+Dd12KlAj2ES4UO/yoyR+GYXD11VdjamoKb7zxxrrb3XPPPVFNO9i6dSslg9dJBh87dgwpKSmiJ4NDnXawXjJYqGkHBoMBmworsPs/XoXFqMP/fbp83WTw1NQULr/88jWTwfvPuvHDA7PYnpWAb56XACD2ksHPPfccUlJSFJsMZhgGn//85/HUU0/h1VdfxdatW0U9npquveEgla76+17ExNwi/nLrBdiRnyz68cTW5fUxqLz7ObiWfHj5y+/FlnSraMdioRiMDbTqB+lSD0rWtOc/X8WpsTk8/v924fySTRtu/8rxUdz0aDO25ybhm41xouuKqIJGqjtNhPzMrbjzG2mT4DiDHvFxBiwseeF0eyRJ0BDa4XOf+xwOHz6MN998k3e7r33ta7j99tuDHlt9/amurg56nr3ANjU1IScnBzk5OdxzlZWVa27LkpWVxf1eUVHBu216ejr3e1lZGe+2aWlp3O92uz3oucbGxnVfm5ycjC1btnB/r+4yv3Lb1NRUFBYWcn/X1dXxjmloaIh7bMeOHbzb5ubmcr9v376dd9vs7Gzu9438zszM5H5fXVq6ettNm5bfbEtLS3m3Xfm33W5HSUkJAKDjrL9qKznBzPm6ctuUlBRs2bKFS/jU19efs9/Fk2PAgQNYgiHotbW1tedsu5L8/Hzu93D8Xl3Nyhez27Zt4902IyOD+32jmF3pt80WXNmQkpIStP3K35OSklBcvHFpsZjceuutePzxx/GXv/wFNpuNq6pMSkpCfHy8rGMjghmbdWNibhE6HVCaqe4ltlkMeh22Zthw5OwMTgzPSpKgIQiCiHXYCppQl9kOFNDAJ9E622Gt4sQwDD73uc/hySefxMsvv4yioiKxxgUAQV8giHORwh+2sa8lTo84Q1jhEoRN4tVMKHb4UYs/bDL4lVdeCSkZbLfbg35CTQ6rxQ+5iEV/2GsV3wpOAL837NQoKRukKw2lx85DDz2EmZkZ7N69G9nZ2dzPH//4R1GOp3Q/IkUKXez0ps2pCYg3GUQ/HiCNLna6llRLbVMMxgZa9YN0qQelalpY9HKfyzLsoTYJXl5mWwpdYZVESH2nyeOJ3Q+1oSCFP44oV3BisVmMGJ11S/ZFhWKHH6X7s3ragdjJYKX7ITex6A/bg2ajykE+b2zUg0bxsRPFOgkRoXQ/IkUKXceHHQCk6z8DSKOrPKDnhEQJGorB2ECrfpAu9aBUTaOz/lWYLHF62Hh6DK6E7UHjYxhJdIVVEiH1nSZ2bj2xNlL4Mxvil5SNsAa+qEiVoKHY4Ufp/tx666147LHH8Pjjj3PJ4OHhYSwsLIhyPKX7ITex6A97reJrEAzwe8NeN2ddS5InApRCLMYOH1r1Qwpdyw2C7RtsKRxS6CoP6DkxIk2ChmIwNtCqH6RLPShV08oGwTpdaI3Z9SsqaKTQFda37lj9gBnLLCdooqugsa/4okIQG/HQQw8BAHbv3h30+COPPIIbb7xR+gERMQd7rbJHce1jX+tjgPlFLxJDvFNDEMS5sFOAtmVro/8MS3lAT+/EHOYXPUgw0XWCIAhCLEYc/gqaDFvoPXL1gUSOVKkQRb8LrG5cSQQjhT/LX1KiC5XlO8nSVNBQ7PCjdH+kTgYr3Q+5iUV/HIFrlT2eP0HD542/d5cOS14GDtdSTCZoYjF2+NCqH2Lr8nh96ApUmJRLWEEjxfnaZDVjk9WMcacbXSNO1Ii8OhXFYGygVT9Il3pQqqZRR3gNggGAbcPqZRhJdEXe9VUCjh8/LvcQFI0U/gg1xclmZqc4SVNBQ7HDD/kTDPnBTyz6M7MQSE7H81/7+LzR6XRcFQ27v1gjFmOHD636Ibau3ok5uD0+JJgMKEhNEPVYK5HqfFUEqmg6hxyiH4tiMDbQqh+kSz0oVRO3glMEFTReHyOJLkUnaObn5+UegqKRwh82ocImWCJF6goaih1+yJ9gyA9+YtEfRyChkrRBBc1G3rCvdywos1me2MRi7PChVT/E1tU5xPafsXG9AKRAqvNVke2vCjouQYKGYjA20KofpEs9KFXTKDfFKZwKmkCTYB8jiS5FJ2isVqvcQ1A0UvjDNcqMeopT4EuKRAkaih1+yJ9gyA9+YtEfR4g9aDbyxsYlaGKzgiYWY4cPrfohti52BScppzcB0p0vdiWnTglWcqIYjA206gfpUg9K1bTcJDj8ChofI40uRSdoiouL5R6CopHCn1m3QFOcAq+XarlZih1+yJ9gyA9+YtEftuJlox40G3nD9u+K1SlOsRg7fGjVD7F1HQ9U0FRI3CBYqvPFJp6ODzlE78FGMRgbaNUP0qUelKqJXWY7nAoabooTw0iiS9EJmkOHDsk9BEUjhT9CreJkk3gVJ4odfsifYMgPfmLRH64HzQbJ6Y28YRM8jhhdwS4WY4cPrfohti52BSepK2ikOl/FGYkw6nVwuDwYmnGJeiyKwdhAq36QLvWgVE0jXJPg0CtoVk5xkkKXohM0hPxwPWhUtooTQRBENHBTnDaooNmIWO9BQxDRMjO/hLPTCwCWl6TWGmajASUZ/rJ5KRoFE/Jw9uxZfOITn0BaWhoSEhJQU1OD1tZWuYdFEDGDa8nL3YDLDKuCxv+vT6JVZhWdoCkoKJB7CIpGCn/YhEr0y2xLu4oTxQ4/5E8w5Ac/sehPqE2CN/LGbontCppYjB0+tOqHmLrY/jO5yfEb9oQSGinPF9uH5rjIfWgoBuVhamoKF1xwAeLi4vD3v/8dx44dw3/+538iOTlZlOMp3Y9IIV3qQYmaxgL9Z0xG/YardK6EbU7v9TGS6IruWzeheZYraNS1ihNBEESk+HwM138r2i+E7AeAWO1BQxDRwiYspO4/IzXl2XagfZAqaDTKv//7vyM/Px+PPPII91hhYaF8AyKIGITtP5NpN0OnC31FQMOKJsFSoOgKmr6+PrmHoGik8Ge5B41QFTTSJGgodvghf4IhP/iJNX9m3R6wVawbXfs28oaroInRBE2sxc5GaNUPMXXJtYITIO354lZyEjlBQzEoD3/961/R0NCAD33oQ8jIyEBtbS1++ctf8r7G7XbD4XAE/bjd7pCOp3Q/IoV0qQclauL6z4QxvQlY7kHj9TGS6KIKGoIXoZsEO90eeH0MF+gEQRBKg02mmI16WOIMUe0rKcabBBNEtBwb9CcsKrKlT9BIybaAvp7xOSwsehFviu7aQyiL06dP46GHHsLtt9+Or3/96zhw4AC+8IUvwGw241Of+tSar9m3bx/uvffeoMduu+02XH/99QCAuro6dHZ2YmFhATabDUVFRTh8+DAAwOVyYXBwEP39/QCAmpoadHd3w+l0IjExEaWlpTh48CAAIC8vDwaDAWfOnAEAVFdXo7e3Fw6HAxaLBZWVlVyvnJycHFgsFpw+fRoAUFVVhYGBAUxPT8NkMqGmpgYHDhwAAGRlZcFqtaK7uxsAUFFRgZGREUxOTsJoNKK+vh4HDhwAwzBIT09HSkoKurq6AABlZWWYnJzE2NgY9Ho9du7ciZaWFkxNTaG7uxsZGRno7OwEAGzduhUOhwMjIyMAgF27dqGtrQ1LS0tISUlBTk4Ojh49CsC/stD8/DyGhoYAAA0NDejo6IDL5UJSUhIKCgpw5MgRAP4KJ4/Hg4GBAc7v48ePY35+HlarFcXFxVzDWHbaC/vlfceOHTh16hScTicSEhJQXl6OtrY2zm+j0Yje3l4AwPbt2zE7O4umpiZYLBZUVVWhpaUFAJCdnY2EhAScOnUKAFBZWYnBwUFMTU0hLi4OdXV1aGpqAgBkZmbCbrfj5MmTnN+jo6OYmJiAwWBAQ0MDmpub4fP5kJ6ejtTUVJw4cQIAUFpaiqmpKYyNjUGn06GxsRGtra3weDxITU1FZmYm53dJSQmcTieGh4cBAI2NjWhvb8fi4iKSk5ORl5eHjo4Obn9sLAJAfX09jh49CpfLBbvdjsLCQi5mN2/eDK/Xy/ldW1uLrq4uzM3NwWq1oqSkBO3t7QCA/Px86PX6oJjt6enB7Ows4uPjUVFRwfmdm5sLk8mEnp4e7O/29zOz6pfQ1NQEs9mM6upqNDc3czGbmJjI+b1t2zYMDw/jxMAYAH8PmqmpKTQ1NSEjIwNJSUmc3+Xl5RgfH8f4+DgXs5GiY8Rezy8KXC4XLJbwMlyxhNj+eH0Mir/+fwCAlm9egk3W0Ltdr8bt8aLsm88BAA7dfdmGfR2ihWKHH/InGPKDn1jz5+jgDK748ZvIsJlx4BuX8G67kTevdY3hhl8fwLZsO/7vi+8WeqiKJ9ZiZyO06odYujxeHyrvfh5ujw+v/ttuFG5KFPwYfEh9vhrufwnjTjeevvUC1OQni3IMikF5MJlMaGhowNtvv8099oUvfAHNzc1455131nyN2+0+p2LGbDbDbN7487jS/YgU0qUelKjp288cw6/f6sEt79mCr72vIuTXDc0s4Lx9LyPOoMORuy4WXZeipzix2StibcT2x+leno4U7RQns9EAk9EfblI0CqbY4Yf8CYb84CfW/GFXXAplBaeNvGEbrMdqD5pYi52N0KofYuk6PT4Ht8eHRJMBBakJohyDD6nP17YcfxXN0cEZ0Y5BMSgP2dnZ2LZtW9BjFRUVvNMlzGYz7HZ70E8oyRlA+X5ECulSD0rUNBhYETA3JT6s17E9aLw+RhJdik7QOJ1OuYegaMT2h02kmIx6mI3Rl9raJWwUTLHDD/kTDPnBT6z5wyZTQlm9biNvYn2KU6zFzkZo1Q+xdK2c3qSXYWq01OeLnebE6hYDikF5uOCCC7jpJCxdXV3YvHmzKMdTuh+RQrrUgxI1Dc74EzQ5SeElaHQrmgTPzoq70h6g8ARNQoL0d0vUhNj+ON3CLLHNwvaxWVmZIxYUO/yQP8GQH/zEmj9sMiWUCpqNvGH34XR74JOq/b+CiLXY2Qit+iGWrmOBhrmVOfL0n5H6fLE6j4nYKJhiUB5uu+027N+/H9/97nfR3d2Nxx9/HL/4xS9w6623inI8pfsRKaRLPShR09mpQIImOcwKmhU3COIl0KXoBE15ebncQ1A0YvvDVrpYzcIkaNj9SDHFiWKHH/InGPKDn1jzh20SHEqvrI28YaeHMox0q9gpiViLnY3Qqh9i6WKn+myTKUEj9flidR4fmoVXpIQuxaA87Ny5E0899RT+8Ic/oKqqCvfddx8efPBBfPzjHxfleEr3I1JIl3pQmibXkhcTc4sAgNxwEzQrluTeWlom6LjWQtEJGrb7MrE2YvszMx/6XeRQYL/sSNGLgWKHH/InGPKDn1jzx+Fiqwc3vvZt5I3ZaIAlTh/Yb+xNc4q12NkIrfohhi6GYbipPtuykwTffyhIfb4K0xIRH2fAwpIXPeNzohyDYlA+rrzyShw5cgQulwudnZ3453/+Z9GOpQY/IoF0qQelaWL7zySaDLDHh1d8oF+RMWltOyjksNY+nuhHIFTLdCCRkpxgEmR/SQn+LzvT87H3JYUgCPXAVtCE+wa+HlImpwlCKww7XJiaX4JRr8PWTKvcw5EEg16HimwbAHGnOREEQcQag9MuAP7pTTpdeD3N9Cu2l2K2uqITNHl5eXIPQdGI7c/0vL8MLFmgChp2P1IkaCh2+CF/giE/+Ik1f7gETQgVNKF4w+4nFitoYi12NkKrfoihi62eKcmwwhIX/UIFkSDH+WKnOYnVKJhiMDbQqh+kSz0oTRNbQRNu/xkguAdNdk6OYGNaD0UnaIxGYe5eahWx/ZnhKmgEStAkSHcXmWKHH/InGPKDn1jzh02khNKDJhRv2GmijhisoIm12NkIrfohhq6j3PQmefrPAPKcL3Y6l1hLbVMMxgZa9YN0qQelaTobRYJmZQWNXi/+DQNFJ2h6e3vlHoKiEdsfttJFuAoaU2C/i4Lsjw+KHX7In2DID35izR/HQqAHTQjXvlC8YVfCY/cbS8Ra7GyEVv0QQxfXf0amBsGAPOerckUFDcMIX0tPMRgbaNUP0qUelKaJraDJTbaE/dqVFTS9Z/oEG9N6KDpBQ8gLW+mSJHQPmhi8i0wQhHqYCWOKUyiwlTixOMWJICKlg13BScYKGjkoy7LBoNdhYm4RIw633MMhCILQBIMz0VTQLP8uQQsaZSdotm/fLvcQFI3Y/nBNglXYg4Zihx/yJxjyg59Y84dNpITSJDgUb+wx3CQ41mJnI7Tqh9C6puYWMTDl/zBdmSvPCk6APOfLEmfA1gx/U+QjZ4Wf5kQxGBto1Q/SpR6Upmllk+Bw0el0YGc5lUqwfLiiEzR9feKXEKkZsf2ZYZsEC9aDxl+JI8WXFIodfsifYMgPfmLNH7ZXTCg9aELxhmsSHIMJmliLnY3Qqh9C62KrZwrTEkL6fygWcp2vqkBSSowEDcVgbKBVP0iXelCSJoZhuB40uREkaADAEMjQ9PefFWxc66HoBM3MjDgN0rSC2P5Mi9QkWIoeNBQ7/JA/wZAf/MSSPx6vD3OLXgChTXEKxZvlKU6x14MmlmInFLTqh9C6Dg/491clY/UMIN/52s4maAamBd83xWBsoFU/SJd6UJKmiblFLHp80OmATHv4PWiA5UbBMw5xVtgLOpboR4gCiyUyA2MFsf1hpyIlxQvTgyZ5RZm/T+RF5Cl2+CF/giE/+Iklf1YmUWyWjac4heINO1UqFitoYil2QkGrfgitqyNQOVKdJ2+CRq7ztT2PraARvlEwxWBsoFU/SJd6UJImtkFwhs0MkzGy9Ic+8LI4k1moYa1/LNGPEAVVVVVyD0HRiOmP18dwfRiEqqBh+zD4GGDWLe6dZIodfsifYMgPfmLJHzaJkmgywGjY+C0yFG/YSpxY7EETS7ETClr1Q2hd7NQeuSto5Dpf27LtMOh1GHe6BW8UTDEYG2jVD9KlHpSkaTCKJbZZ2ClOJVtLBRkTH4pO0LS0tMg9BEUjpj+zriWwN22Emv9tiTMgPs6/dvyMyI2CKXb4IX+CIT/4iSV/2MR0qNe9ULyxx/AqTrEUO6GgVT+E1LWyQbDcCRq5ztfKRsGHBZ7mRDEYG2jVD9KlHpSk6WwUDYJZ9IGlnA4dPiLImHiPJfoRCFXCTm+ymo2IC+EucqhwfWgWxO9DQxAEES6OBX91n13AxqRcD5qF2OtBQxDhwlbPFG1KFGypezXC9qHpEKFRMEEQRCzRPzkPAMiLJkETqKDxCTztdM1jiX6EKMjOzpZ7CIpGTH+mw1jFJBySJFpqm2KHH/InGPKDn1jyh52GFOoXw1C8ieUpTrEUO6GgVT+E1KWU6U2AvOdruQ+NsAkaisHYQKt+kC71oCRNvRNzAIDCTYkR78MQqKDZlJ4uyJj4UHSCJiEhQe4hKBox/ZkWeIltluUKGnG/qFDs8EP+BEN+8BNL/rDTkNjGvhsRijfsvhaWvFj0+CIfnAqJpdgJBa36IaSuI4EVnLbn2gXbZ6TIeb5WLrUtZKNgisHYQKt+kC71oCRNveOBBE1a5AkatoLGZBa/+bGiEzSnTp2SewiKRkx/ZgReYpslObAi1IzIS21T7PBD/gRDfvATS/6wTYJDneIUije2FdU4szHWhyaWYicUtOqHkLrYipHtucmC7TNS5Dxfy42CFzHscAm2X4rB2ECrfpAu9aAUTUteH9fXrHBT5EmjQAENzvT1CzEs/mOJfgRClbBTkJIFWmKbhaugEXmKE0EQRCRwFTQC9r4w6HWwmQNLbbuoDw1BrMe4042z0wvQ6YBKBVTQyIklzoDSTBsA4FD/tLyDIQiCUCmD0wvw+BhY4vTItEVe/cJOcZKgBY2yEzSVlZVyD0HRiOkPm0BJEriCJkmiKU4UO/yQP8GQH/zEkj9T8+H13wrVG7YiZ0rk6kGlEUuxEwpa9UMoXe190wCAknSrIhoEy32+avKTAQAHBUzQyK1JLLSqK1K06gfpUg9K0dQTmN60OTWRW4kpEtgpToVFRYKMi/dYoh8hCgYHB+UegqIR0x92laVkgZsEsxU5YlfQUOzwQ/4EQ37wE0v+TDr91740a2jVg6F6w+6P3X+sEEuxEwpa9UMoXe2BRASbmJAbuc9XbcAHNnElBHJrEgut6ooUrfpButSDUjSdmfCv4LQ5LbqeOGwFzcjYWNRj2ghFJ2impqbkHoKiEdOfmXmRetAksKuZiPslhWKHH/InGPKDn1jyZ3LOf21KTQwtQROqN+z+2P3HCrEUO6GgVT+E0nWw37+fmoJkQfYXLXKfL9aHI2dn4PEK02Bcbk1ioVVdkaJVP0iXelCKJraCpiiKFZyA5R40MzOz0Q5p42OJfoQoiIuTv7xVyYjpDzsFSfAeNBIts02xww/5Ewz5wU8s+TMx5wYQeoImVG/Y/U3EWIImlmInFLTqhxC6fD4Gh/v9DYKVUkEj9/kqTrfCajZiftGLrhGnIPuUW5NYqE3Xvn37oNPp8KUvfUmU/avNj1AhXepBKZrOBJbY3hzFCk4AuOlReqMh6jFteCzRjxAFdXV1cg9B0YjpD7vMtlp70FDs8EP+BEN+8BNL/rAVLmmJ5pC2D9WbNK6Cxh3ZwFRKLMVOKGjVDyF0nRpzYtbtQXycAWWB5rhyI/f5Muh12JHvX267XaA+NHJrEgs16WpubsYvfvELVFdXi3YMNfkRDqRLPShFEzvFKZoVnADAEOhBU1yyNeoxbYSiEzRNTU1yD0HRiOnPcgWNOnvQUOzwQ/4EQ37wEyv+eH0Md+0LtYImVG9SAwmfWKugiZXYCRWt+iGEroOBPivb85JgNCjj46kSzhdbTdTeL8x0ASVoEgO16HI6nfj4xz+OX/7yl0hJSRHtOGrxI1xIl3pQgiaP14e+yUCCJsoKGrYHzdFjnVGPayOU8Q5IKI7lHjTiLLM9s7AIRop1ygiCIEJkan6RWz4xReDqwbQY7UFDEKHCrlRUq5DpTUqhJt//Jf6ggI2CCfm49dZbccUVV+CSSy7ZcFu32w2HwxH043bHVhUmQUTD4LQLHh8Ds1GPLHvkS2wDgC5QQeOT4OurUfxDRE5mZqbcQ1A0YvnDMMt3kcVqErzkZTC/6EWiWZwQpNjhh/wJhvzgJ1b8YZMnyQlxId/BD9WbWG0SHCuxEypa9UMIXQf7/BUitQppEAwo43yxFTTdY07MupZgi3L5cSVoEgM16HriiSfQ1taG5ubmkLbft28f7r333qDHbrvtNlx//fUA/FNIOjs7sbCwAJvNhqKiIhw+fBgAYLFYMDg4iP7+fgBATU0Nuru74XQ6kZiYiNLSUhw8eBAAkJeXB4PBgDNnzgAAqqur0dvbC4fDAYvFgsrKSrS2tgIAcnJyYLFYcPr0aQBAVVUVBgYGMD09DZPJhJqaGhw4cAAAkJWVBavViu7ubgBARUUFRkZGMDk5CaPRiPr6ehw4cAAMwyA9PR0pKSno6uoCAJSVlWFychJjY2PQ6/XYuXMnWlpaMD8/j+7ubmRkZKCz01/JsHXrVjgcDoyMjAAAdu3ahba2NiwtLSElJQU5OTk4evQoAKC4uBjz8/MYGhoCADQ0NKCjowMulwtJSUkoKCjAkSNHAACFhYXweDwYGBjg/D5+/Djm5+dhtVpRXFyMQ4cOAQAKCgoAAH19fQCAHTt24NSpU3A6nUhISEB5eTna2to4v41GI3p7ewEA27dvB8MwaGpqgsViQVVVFVpaWgAA2dnZSEhIwKlTpwD4l64eHBzE1NQU4uLiUFdXx1WqZGZmwm634+TJk5zfo6OjmJiYgMFgQENDA5qbm+Hz+ZCeno7U1FScOHECAFBaWoqpqSmMjY1Bp9OhsbERra2t8Hg8SE1NRWZmJud3SUkJnE4nhoeHAQCNjY1ob2/H4uIikpOTkZeXh46ODszPz2NsbAwul4tb0am+vh5Hjx6Fy+WC3W5HYWEhF7ObN2+G1+vl/K6trUVXVxfm5uZgtVpRUlKC9vZ2AEB+fj70en1QzPb09GB2dhbx8fGoqKhAW1sbDo34P3PlJpnR3HyA87u/vx/T09Mwm82orq7m/k9mZWUhMTGR83vbtm0YHh7G5OQk3C5/JY7L5UZTUxMyMjKQlJTE+V1eXo7x8XGMj49zMRspOkbBZQyTk5NITU2VexiKRSx/Zl1L2H7PCwCA4/ddDkuccM2QGIZB2Tefw6LXh7fuvBi5yfGC7XslFDv8kD/BkB/8xIo/75yawEd/uR9b0hPx8pd3h/SaUL1p65vCtT99G7nJ8XjrzoujHKl6UEPsvP7663jggQfQ2tqKoaEhPPXUU7jmmmtEOZYa/IiEaHXNuT3Yfs/z8DHA/q/tQVZSdHc6hUIp5+vCf38ZA1ML+P3/24ULSjZFtS+laBIapevq7+9HQ0MDXnjhBezYsQMAsHv3btTU1ODBBx9c8zVut/ucihmz2QyzeeMeaUr3I1JIl3pQgqbfvtOLb/3lKC7dlolffqohqn1d/ZM3cWhgBg9eW45rGosFGuHahD3F6fXXX8dVV12FnJwc6HQ6PP300yIMyw+bkSLWRix/2P4wlji9oMkZwF8exjUKnhfvTjLFDj/kTzDkBz+x4s9yg+DQp3aG6k2sTnFSQ+zMzc1hx44d+MlPfiL6sdTgRyREq+tQ/zR8DJCdZFFMcgZQzvmqLfBPc2o9E30fGqVoEhql62ptbcXo6Cjq6+thNBphNBrx2muv4cc//jGMRiO8Xu85rzGbzbDb7UE/oSRnAOX7ESmkSz0oQVPvuL/qJdoltoHlKU4DZ89Gva+NCDtBI+UHGUIeZgLTm5IEbhDMwu53RuRGwYS6kTIZTBDA8gpLoTYIDgd2nwtLXiwsnvtBnJCPvXv34v7778e1114r91BilgO9kwCAhkJt3UEWip2F/gRNc8AnQn3s2bMHR44cQXt7O/fT0NCAj3/842hvb4fBIP7SvQQRa5wacwKIvkEwsNwkWIqpR2E3ANm7dy/27t0rxljOoaKiQpLjqBWx/GEraNgVl4SGXRlKzKW2KXb4UYM/bDL4pptuwgc/+EFRj6UGP+QkVvxhV1hKDXGJbSB0b6xmI0wGPRa9PkzMuZFnim65R7WgxdiJZtqBFv0AotfV0uuvDGksFG9Vm0hQyvnaGUhctZ2Zgsfri2qVK6VoEhql67LZbKiqqgp6LDExEWlpaec8LgRK9yNSSJd6UIKmrpFZAEBZljXqfbHLbGfn5Ea9r41QdJPg0dFR2O12uYehWMTyZ3rB/yUlSeAGwSzJ3BQn8RI0FDv8qMEfKZPBavBDTmLFn0imOIXqjU6nQ2qiCcMOF6bmlpCnrO+hoqHF2ImmcafJZEJmZqbmGnc6HA4UFBRE1LjT62O4qTvm2bNoahpRTOPOjo4OGAwG2Rt3To+OIiFOh7lFL5585QA223QRN+6cm5vD9u3bZW3cCQC5ubkwmUzo6enh/I6kcSfrd0tLCxITE0Vt3KkmtHjtBUiXmpBb08z8EoZmXACA0kxb1PvTB/LiU9MzUe9rI0RP0ERzp2liYgIlJSViDU31iOXPcgWNWFOc/F9+2ESQGFDs8KNFf+haIx6x4s9yBU3oCZpwvGETNBNzsbNMqhZj52tf+xpuv/32oMdWX2uqq6uDnt+1axcAoKmpCTk5OcjJyeGeq6ysXHNblqysLO731XckV2+bnp7O/V5WVsa7bVpaGvf76g/RjY2N6742OTkZW7Zs4f5uaGhAU1MTd55XbpuamorCwkLu77q6unP2e6h/GgtLb8FuMeK6S86HPlBGzjZSXW/8ubnLdzG3b9/Ou212djb3+0Z+r1wNyGAwBD2/ettNm5Yb9paWlvLud+Xfdrs96P/F6qTBym1TUlKwZcsW7Oo8gFdOjGHWkoldu4rW3DYtLQ2bN2/m/q6trT1nv01NTVyc5Ofnc8+F4/fqig++mN22bRvvthkZGdzvG8XsSr9ttuAvXImJieueq6SkJBQXi9vUMxJeffVV0fatxWsvQLrUhNyaTgSqZ3KT46Ne/Q4A9IEKGsfsbNT72gjREzTR3GlaXFzU5BJxXq8XaWlpUS8RNz09jaamJsHvNLUf9/uSHG/A0aNHQ77T1NfXh5mZmQ3vNNnN/gaAHSfPoM3uEGWJuOnpaQCIeok4ANiyZYvm7jSxsaOlO010rVHftSbSu9qhXmvCvat9dsx/V2RmdABAkeDXGqN3AQBw8NhJxE/30LVGpdeaUBO/a6HVPhPR6Gpe0X+GTc4oBSWdr51FqXjlxBiaeyZx84VFG79gHZSkSUi0qitStOoH6VIPcms6MewAAJRlRV89Ayz3oNHpIp9iGipRLbOt0+k2XI4ymrvahDx8/akjeLypD1/csxW3XVq68QvC5FdvnMb9z3biyups/ORjdRu/gIh56FpDSME//fB1nBiZxe9ubsS7t6Zv/IIw+eITB/GX9kF884oK/L93b9n4BYTkhHKtIYTllt+14PmjI/jq5WX47G5t3UEWkubeSXzoZ+8gLdGElm9ewq0oQhAEQZzLN546gt839eFfdxfjjsvLo97fDb8+gNe6xvAfH9qB6+rzBBjh+oieAopmiTj2Lh2xNmL5M+rwf8lNt4nzxTbD7q+gGZsVr8yfYocfLfpD1xrxiBV/IpniFI437H4nYmipbTXEjtPp5FZVAYCenh60t7dzFV5CogY/IiFSXQzDrGgQrLwVnJR0vqrzkmAy6jExt4ie8bmI96MkTUKiVV2RolU/SJd6kFsT2yC4XKAKGrbA81SgKl5Mwp7i5HQ6uRJ8YPmDTGpqKle2LhQ+n0/Q/WkNsfwZc/oTJxkiJWjSrf79ipmgodjhh/wJhvzgJxb88fkYTM2zTYJDv/aF4w3bfHjSGTsJGjXETktLCy666CLub7a/zA033IBHH31U0GOpwY9IiFTX6fE5TMwtwmTUY3teksCjih4lnS+z0YCavGQc6J1Ec+8ktqRHtiqJkjQJiVZ1RYpW/SBd6kFOTQzD4PiwP0EjRINgYHmKk9cr/kLbYSdopPwgs7LZHXEuYvkz5vB3vGYrXYQmw+7/8jMqYoKGYocfNfgjZTJYDX7ISSz443Atwevzv+mmJIbeTC4cb9jlu2OpgkYNsbN7925EMds7LNTgRyREqqvptL//TE1eMsxG5fVgUNr52lmUggO9k9h/ehLX74zsfVBpmoRCq7oiRat+kC71IKemoRkXZl0eGPU6FEeYzF4N2yQ40SrM/vgIO0Ej5QeZ1FTllbsqCTH8YRhG9Aoadr9Otwfzix4kmITvVU2xw48a/JEyGawGP+QkFvxhkyY2szGsL4rheMNOcZqMoVWcYiF2wkGrfkSq663ucQDA+SVpG2wpD0o7X+cXb8J/v3IKb3WPg2GYiPrQKE2TUGhVV6Ro1Q/SpR7k1HQiUD2zJT0RJqMwHV3YChpLQoIg++ND/DbEUcCumEGsjRj+TM8vYSlQurXJKk6Cxmo2Ij7O/wVIrGlOFDv8qMEfNhm8+kfo5AygDj/kJBb8mWT7z1hD7z8DhOdNmpVN0MROBU0sxE44aNWPSHT5fAzePuVP0FxYsmmDreVBaeerfnMKTEY9RmfdODXmjGgfStMkFFrVFSla9YN0qQc5NbFLbJdl2QXbJ1tBMzQ8Itg+1z2W6EcgVAU77SglIU6wjONqdDod14BYzGlOBEEQoTLhDL9BcLjEYpNggliPzmEHpuaXkGgyYEd+stzDUQWWOAN2FqYAAN7qnpB5NARBEMqEraApyxRuOpI+UEEjxUQiRSdoSkuFX+JZS4jhz+hsoP+MTZz+MyzsNCd2xSihodjhh/wJhvzgJxb8Yata0sJM0ITjDbvvWZcHix7tNQRci1iInXDQqh+R6GKnNzUWpSLOoMyPo0o8X+cX+6uN3gz4Fy5K1CQEWtUVKVr1g3SpBzk1dQ45AAhbQWMIzChN2yR+bx1lviMGmJqaknsIikYMf8ReYpuFbRQ8FkgICQ3FDj/kTzDkBz+x4A/bFybcCppwvLFb4rg5zOyKUVonFmInHLTqRyS62AqQCxQ6vQlQ5vli/dp/egIeb/iJXiVqEgKt6ooUrfpButSDXJrm3B5uie1qAVcHZKc4OefnBdvnuscS/QhRMDY2JvcQFI0Y/ojdIJiFXWpbrClOFDv8kD/BkB/8xII/7LSj1DCW2AbC80av1yElITDNKUaW2o6F2AkHrfoRrq5Fjw8HevwrOCk5QaPE87U9Nwk2ixGzLg86Bh1hv16JmoRAq7oiRat+kC71IJemwwMz8DFATpIFmQKuSMxOcZqdjaz/V1jHEv0IURBJd/pYQgx/uAoau9gVNP7/MGIlaCh2+CF/giE/+IkFfyKd4hSuN2mJsdUoOBZiJxy06ke4ug72TWFhyYtNVhPKMm0ijSp6lHi+DHodztviX/XqrQimOSlRkxBoVVekaNUP0qUe5NLU3j8NAKgtSBF0v4aAHinWslZ0gqaxsVHuISgaMfxhe9Cki7SCEws7hUqsVZwodvghf4IhP/iJBX8ibRIcrjfs/sedsdEgPRZiJxy06ke4utj+KecVb+LuSioRpZ4vturojZPh36FWqqZo0aquSNGqH6RLPcil6WCff2pVjcDN59n3qpzcPEH3u+axRD9CFLS2tso9BEUjhj9sRUuGgCVha5Eh8ipOFDv8kD/BkB/8xII/g9MLAIDs5PCufeF6w+5/cGYhrNeplViInXDQqh/h6nr5+CgAYHep+M0Wo0Gp52t3md+3lt4pzCwshfVapWqKFq3qihSt+kG61IMcmhiGwUGugiZZ0H2z9xIGzp4VdL9rHkv0I0SBx+ORewiKRgx/xmcl6kFjE7dJMMUOP+RPMOQHP1r3h2EYnA0kaHKT48N6bbjesPtnE0JaR+uxEy5a9SMcXUMzCzg66IBOB1xUniHiqKJHqedrc1oiSjKs8PgYvN4VXhWNUjVFi1Z1RYpW/SBd6kEOTWenFzA264ZRr0NVrnANggFwizxE0pw9XBSdoElNTZV7CIpGDH/YihbRV3EKLOM9MbcoSqBT7PBD/gRDfvCjdX8m5hbh9vig0wHZSeElaML1hk3QnJ2KjQSN1mMnXLTqRzi62OqZuoKUsKcUSo2Sz9eeQHKL9TNUlKwpGrSqK1K06gfpUg9yaDrYNw0A2JZjhyXOIOi+2VWczJbwPidGdCzRjxAFmZmZcg9B0Qjtz/yiB063P9spdgVNWqIJBr0ODLO8eoqQUOzwQ/4EQ37wo3V/2GRJhs0MkzG8t8VwvclNCSRoYqSCRuuxEy5a9SMcXS93+hMKFyu8egZQ9vli/XvlxCi8vtDbVipZUzRoVVekaNUP0qUe5NDEJmhqBe4/AyxX0MQnJAi+79UoOkHT2dkp9xAUjdD+sCs4xccZYDUbBd33avR6HTZZTUHHFRKKHX7In2DID3607k+k05uA8L3JWVFBwzBSrAUgL1qPnXDRqh+h6lpY9HINgvdUKD9Bo+TzVb85BUnxcZieX+KaYoaCkjVFg1Z1RYpW/SBd6kEOTe39/muh0Cs4Acs9aMbGJgTf9znHEv0IhGoYcy5Pb5JiaTSuD41TnD40BEEQocD2g8mJIEETLmwSaG7RC8eC9uacEwQf75weh9vjQ25yvKKX11YDRoOeaxb8jzCnORHSs2/fPuzcuRM2mw0ZGRm45pprcOLECbmHRRCawbXkRcdZBwDhV3AClldx8klwc03RCZqSkhK5h6BohPaHrWQRe3oTC9uHRowKGoodfsifYMgPfrTuz0BgihM7/SgcwvXGEmfgqgcHpufDPp7a0HrshItW/QhV10srpjdJcSMoWpR+vthpTi8dGwn5NUrXFClK1/Xaa6/h1ltvxf79+/Hiiy/C4/Hgsssuw9zcnCjHU7ofkUK61IPUmpp6JrHo9SE7yYLNacJPQzIE3rPsycmC73s1ik7QOJ1OuYegaIT2ZzSwolKGXaoEjXhLbVPs8EP+BEN+8KN1f9gpTnkRVNBE4k1ODDUK1nrshItW/QhFl8frw/MdwwCAS7epo9+C0s/X7rIMxBl0ODnqxMmR2ZBeo3RNkaJ0Xc899xxuvPFGVFZWYseOHXjkkUfQ19cn2lLESvcjUkiXepBa0xuBFe3eszVdlBsAbJNgl1v43qnnHEv0I0TB8PCw3ENQNEL7M8otsW0RdL/rkc4laISf4kSxww/5Ewz5wY/W/YlmilMk3sTSUttaj51w0aofoeh65/QEJuYWkZpowvnFaRKMKnqUfr6S4uPwnq3+aU7PHB4K6TVK1xQpatM1MzMDQLyVbtTmR6iQLvUgtaY3Tvr7m727dJMo+2enODnnxK9+VnSChpCWocCXBakqaLKS/ImgwWnqQUMQhHxwTYIjmOIUCdxS2zGQoCEIlmcODQIALq/KgtFAHz+F4sod2QCAvx0ajInG41qAYRjcfvvtuPDCC1FVVbXudm63Gw6HI+jH7Ra+6pwg1M7wjAsnRmah0wEXFIuToGGnOElxmRV3qZ4oaWxslHsIikZof3rG/fNgt2xKFHS/61GU5j9O77jw828pdvghf4IhP/jRsj9zbg+m55cARLaKUyTe5MRQgkbLsRMJWvVjI12LHh+eC0xvuqo6R4ohCYIaztclFZkwG/U4PT6HY0MOVOYk8W6vBk2RoCZdn/vc53D48GG8+eabvNvt27cP9957b9Bjt912G66//noAQF1dHTo7O7GwsACbzYaioiIcPnwYAFBQUIDBwUH09/cDAGpqatDd3Q2n04nExESUlpbi4MGDAIC8vDwYDAacOXMGAFBdXY3e3l44HA5YLBZUVlZyU7FycnJgsVhw+vRpAEBVVRUGBgYwPT0Nk8mEmpoaHDhwAACQlZUFq9WK7u5uAEBFRQVGRkYwOTkJo9GI+vp6HDhwAAzDID09HSkpKejq6gIAlJWVYXJyEmNjY9Dr9di5cydaWlrAMAy6u7uRkZHBrRK0detWOBwOjIz4ezHt2rULbW1tWFpaQkpKCnJycnD06FEAQHFxMebn5zE05K84a2hoQEdHB1wuF5KSklBQUIAjR44AAAoLC+HxeDAwMMD5ffz4cczPz8NqtaK4uBiHDh3i/AaAvr4+AMCOHTtw6tQpOJ1OJCQkoLy8HG1tbZzfRqMRvb29AIDt27fDbrejqakJFosFVVVVaGlpAQBkZ2cjISEBp06dAgBUVlZicHAQU1NTiIuLQ11dHZqamgD4l7W22+04efIk5/fo6CgmJiZgMBjQ0NCA5uZm+Hw+pKenIzU1lWtUXVpaiqmpKYyNjUGn06GxsRGtra3weDxITU1FZmYm53dJSQmcTidXIdPY2Ij29nYsLi4iOTkZeXl56OjoAMMwGBsbg8vlwuCgP0FfX1+Po0ePwuVywW63o7CwkIvZzZs3w+v1cn7X1taiq6sLc3NzsFqtKCkpQXt7OwAgPz8fer2ei9lTPn8lYXGyEf2njsNaUcH5nZubC5PJhJ6eHs7v/v5+TE9Pw2w2o7q6Gs3NzVzMJiYmcn5v27YNw8PDmJycxMiwv6DAxzBoampCRkYGkpKSOL/Ly8sxPj6O8fFxLmYjRccoON1+8OBB1NbWyj0MxSKkPwzDoPqeFzDr9uCF296DUglWVxieceFd+/4Bo16HzvsuR5yAd9Qodvghf4IhP/jRsj8nR2Zx6Q9fh91ixOF7/ins10fizfNHh3HL71qxIz8Zf7n1grCPqSa0HDuRoFU/NtL18vERfPrRFqTbzNj/tT0w6JXfIBhQz/n6zO9a8dzRYfzr7mLccXk577Zq0RQuatH1+c9/Hk8//TRef/11FBUV8W7rdrvPqZgxm80wmzeudFeLH+FCutSDlJq+8IeD+OuhQXz+4hJ8+bIyUY7x369044HnT+Dionj8+paLRTkGi6JrTBcXxW/Co2aE9GfcuYhZtwc6HVCQKnzn67XItJsRH2eAx8egf1LY+XwUO/yQP8GQH/xo2Z+BKJfYjsSb3BhqEqzl2IkErfqxka5nDvnvVl+xPVs1yRlAPeeLneb0TAjTnNSiKVyUrothGHzuc5/Dk08+iZdffnnD5AzgT8bY7fagn1CSM4Dy/YgU0qUepNLk8zF4s9vff+Y9pemiHYd97/J4fKIdg0XRCZpkCZaxUjNC+sNOb8pNjoclziDYfvnQ6XQoCkyn6hF4mhPFDj/kTzDkBz9a9odNkuRF2H8mEm/YBM240w3Xkjei46oFLcdOJGjVDz5dDtfS8vSmQCJBLajlfF1cnoFEkwEDUwto6pnk3VYtmsJF6bpuvfVWPPbYY3j88cdhs9kwPDyM4eFhLCyIk6hXuh+RQrrUg1SaDvZPYXJuEVazETX54h2T7UETZzKJdgwWRSdo8vLy5B6CohHSn9Nj/qXQtqRbBdtnKBSlJwaOL2yChmKHH/InGPKDHy37w66kFEn/GSAyb5IT4pBg8ifCh2a03SRdy7ETCVr1g0/XX9oHsbDkRUmGFXUFKRKOKnrUcr4STEa8v8bf2+cPB/p4t1WLpnBRuq6HHnoIMzMz2L17N7Kzs7mfP/7xj6IcT+l+RArpUg9Safpru7+/zaXbMgVtl7EaduVus0X8BSUUnaDp6OiQewiKRkh/pG4QzFIcON5pgStoKHb4IX+CIT/40bI/Z6Oc4hSJNzqdLmamOWk5diJBq36sp4thGPyhyZ8w+GhjAXQ69UxvAtR1vj7a6G9S+veOYUzNrT+1QE2awkHpuhiGWfPnxhtvFOV4SvcjUkiXepBCk8frw7NH/FNo2SS1WLBTnKamp0U9DqDwBA0hHWyCpEjiBA1bQdMz7pT0uARBEMBygkSqJbZZlldyErb/FkEoiSNnZ3BsyAGTQY9ra3PlHo6m2Z6bhG3Zdix6fHjy4Fm5h0MQBCE6b5+awLhzESkJcbiwRJzltVnYBI1PguWVFJ2g2bJli9xDUDRC+sNV0KRLnKDZZA06vlBQ7PBD/gRDfvCjZX/6p/wJkkinOEXqDZsQ6hO4QbrS0HLsRIJW/VhPFzvdZu/2LKQkij9vX2jUdL50Oh0+ustfRfOHA33rNgtWk6Zw0KquSNGqH6RLPUih6a+H/NObrqjOFnV6EwDoAxWg8Qnif1dWdILG5dL23PxoEcofj9eHMxMyVdAEjjficMPp9gi2X4odfsifYMgPfrTqz7jTjRGHGzodsDXTFtE+IvWmPMt/vM6h2Yherxa0GjuRolU/1tI1M7+EvwR6A7DTb9SG2s7X1TU5iI8zoHvUiXdOT6y5jdo0hYpWdUWKVv0gXepBbE2uJS/XgP79O8Sv0GQTNB6P+Is7KDpBMzg4KPcQFI1Q/pydXsCSl4HZqEdOkrRl/knxcdhk9d9V6xWwioZihx/yJxjygx+t+nN00AHAnyi2mo0R7SNSbypzkgD4p4BoGa3GTqRo1Y+1dP32nV7ML3pRnmXDrqJUGUYVPWo7X3ZLHD5Y7/+i8rPXTq+5jdo0hYpWdUWKVv0gXepBbE3PHx2G0+1BTpIFDZvFb0DPFujMi7Ty2koUnaAhpGFl/xm9XvoGfkUiNQomCILgoyOQHKkKJEukpCLbBr0OGJt1Y9ShvTtnRGyzsOjFI2/3AgD+dXex6poDq5l/eXcx9Drg9a4x7hpHEAShJRiGwcNv9gAAPrwzX5Lvr2wFjU/0Iyk8QVNfXy/3EBSNUP70jMkzvYmFPW6PgEttU+zwQ/4EQ37wo1V/uARNrj3ifUTqTYLJiOJ0fw+ujkHtfonSauxEilb9WK3rf1r6MTm3iPzUeFyxPVumUUWPGs9XQVoCrtrhX83kZ6+dOud5NWoKBa3qihSt+kG61IOYmpp7p3B4YAZmox6ffNdm0Y6zErZJsNUW+WfGUFF0gubo0aNyD0HRCOXP6cAKSnIlaLYEvqScFnAlJ4odfsifYMgPfrTqD5sYqcqNvIImGm+2B457ZMAR8T6UjlZjJ1K06sdKXUteH37xun96zb+8pxhGkRs3iolaz9dn3lsMAPi/I0PnLMKgVk0boVVdkaJVP0iXehBT0y/f8L/HXFuXhzSrWbTjrIStoHHMir/ysKLfNbXYMElIhPLn5Ii8CRr2uOw4hIBihx/yJxjygx8t+jMzv4T+Sf884soopjhF401lIEGj5QoaLcZONGjVj5W6Htt/BmenF7DJasaH6vNkHFX0qPV8VWTbcXF5BnwM8B/Pnwh6Tq2aNkKruiJFq36QLvUglqae8Tm81DkCALj5wiJRjrEW7DQqj1f8SU6KTtDY7eKXEKkZIfxxLXnR3j8NAKgtSI56f5FQk+8/7vFhB2bmlwTZJ8UOP+RPMOQHP1r0h02KFKQmICk+LuL9RONNVY7/tUc13CdCi7ETDVr1g9U1Pb+IB186CQC4/dJSWOIMcg4ratR8vr7yT2XQ64BnjwzhQM8k97iaNfGhVV2RolU/SJd6EEvTgy91gWGAi8szUJJhFeUYa2EIVNDoDeK/ryk6QVNYWCj3EBSNEP4c7JuG2+NDus3M9UOQmky7BcXpifAxQFPP2stChgvFDj/kTzDkBz9a9EeI/jNAdN5sCyRoBmdcmHC6oxqHUtFi7ESDVv1gdT340knMLCyhPMuG63fmyzsoAVDz+arItuP6nf7lze/72zH4fAwAdWviQ6u6IkWrfpAu9SCGppbeSfylfRA6nf8mgJSwfYhNJvGnVCk6QXP48GG5h6BohPDn7VPjAIDzi9NkXWXh/OJNgfEIk6Ch2OGH/AmG/OBHi/50BJbYjqb/DBCdNzZLHLYEpniy49EaWoydaNCqH4cPH0bXyCx+t/8MAOCuK7dxDRXVjNrP15cvK4XVbMSRszP4U2s/APVrWg+t6ooUrfpButSD0Jp8Pgb3POPva3N9Q37Un9/ChZ3iNOsUf9VhRSdoCPFhEyIXBBIkcnF+cRqA5YQRQRCEmByVcYntlXB9aDQ8zYnQPh4fg9v+2A6vj8ElFZm4oETezxSEn01WM76wpwQAcP/fOjEwNS/ziAiCICLjjy396DjrgM1sxL/9U5nkxzfQMtt+Nm+WZtkstRKtP063B4cC/WfOCyRI5OJdW9Kg0wFdI06MzkbfVIpihx/yJxjygx+t+TPudON0YGWTypzopjhF6w3bh6b1zFRU+1EqWoudaNGqHy8MxuHooAPJCXH47geq5B6OYGjhfH36giLUFSRj1u3Bl//nEPILCuQekiho4VwJiVb9IF3qQUhN3aOzuO9vxwAAX7xkKzZJtHLTStiq0Lg4k+jHUnSCxuv1yj0ERROtP809k/D4GBSkJiA/NUGgUUVGSqIJ27L9X1TeEWCaE8UOP+RPMOQHP1rz52+HBgEA1XlJUS/PGK03F5dnAABe7xrD1NxiVPtSIlqLnWjRoh/7T0/gdy3DAIB9H9iODLtF5hEJhxbOl9Ggxw8+XIMEkwFNPZP47YEhuYckClo4V0KiVT9Il3oQStP8ogef/X0b5he9OL84DTddIN3KTSthO4F4A/28xETRCZqBgQG5h6BoovVnZf8ZJcCOQ4gEDcUOP+RPMOQHP1rz56l2f4LmA7W5Ue8rWm+2ZtpQmWOHx8fg2SPa++KktdiJFq35cWrMic881gofA1xbl4u927PlHpKgaOV8FW5KxF1XbgMA/PydITzXQdcaraNVP0iXehBCk9fH4M4/H0HXiBPpNjMe/EiNbP3N2OO6F8W/maboBA0hHgzD4I2TgQSNQuaKs+N44+Q4t9oAQRCEkJwec+JQ/zQMeh2urM6RezgAlhNFTx88K/NICCJ0Jpxu3PRIM6bnl1CSYsR3rtku95AIHj6yMx+feFcBGABffKIdbX3anFZJEIQ28PkY3Pnnw/jroUEY9Dr86CM1yLDJV6HJ9aCR4CuqohM0tbW1cg9B0UTjzzunJ3B8eBYmgx4XKKSCprEwFVazEWenF/DCsZGo9kWxww/5Ewz5wY+W/Hk6UD3z7q2bkG6Lfg6zEN5ctSMHeh3QcmYKfRPaauKppdgRAq34MTi9gI/8Yj/6JudRkJqA3/3zBYg3GeQeluBo5XwBgE6nwz1XVWJ36Sa4PT7c8OsDaDotzMqZSkBL50oItOoH6VIP0Whye7z46p8P40+tA9DrgAevr+FW/JULdrVjk1n8JJGiEzRdXV1yD0HRROoPwzB48KWTAICPNuZH3YNBKBLNRtx0QSEA4Ef/OBlVFQ3FDj/kTzDkBz9a8YdhGK5KRYjpTYAw3mTaLdyqN39p11YVjVZiRyi04MeJ4Vlc+9O3cXLUiUy7GY/ctBMTg71yD0sUtHC+VmI06HFrbTx2FqZg1uXBJ399AP+nkamVWjtX0aJVP0iXeohU08DUPD788/3430By5ofX1+CqHfJXPLNTnFxut+jHUnSCZm5O/HXG1Uyk/rxzegIHeiZhMujxr7tLBB5VdNx8YRGsZiM6hxxRVdFQ7PBD/gRDfvCjFX+eOTyEvsl5JJgMuHRbpiD7FMqba2r8CaPHms5oqlmwVmJHKNTsh8/H4NG3evD+n7yJYYcLJRlWPPnZC1CcblW1Lj60qMu3uIDf3bwLl23LxKLHh8/+vg3ffPoI5hc9cg8tKrR4rqJBq36QLvUQrqYlrw+PvtWD9/3oDRzqn0ZSfBwevmEnrq4R5oZatBgCWROvT/yFtiNK0Pz0pz9FUVERLBYL6uvr8cYbbwg9LgCA1WoVZb9aIRJ/vD4GP3zRn9H8aGM+spKUtdpCcoKJq6J58KUuLHkj+09AscOPWvyha40y0II//ZPz+MaTRwAA/+/dW5BgMgqyX6G8uaI6G1vSEzHicOMr/3sYDKONPlxqiR261vDT1jeFj/5yP+555hjcHh/evXUT/vcz5yE3OR6AenVthBZ1Wa1WWOIMeOgT9fh/F/pXQ3lsfx/2/ugNPHNoULU9ANVyruhaEx2kSz2Eqsm15MX/tPRj74/ewD3PHIPD5cGOvCT87fMX4qLASpdKQM8u4yRBfYuOCfNT4B//+Ed88pOfxE9/+lNccMEF+PnPf45f/epXOHbsGAoKCgQdnNvthtmsjOk3SiRcfxY9Ptz+P+342+EhmIx6vP6VixSXoAGA6flFvPvfX8Gs24M95Rn474/XwRIX3tx2ih1+1OAPXWuUg9r9cXu8uP7n+9HeP42GzSl44l/eBaNBmDdYIb05OjiDD/z321j0+nD3VdtkW0pSSNQQO3StWZtFjw8vHx/FHw704bWuMQCAJU6Pr7+vAp/YtRn6FStpqElXOGhR12pNb54cx1f+9xCGZlwAgPIsGz7xrs24akcOkuLj5Bpm2KjhXNG1JnpIl3rg0+T2eNHSO4W/dwzh70eGMRGoHE5JiMO//VMZPrKzQLbVmtbjUP80rv7vt5CdZMY7X7tE1GOFnaDZtWsX6urq8NBDD3GPVVRU4JprrsG+ffsEHVxTUxN27dol6D61RDj+HBt04HvPHcfrXWOIM+jw4PW1uKJaucthvnpiFLf8rhVujw+Nhan4xhUVqM5L4ho0bQTFDj9q8IeuNcpBzf68eXIc3/pLB06Pz8FuMeL/vvhu5KUkCLZ/ob159K0e3PPMMQDA1TU5+Mb7KpBhV14iPVTUEDt0rfGz6PHh5OgsDg/M4M3ucbzVPY7p+SUA/rn319Xl4fN7Stb8/6NkXdGgRV1raXK6Pfj1mz345eunMev2T3UyGfXYVZSKd2/dhNqCFJRn2WCzKDdho4ZzRdea6CFd6qGpqQmNjY2Yml9C/+Q8To05cWJ4Fu3902jvn4bbszxLIifJgk+dX4iPNhYoNjHccXYGV/7Xm0ix6HHwnr2iHiusGu/FxUW0trbizjvvDHr8sssuw9tvvy3owDrOzuDw6CKWAktBE+fSObqIxZNjYBiAAeBjGHi9DBa9PjjdHkzPL6JnfB5Hzk6j46wDgP/u188+UY/dZcopGVuL3WUZ+O2nG3Hzb1pwoHcSV//3WyjPsqEmPxmFmxKRmmBCotkIk1EPo0EHvU4HHQCdDtBBh06KHV7OTC5ByW8DUl5rjg066FqzAUr7/8TAf1+Bu/b5GCx5ff5rn8uDcacbPePzONA7gf7JBQDAJqsZP/pIjaDJGTG44fxCDEwt4OG3evCX9kE8e3gI2/OSUFeQgiy7BWlWE+LjDDAZ9dDrdTDodGDz1v6roLLomaBrDcuJ4VnRrjUMGO7/A8Mw3L9en3++/JKXwaLH/39kftELp8sDh2sJk3OLGJ11YWBqAWenFuBZNb0lw2bGtXV5+MjOfBRuShR83IQysJqN+MKerfjUeZvxv60D+N/WARwfnsUbJ8fxxop4zbJbkJcSj6wkC9ISTUhKMMFqNvz/9u41uIny3wP4d5NsNknblBZpKVbKTVsqotACLfLXM4OCc9D/6Btw5sig4ozK6KC+cFDPHC8vRGa8OygDIrwCFEuVOYrKHKEoKNdW6IWCFaQKtRQotM09ec6LtJHYJiWXZjfb72em43R9Gp7fb3e/SZ8mu8hQTFBMwVySDRKMvV8GQ99rs2A2Xf06LdlaOpg1fX5tH7qsUZvWXo8ky1DV1fd6KfS96Nt+9bbeUeKq5xIR/L0yIHqfS4SAPyDg8wf/6/EH4PUH4PYF4PL64fT64XD70eP24bLTi0sOD/68cAVX/vdbOL3+Aed2XaYZc0vycc8to/GvSdcl7Z3NQ8UQus320H8MNKYFmo6ODvj9fuTnh19cMT8/H21tbQP+jNvthvsfVztWFGXQt3Gt+uY4fjh5Bdi7P5YpDj97D1zTMNko4e7SfCz7j0mYcn32EE8qOWZNGIlty2Zj9a5fsaO+DcfbunC8revaH4DHTkST8234r6Fd/E1IKrPmvf87gW8bmDWDStP+mAwSHqoownPzboJ9CP76e8MNNyT18SRJwn/fW4p/3zYG//NlA+paO1F7JviVjsbnWrD4P9WeRWSpzJo1NS2ortV21tgtJkwusGPWhJHBd07cMOKaXjQn+zzQCj3WFa2mETYzHvvXBCydMx4n27vxw8kO/NTSgfo/r6Dtiiv0pUWjs8xYskDtWUSWyqz5ZO9pbNqv7axJCOtKO3lZCsZdl4HJo7MwucCO8nG5mDgq45o/GaEFfR+5kgyxXXYjHnFdJfGfzRRCRGzwypUr8eqrr4Zte/bZZ7Fo0SIAwPTp09HU1ASn04msrCyMHz8eR48eheLtxrgcM4wGA7ze4FtsLVYrPG43AoEADAYDFEWB0xn866gsy4AkwesJfobNYrHA4/Ui4PdDkgywWP4ea5JlGCQJnqvGer1e+P1+SJIEi9UKp8MRHGsywWAwwuMJBqSiKPD5fPD7/YAkwWa1wnHVWKPRGApTRVHg9/vh8wXfLmqz2eBwOgEhYDQaYTKZQmPNioLAP8Y6nU6IvrGyDLcr+KRoNpuDfzH2eiBJEjJsNrhcLgABKCYTbFYLhMcBm2zATdfn4ga7jEK5G9mKD8V5Nhw7dgwOhwOZmZmYOHEifvnlFwAIff71zJkzAIBbb70VLS0t6O7uhs1mQ0lJCY4cOQIAKCwshMlkwunTpwEAt9xyC86cOYPLly/DYrFgypQpOHToEACgoKAANpsNLS0tAICbb74ZZ8+exaVLlyDLMqZPn479+4OhlJ+fD7vdjpMng7cBnzx5Mp4uz8K/r3fhWIcfIjMPR06cQY8nAK9kAgxGdDucECLYQ58v2EMhBDIyMiL326wgEPi731abDa6r+i3Lcm9Pe/stBHy9x6HVaoXL5YYQARiMRpivGiubzYAQoWPWarXCfdUxa1YUuKIdsx4PAoEAJIMBln8c31JMx6whNFaxWOC7aqzVakW25MT+/fuRl5eH7OzsUL9LSkrQ0dGBjo4OGAwGzJgxA2pKRdYYXcyawbLG4/XA0HvsRDr2zWYzRLRj36zA5bqqh8Df/bZY4fFE7vfVx77VaoHH40Ug4IdBMsBmtcLpdMAgAVlWBRkWGbLfiVyrAfOmTUK+yQF392X82nhs0Kxpb2/HhQsXYDQaUV5ejoMHDyIQCGDUqFHIzc1Fc3MzAOCmm27CpUuXcP78ebjdbowZMwaHDx+Gz+dDbm4u8vPz0dTUBACYNGkSuru7Qy/AZ86cibq6Ong8HowYMQKFhYWor68HAEyYMAEulwtnz54FAHz++Cx8v/8X1P3ZjXMuI7xGC878dREev4DBZIY/IOAKHVvazJoMwazpyxrR0zNkWSPLcu++cEMCYLNa4fMFx5qMBmRnZcLZ0wXZIGFEphXZNgVeZxfsZgmTJxTC6nfAGuhBXqYZ5eXlOHDgAAJ/XcKZwCjk5OSEbpdaXFyMixcv4vz586H9dujQIfT09MDhcCAvLy907N944424cuUK/voreEfGWbNm4ciRI/B6vcjJycGYMWPQ0NAAAJg4cSIcDgfOnQve8rm8vBz19fVwuVzIzs7G2LFjcexY8CLf48aNg8/nwx9//BHq9/Hjx4fkdc3Zs2fR2tqa9Nc18WSNJEmYOXNmwlnjcrlCr3H6sqasrAwNDQ1wuVyw2+0YN24cLp1uxBQZWDC/CH6/H00tZ/BXjx+2vLFo/O0PdHS74BYmmCwZOHv+InwBAYNJgV8APU4XBAClN5f8vcesYrGEnseSnTWyz8Gs6c0a3xWHbl/XBAIByCZTwq9rfL7BX9On6nWNxWIJ3rpZiP79NskwGKSox/7VPTQYjfD09sViscDn88Hn80GSpGAPHQ4AAiaTCSajCW53sFarxQKf3w+/zwdJArIyM+Ho6QEgYJZlWBUznE4HjBKQlZkBCD/8Hjdkg4TCgjx0dV6E2RBATqYNN4weicvt52CVfCifciOyTAFcaA/mUllZCRoaGnDh1Dl4LwSz5ujRowCAoqJg1vRl+7Rp03DixAn09PQgMzMTkyZNQl1dHYDgQrPBYMDvv/8OAJg6dSpOnTqFrq4uWK1WTJ48OZTt119/PcxmM06dOgUgmO2tra3o7OyEoiiYOnUqDh48CAAYPXo0MjIyQtleWlqKtrY2XLx4ERfcEorzswDv0GdNTNeg8Xg8sNls2Lp1Kx544IHQ9uXLl6Ourg41NTX9fibe1V9An5/HSyb2JzL2Jjqt94dZoy3sT2TsTXRa7w+zJjlYV/rQY02A9uti1iQH60ofeqwJSE1dMX3Yy2w2o6ysDDt37gzbvnPnTsyePXvAn1EUBXa7PexLb1epJqLkYtYQUSowa4goFZg1RHSt4r7N9po1a1BZWYm1a9di3bp1aGhoQFFRUVIn53Q6YbVak/qYesL+RMbeRJcO/WHWaAf7Exl7E1069IdZkzjWlT70WBOQHnUxaxLHutKHHmsCUlNXzJdLXrRoEd5991289tpruO2227Bnzx58/fXXSQ8WAKHPitHA2J/I2Jvo0qE/zBrtYH8iY2+iS4f+MGsSx7rShx5rAtKjLmZN4lhX+tBjTUBq6orrIsHLli3DsmXLkj2Xfrq6YrhjzzDE/kTG3kSXLv1h1mgD+xMZexNduvSHWZMY1pU+9FgTkD51MWsSw7rShx5rAlJTl6ZvOK7Ht0UlE/sTGXsTHfsTjv2Ijv2JjL2Jjv0Jp9d+sK70oceaAP3WFS+99oN1pQ891gSkpq6Yr0GTSl6vN3TbMuqP/YmMvYmO/QnHfkTH/kTG3kTH/oTTaz9YV/rQY02AfuuKl177wbrShx5rAlJTl6bfQdN3/3IaGPsTGXsTHfsTjv2Ijv2JjL2Jjv0Jp9d+sK70oceaAP3WFS+99oN1pQ891gSkpi5NL9AQEREREREREQ0Hml2gcbvd2LFjB9xut9pT0ST2JzL2Jjr2Jxz7ER37Exl7Ex37E06v/WBd6UOPNQH6rSteeu0H60ofeqwJSF1dmr0GzZUrV5CdnY3Lly/DbrerPR3NYX8iY2+iY3/CsR/RsT+RsTfRsT/h9NoP1pU+9FgToN+64qXXfrCu9KHHmoDU1aXZd9AQEREREREREQ0XXKAhIiIiIiIiIlIZF2iIiIiIiIiIiFSm2QUaRVHw8ssvQ1EUtaeiSexPZOxNdOxPOPYjOvYnMvYmOvYnnF77wbrShx5rAvRbV7z02g/WlT70WBOQuro0e5FgIiIiIiIiIqLhQrPvoCEiIiIiIiIiGi64QENEREREREREpDIu0BARERERERERqYwLNEREREREREREKtPsAs2HH36I8ePHw2KxoKysDD/88IPaU9KEPXv24L777sOYMWMgSRK++OILtaekGStXrsSMGTOQlZWFvLw83H///WhublZ7Wprw0UcfYerUqbDb7bDb7aisrMSOHTvUnpYmMGsGxqyJjFkT2XDLmljzo6amBmVlZbBYLJgwYQLWrFnTb0xVVRVKS0uhKApKS0tRXV09VNMfUCw1bdu2DXfffTdGjRoV2t/ffvtt2JiNGzdCkqR+Xy6Xa6hLCRNLXbt37x5wzsePHw8bp/a+AmKr6+GHHx6wrptvvjk0Ru39Fc9zTzqcV4li1jBr1D5+mTUpPK+EBm3ZskXIsizWrVsnGhsbxfLly0VGRob4/fff1Z6a6r7++mvx0ksviaqqKgFAVFdXqz0lzZg/f77YsGGDqK+vF3V1dWLBggVi7Nixoru7W+2pqW779u3iq6++Es3NzaK5uVm8+OKLQpZlUV9fr/bUVMWsiYxZExmzJrLhlDWx5sdvv/0mbDabWL58uWhsbBTr1q0TsiyLzz//PDRm3759wmg0itdff100NTWJ119/XZhMJvHzzz9rsqbly5eLVatWiQMHDogTJ06IF154QciyLI4cORIas2HDBmG328W5c+fCvlIp1rp27dolAIjm5uawOft8vtAYtfeVELHX1dnZGVZPa2uryM3NFS+//HJojNr7K9bnnnQ4rxLFrGHWqH38MmtSe15pcoFm5syZ4oknngjbVlJSIlasWKHSjLSJvzRF197eLgCImpoataeiSTk5OeLjjz9WexqqYtZcG2ZNdMya6PSaNbHmx/PPPy9KSkrCtj3++OOioqIi9P3ChQvFPffcEzZm/vz54sEHH0zSrKNLRiaWlpaKV199NfT9hg0bRHZ2drKmGJdY6+r7penSpUsRH1PtfSVE4vururpaSJIkTp8+Hdqmhf3V51qee9LhvEoUs2ZgzBpmTbJoLWs09xEnj8eDw4cPY968eWHb582bh3379qk0K0pHly9fBgDk5uaqPBNt8fv92LJlC3p6elBZWan2dFTDrKFkYdYMTM9ZE09+/PTTT/3Gz58/H4cOHYLX6406JhWZlIxMDAQC6Orq6ncudHd3o6ioCIWFhbj33ntRW1ubtHkPJpG6pk2bhoKCAsydOxe7du0K+39q7isgOftr/fr1uOuuu1BUVBS2Xc39FSutn1eJYtYMjFnDrEm1VJ5Xmlug6ejogN/vR35+ftj2/Px8tLW1qTQrSjdCCDz33HOYM2cOpkyZovZ0NOHYsWPIzMyEoih44oknUF1djdLSUrWnpRpmDSUDs6a/4ZA18eRHW1vbgON9Ph86OjqijklFJiUjE9966y309PRg4cKFoW0lJSXYuHEjtm/fjs2bN8NiseD222/HyZMnkzr/SOKpq6CgAGvXrkVVVRW2bduG4uJizJ07F3v27AmNUXNfAYnvr3PnzmHHjh147LHHwrarvb9ipfXzKlHMmoExa5g1qZbK88qU2FSHjiRJYd8LIfptI4rkqaeewtGjR/Hjjz+qPRXNKC4uRl1dHTo7O1FVVYUlS5agpqZGd784xYpZQ4lg1vQ3nLIm1vwYaPw/t6udSfH++5s3b8Yrr7yCL7/8Enl5eaHtFRUVqKioCH1/++23Y/r06fjggw/w/vvvJ2/ig4ilruLiYhQXF4e+r6ysRGtrK958803ccccdcT3mUIl3Dhs3bsSIESNw//33h23Xyv6KRTqcV4li1vyNWcOsUUuqzivNvYPmuuuug9Fo7LfS1N7e3m9FimggTz/9NLZv345du3ahsLBQ7elohtlsxqRJk1BeXo6VK1fi1ltvxXvvvaf2tFTDrKFEMWsGNhyyJp78GD169IDjTSYTRo4cGXVMKjIpkUz89NNPsXTpUnz22We46667oo41GAyYMWNGyv5Kmqysr6ioCJuzmvsKSKwuIQQ++eQTLF68GGazOerYVO+vWGn9vEoUsyYcsya+x0wEsyYoleeV5hZozGYzysrKsHPnzrDtO3fuxOzZs1WaFaUDIQSeeuopbNu2Dd9//z3Gjx+v9pQ0TQgBt9ut9jRUw6yheDFrYqPHrIknPyorK/uN/+6771BeXg5ZlqOOSUUmxZuJmzdvxsMPP4xNmzZhwYIFg/47QgjU1dWhoKAg4Tlfi2RlfW1tbdic1dxXQGJ11dTU4Ndff8XSpUsH/XdSvb9ipfXzKlHMmr8xa/7GrEm9lJ5XMV1SOEX6buW1fv160djYKJ555hmRkZERduXn4aqrq0vU1taK2tpaAUC8/fbbora2lrcFFkI8+eSTIjs7W+zevTvsdm0Oh0PtqanuhRdeEHv27BGnTp0SR48eFS+++KIwGAziu+++U3tqqmLWRMasiYxZE9lwyprB8mPFihVi8eLFofF9t+h89tlnRWNjo1i/fn2/W3Tu3btXGI1G8cYbb4impibxxhtvqHLr22utadOmTcJkMonVq1eHnQudnZ2hMa+88or45ptvREtLi6itrRWPPPKIMJlMYv/+/SmpKZ663nnnHVFdXS1OnDgh6uvrxYoVKwQAUVVVFRqj9r6Kp64+Dz30kJg1a9aAj6n2/hrsuScdz6tEMWuYNWofv8ya1J5XmlygEUKI1atXi6KiImE2m8X06dN5+9Jefbdj++fXkiVL1J6a6gbqCwCxYcMGtaemukcffTR0Po0aNUrMnTtXl78wxYNZMzBmTWTMmsiGW9ZEy48lS5aIO++8M2z87t27xbRp04TZbBbjxo0TH330Ub/H3Lp1qyguLhayLIuSkpKwF+qpEEtNd95556A58cwzz4ixY8eGjol58+aJffv2pbCioFjqWrVqlZg4caKwWCwiJydHzJkzR3z11Vf9HlPtfSVE7MdgZ2ensFqtYu3atQM+ntr7a7DnnnQ9rxLFrGHWqH38MmtSd15JQvRe3YaIiIiIiIiIiFShuWvQEBERERERERENN1ygISIiIiIiIiJSGRdoiIiIiIiIiIhUxgUaIiIiIiIiIiKVcYGGiIiIiIiIiEhlXKAhIiIiIiIiIlIZF2iIiIiIiIiIiFTGBRoiIiIiIiIiIpVxgYaIiIiIiIiISGVcoCEiIiIiIiIiUhkXaIiIiIiIiIiIVMYFGiIiIiIiIiIilf0/7Zc7lTz9zP4AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "
"
       ]
@@ -2168,7 +3329,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "fig, ax = plt.subplots(1, 4, figsize=(14, 3), dpi=100)\n",
-    "for axis, (param, d) in zip(fig.axes, model.param_priors.items()):\n",
+    "for axis, (param, d) in zip(fig.axes, model.priors[0].items()):\n",
     "    lower, upper = d.a, d.b\n",
     "    lower = max(lower, 0)\n",
     "    upper = min(3, upper)\n",
@@ -2190,23 +3351,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "id": "67927b6d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'epsilon_A': }"
+       "{'epsilon_A': }"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model.shock_priors"
+    "model.priors[1]"
    ]
   },
   {
@@ -2219,28 +3380,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "id": "e1b2cfa3",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'scale': }"
+       "{'scale': }"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model.shock_priors[\"epsilon_A\"].rv_params"
+    "model.priors[1][\"epsilon_A\"].rv_params"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 42,
    "id": "5a773f20",
    "metadata": {},
    "outputs": [],
@@ -2252,7 +3413,7 @@
     "\n",
     "pdf_grid = [\n",
     "    [\n",
-    "        model.shock_priors[\"epsilon_A\"].pdf({\"scale\": scale, \"obs\": eps})\n",
+    "        model.priors[1][\"epsilon_A\"].pdf({\"scale\": scale, \"obs\": eps})\n",
     "        for scale in scale_grid\n",
     "    ]\n",
     "    for eps in eps_grid\n",
@@ -2269,13 +3430,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 43,
    "id": "c70861be",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -2301,333 +3462,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 44,
    "id": "37334ad4",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Steady state found! Sum of squared residuals is 6.695381126805323e-23\n",
-      "A_ss               1.000\n",
-      "C_ss               2.358\n",
-      "I_ss               0.715\n",
-      "K_ss              35.732\n",
-      "L_ss               0.820\n",
-      "Y_ss               3.073\n",
-      "lambda_ss          0.276\n",
-      "r_ss               0.030\n",
-      "w_ss               2.436\n"
-     ]
-    }
-   ],
-   "source": [
-    "model.steady_state()\n",
-    "model.print_steady_state()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "15036ec4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Solution found, sum of squared residuals:  3.980959555625145e-31\n",
-      "Norm of deterministic part: 0.000000000\n",
-      "Norm of stochastic part:    0.000000000\n"
+      "You provided a function to compute the full hessian, but method trust-ncg allows the use of a hessian-vector product instead. Consider passing hessp instead -- this may be significantly more efficient.\n"
      ]
-    }
-   ],
-   "source": [
-    "model.solve_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40081234",
-   "metadata": {},
-   "source": [
-    "## Model Statistics\n",
-    "\n",
-    "Model statistics are computed at the initial values of the parameters -- there is no integration of the prior information into the stationary covariance matrix or autocorrelation function. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "76c1f974",
-   "metadata": {},
-   "source": [
-    "## Simulation\n",
-    "\n",
-    "Simulation becomes more conventient with defined priors, you don't need to pass a covariance matrix anymore. You still can -- doing so will draw innovations from a multivariate normal with the supplied covaraince matrix rather than from the prior distributions defined in the GCN file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "21b82dc1",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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",
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ed103e191944ea1b3853d02fb63e325",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "
"
+       "Output()"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp.plot_simulation(\n",
-    "    model.simulate(), figsize=(14, 6), vars_to_plot=[\"A\", \"C\", \"I\", \"L\", \"Y\", \"r\", \"w\"]\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "471b4142",
-   "metadata": {},
-   "source": [
-    "## Useful things to do with priors\n",
-    "\n",
-    "Researchers usually assign priors because they want to run a bayesian estimation of the model. This functionality is coming of course, but in the mean time there are other useful things one can do."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2ec3d44b",
-   "metadata": {},
-   "source": [
-    "### Simulation from Prior\n",
-    "\n",
-    "An excellent first step is to simulate model trajectories from draws of the prior, to see if they are reasonable. If your priors generate crazy outputs, they should probably be adjusted prior to estimation. The function `simulate_trajectories_from_prior` helps with this. It has 3 important parameters: `n_samples` is the number of draws from the prior, `n_simulations` is the number of trajectories to draw from each parameter combination sampled from the prior, and `simulation_length` controls the... length of the simulation.\n",
-    "\n",
-    "This will return up to `n_samples x n_simulations x simulation_length` values, which can be a lot, so be aware it might take some time, especially if the prior produces samples in bad regions of the parameter space (see below)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "4039c79c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sampling 1000 / 1000 [==================================================] elapsed: 00:11, remaining: 00:00, 89.19iter/sec\n"
-     ]
-    }
-   ],
-   "source": [
-    "simulations = ge.sampling.simulate_trajectories_from_prior(\n",
-    "    model, n_samples=1000, n_simulations=100, simulation_length=40\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "efe0e130",
-   "metadata": {},
-   "source": [
-    "The spaghetti plots can take a long time to draw for these, passing a CI will speed things up considerably."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "82fc5868",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
-      "image/png": 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-      "text/plain": [
-       "
"
-      ]
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gp.plot_simulation(\n",
-    "    simulations, vars_to_plot=[\"A\", \"Y\", \"C\", \"I\", \"K\"], figsize=(14, 6), ci=0.95\n",
-    ");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5712edd5",
-   "metadata": {},
-   "source": [
-    "### Steady State Bounds\n",
-    "\n",
-    "It is also possible that your priors will generate samples that are in regions of parameter space which have no associated steady state. You can check how the steady state handles different values from the priors using the `plot_prior_steady_state_solvability` function in the plotting tools.\n",
-    "\n",
-    "Here it seems that the prior over `alpha` is much too wide -- after 0.5 the model isn't able to solve a steady state anymore. This would cause a lot of headaches for an MCMC sampler because of an apparent discontinunity in the parameter space. It could potentially be solved by providing steady state equations. Whether this discontinunity is a mathmatical feature of the model or whether it is an artefact from the numerical solver would need to be investigated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "c67068db",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sampling 1000 / 1000 [==================================================] elapsed: 00:01, remaining: 00:00, 577.94iter/sec\n"
-     ]
     },
     {
      "data": {
-      "image/png": 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",
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
       "text/plain": [
-       "
"
+       "\n"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "solve_data = ge.sampling.prior_solvability_check(model, n_samples=1000)\n",
-    "gp.plot_prior_solvability(solve_data);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87c23847",
-   "metadata": {},
-   "source": [
-    "You can change a prior by changing the GCN, or by directly assigning a new scipy distribution in the `model.param_priors` dictionary. Assigning a new prior is nice for testing, but it won't save to the GCN and will revert back the next time you load the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "b1b32a75",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "b13b048d",
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "3.00000000000000 12.0000000000000\n"
+      "Steady state IS found, although optimizer returned success = False.\n",
+      "This can be ignored, but to silence this message, try reducing the solver-specific tolerance, or use a different solution algorithm.\n",
+      "--------------------------------------------------------------------------------\n",
+      "Optimizer message             A bad approximation caused failure to predict improvement.\n",
+      "Sum of squared residuals      7.817537743289768e-29\n",
+      "Maximum absoluate error       8.552207923778995e-15\n",
+      "Gradient L2-norm at solution  5.825605443052315e-16\n",
+      "Max abs gradient at solution  5.620504062164855e-16\n",
+      "A_ss               1.000\n",
+      "C_ss               2.358\n",
+      "I_ss               0.715\n",
+      "K_ss              35.732\n",
+      "L_ss               0.820\n",
+      "Y_ss               3.073\n",
+      "lambda_ss          0.276\n",
+      "r_ss               0.030\n",
+      "w_ss               2.436\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(0.2, 0.01)"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# The mean of a beta distribution is a / (a + b), and the variance is ab / ((a + b)^2 + (a + b + 1))\n",
-    "# Use sympy to solve for parameters a, b given desired moments.\n",
-    "from sympy.abc import a, b\n",
-    "\n",
-    "eq1 = 0.2 - a / (a + b)\n",
-    "eq2 = 0.1 - sp.sqrt(a * b / (a + b) ** 2 / (a + b + 1))\n",
-    "a, b = sp.solve([eq1, eq2], a, b)[0]\n",
-    "print(a, b)\n",
-    "d = stats.beta(a=float(a), b=float(b))\n",
-    "d.stats()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "3235ceab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.param_priors[\"alpha\"] = d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d95708df",
-   "metadata": {},
-   "source": [
-    "After the change the model is much more sample efficient. It might be nice to hard-code a boundary at 0.5, but this is not currently supported with a beta distribution, because I haven't added improper priors. Currently, options would be to use a truncated normal or further shrink the variance.\n",
-    "\n",
-    "Note that sampling the prior and repeatedly solving the steady state sped up considerably by shifting the piror to a better region of the parameter space."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "ad7fda87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2"
+    "ss_res = model.steady_state()\n",
+    "ge.print_steady_state(ss_res)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "id": "17bf82bd",
+   "execution_count": 45,
+   "id": "15036ec4",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 1000 / 1000 [==================================================] elapsed: 00:00, remaining: 00:00, 1504.77iter/sec\n"
+      "Solution found, sum of squared residuals: 0.000000000\n",
+      "Norm of deterministic part: 0.000000000\n",
+      "Norm of stochastic part:    0.000000000\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "solve_data = ge.sampling.prior_solvability_check(model, n_samples=1000)\n",
-    "gp.plot_prior_solvability(solve_data);"
+    "T, R = model.solve_model(steady_state=ss_res)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be9701ce",
+   "cell_type": "markdown",
+   "id": "40081234",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Model Statistics\n",
+    "\n",
+    "Model statistics are computed at the initial values of the parameters -- there is no integration of the prior information into the stationary covariance matrix or autocorrelation function. "
+   ]
   }
  ],
  "metadata": {
@@ -2646,7 +3586,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/Fitting Basic RBC to US Data.ipynb b/examples/Fitting Basic RBC to US Data.ipynb
index 08afc57..6b8af23 100644
--- a/examples/Fitting Basic RBC to US Data.ipynb	
+++ b/examples/Fitting Basic RBC to US Data.ipynb	
@@ -1748,7 +1748,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.6"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/Multiple Households.ipynb b/examples/Multiple Households.ipynb
new file mode 100644
index 0000000..ccebf64
--- /dev/null
+++ b/examples/Multiple Households.ipynb	
@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a3c80085",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sympy as sp\n",
+    "import gEconpy as ge\n",
+    "import gEconpy.plotting as gp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "844c4ca8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Model Building Complete.\n",
+      "Found:\n",
+      "\t16 equations\n",
+      "\t16 variables\n",
+      "\tThe following variables were eliminated at user request:\n",
+      "\t\tTC_t,U_NR_t,U_R_t\n",
+      "\tThe following \"variables\" were defined as constants and have been substituted away:\n",
+      "\t\tmc_t\n",
+      "\t2 stochastic shocks\n",
+      "\t\t 0 / 2 has a defined prior. \n",
+      "\t10 parameters\n",
+      "\t\t 0 / 10 has a defined prior. \n",
+      "\t0 parameters to calibrate.\n",
+      "Model appears well defined and ready to proceed to solving.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "mod = ge.model_from_gcn(\"../GCN Files/RBC_two_household_additive.gcn\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5fd4691e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[rho_beta_R*log(shock_beta_R_t-1) + epsilon_beta_R_t - log(shock_beta_R_t),\n",
+       " I_t - K_t + K_t-1*(1 - delta),\n",
+       " -lambda_R_t + shock_beta_R_t/C_R_t**sigma_R,\n",
+       " -Theta_R*shock_beta_R_t + lambda_R_t*w_t,\n",
+       " -lambda_R_t + q_t,\n",
+       " beta*(lambda_R_t+1*r_t+1 - q_t+1*(delta - 1)) - q_t,\n",
+       " -C_NR_t + L_NR_t*w_t,\n",
+       " -lambda_NR_t + C_NR_t**(-sigma_N),\n",
+       " -Theta_N + lambda_NR_t*w_t,\n",
+       " rho_TFP*log(TFP_t-1) + epsilon_TFP_t - log(TFP_t),\n",
+       " K_t-1**alpha*L_t**(1 - alpha)*TFP_t - Y_t,\n",
+       " alpha*K_t-1**(alpha - 1)*L_t**(1 - alpha)*TFP_t - r_t,\n",
+       " TFP_t*(K_t-1/L_t)**alpha*(1 - alpha) - w_t,\n",
+       " C_t + I_t - Y_t,\n",
+       " omega*L_R_t + L_NR_t*(1 - omega) - L_t,\n",
+       " omega*C_R_t + C_NR_t*(1 - omega) - C_t]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod.equations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cf73612b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ss_res = mod.steady_state()\n",
+    "A, B, C, D = mod.linearize_model(steady_state=ss_res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e6aa8e6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C_ss': 1.8103313609185514,\n",
+       " 'C_NR_ss': 1.8103313609185514,\n",
+       " 'C_R_ss': 1.8103313609185514,\n",
+       " 'I_ss': 0.5485612737719945,\n",
+       " 'K_ss': 27.42806368859972,\n",
+       " 'L_ss': 0.6294835982484689,\n",
+       " 'L_NR_ss': 0.7432261172918171,\n",
+       " 'L_R_ss': 0.5157410792051207,\n",
+       " 'TFP_ss': 1.0,\n",
+       " 'Y_ss': 2.358892634690546,\n",
+       " 'lambda_NR_ss': 0.4105470044526591,\n",
+       " 'lambda_R_ss': 0.4105470044526591,\n",
+       " 'q_ss': 0.4105470044526591,\n",
+       " 'r_ss': 0.030101010101010184,\n",
+       " 'shock_beta_R_ss': 1.0,\n",
+       " 'w_ss': 2.4357746839078733}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ss_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8e61081b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Model solution has 3 eigenvalues greater than one in modulus and 3 forward-looking variables. \n",
+      "Blanchard-Kahn condition is satisfied.\n"
+     ]
+    }
+   ],
+   "source": [
+    "ge.bk_condition(mod, steady_state=ss_res);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d29842a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gp.plot_eigenvalues(mod, A=A, B=B, C=C, D=D);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "924ba596",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gp.plot_irf(\n",
+    "    {\n",
+    "        f\"Percent Ricardian = {omega:0.0%}\": ge.impulse_response_function(\n",
+    "            mod,\n",
+    "            shock_size={\"epsilon_beta_R\": 1.0},\n",
+    "            verbose=False,\n",
+    "            omega=omega,\n",
+    "            sigma_N=20.0,\n",
+    "            Theta_N=10.0,\n",
+    "        )\n",
+    "        for omega in [0.2, 0.5, 0.8, 0.99]\n",
+    "    },\n",
+    "    [\"C_R\", \"C_NR\", \"L_R\", \"L_NR\", \"K\", \"I\"],\n",
+    "    figsize=(14, 4),\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7af80e1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gp.plot_irf(\n",
+    "    {\n",
+    "        f\"Percent Ricardian = {omega:0.0%}\": ge.impulse_response_function(\n",
+    "            mod,\n",
+    "            shock_size={\"epsilon_TFP\": 1.0},\n",
+    "            verbose=False,\n",
+    "            omega=omega,\n",
+    "            sigma_N=100.0,\n",
+    "            Theta_N=10.0,\n",
+    "        )\n",
+    "        for omega in [0.2, 0.5, 0.8]\n",
+    "    },\n",
+    "    [\"C_R\", \"C_NR\", \"L_R\", \"L_NR\", \"K\", \"I\", \"Y\"],\n",
+    "    figsize=(14, 4),\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a570e8d1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/Production Functions.ipynb b/examples/Production Functions.ipynb
new file mode 100644
index 0000000..8a1d9eb
--- /dev/null
+++ b/examples/Production Functions.ipynb	
@@ -0,0 +1,470 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "738c0a02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gEconpy as ge\n",
+    "import gEconpy.plotting as gp\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "\n",
+    "from cycler import cycler\n",
+    "from matplotlib.colors import Normalize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "951ac1fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Model Building Complete.\n",
+      "Found:\n",
+      "\t9 equations\n",
+      "\t9 variables\n",
+      "\tThe following variables were eliminated at user request:\n",
+      "\t\tTC_t,U_t\n",
+      "\tThe following \"variables\" were defined as constants and have been substituted away:\n",
+      "\t\tmc_t\n",
+      "\t1 stochastic shock\n",
+      "\t\t 0 / 1 has a defined prior. \n",
+      "\t8 parameters\n",
+      "\t\t 0 / 8 has a defined prior. \n",
+      "\t0 parameters to calibrate.\n",
+      "Model appears well defined and ready to proceed to solving.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "mod_cd = ge.model_from_gcn(\n",
+    "    \"../GCN Files/RBC_steady_state.gcn\", backend=\"pytensor\", mode=\"JAX\", verbose=False\n",
+    ")\n",
+    "mod = ge.model_from_gcn(\"../GCN Files/RBC_with_CES.gcn\", backend=\"pytensor\", mode=\"JAX\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2258123",
+   "metadata": {},
+   "source": [
+    "## Show that the MRS is equal to $\\psi$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e3a5d478",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "globals().update({x.base_name: x.to_ss() for x in mod.variables})\n",
+    "globals().update({x.name: x for x in mod.params})\n",
+    "\n",
+    "# Compute derivatives of production function w.r.t inputs\n",
+    "production_fn = ge.utilities.eq_to_ss(mod.equations[5].args[1])\n",
+    "dY_dK = production_fn.diff(K).simplify()\n",
+    "dY_dL = production_fn.diff(L).simplify()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9680fe8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{w_{ss}}{r_{ss}} = \\left(\\frac{K_{ss} \\left(1 - \\alpha\\right)}{\\alpha L_{ss}}\\right)^{\\frac{1}{\\psi}}$"
+      ],
+      "text/plain": [
+       "Eq(w_ss/r_ss, (K_ss*(1 - alpha)/(alpha*L_ss))**(1/psi))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{K_{ss}}{L_{ss}} = - \\frac{\\alpha \\left(\\frac{w_{ss}}{r_{ss}}\\right)^{\\psi}}{\\alpha - 1}$"
+      ],
+      "text/plain": [
+       "Eq(K_ss/L_ss, -alpha*(w_ss/r_ss)**psi/(alpha - 1))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle Z = - \\frac{V^{\\psi} \\alpha}{\\alpha - 1}$"
+      ],
+      "text/plain": [
+       "Eq(Z, -V**psi*alpha/(alpha - 1))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle MRS = \\psi$"
+      ],
+      "text/plain": [
+       "Eq(MRS, psi)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sympy as sp\n",
+    "\n",
+    "# At the optimum, we require dY/dK / r = dY/dL / w\n",
+    "w_res = sp.solve(dY_dK / r - dY_dL / w, w)[0]\n",
+    "ratio = w_res / r\n",
+    "display(sp.Eq(w / r, w_res / r))\n",
+    "\n",
+    "V, Z = sp.symbols(\"V Z\", positive=True)\n",
+    "Z_solved = sp.solve((w / r - ratio).subs({w / r: V, K / L: Z}), Z)[0]\n",
+    "\n",
+    "display(sp.Eq(K / L, Z_solved.subs({V: w / r})))\n",
+    "\n",
+    "# Use indivator variables V, Z to compute MRS = dZ/dV * V / Z\n",
+    "display(sp.Eq(Z, Z_solved))\n",
+    "display(sp.Eq(sp.Symbol(\"MRS\"), (Z_solved.diff(V) * V / Z).subs({Z: Z_solved})))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03e3a8c3",
+   "metadata": {},
+   "source": [
+    "# Compare CES to Cobb Douglass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6cce49fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cd_production_fn = mod_cd.equations[5].args[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c5478fe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_ces = sp.lambdify([psi, alpha, A, K, L], production_fn)\n",
+    "adjustment_factor = (1 - alpha) ** (alpha - 1) / alpha**alpha\n",
+    "f_cd = sp.lambdify(\n",
+    "    [alpha, A, K, L], ge.utilities.eq_to_ss(adjustment_factor * cd_production_fn)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c4041fc6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rq/XZzlwt2JCl9RnHnc+3ibDqxosTdfOgJIZhAA/XqGGa++67T/Pnz9cHH3xQa22RqKgo53DL7NmzdeTIEb355puSpJKSEvXq1UuXXnqpnnjiCRUWFuquu+7SZZddpv/85z8Nel2upgF8z6FjpXrrm0N6/7vDOllWKUmyWKRR3dvolsEdNLpnnAJZERVwa80yTDNnzhxJ0qhRo2o9P3fuXE2ZMkWSlJOTo6ysLOf3wsPDtWrVKk2fPl0DBw5U69atddNNN+mpp55qzEsD8AEOh6HUfQV68+uDWrO3wDkXJCEqWDcOTNJNg5LUPrruq/AAeK7zWmekpdAzAng3W1mlFm3O1lvrD+nQsTLn85d1b6NJQzpqVI84VkUFPFCLrDMCAOcj/WiR3lp/UEu3HFF5pUOSFBEcoJsGJunXl3ZUcixzQQBfQBgB0KIcDkOrd+frP2sz9G3mjxNSe7aL0KQhnTRxQIJCg3hrAnwJ/+IBtIjyymot/u6wXl+XqYyCUklSgJ9FV/Vtp8lDOmlQp1ayWBiKAXwRYQRAsyootuutbw7q7W+zdLy0Zm2QiOAA3Tq4g6YM66T4KCakAr6OMAKgWezLK9Z/12Zq6dYjqqiqmQ+S2CpEdwxL1k2DkhRu5e0HQA3eDQC41Lbsk3rly/1amZ7nfK5/UrTuHtFZV/VpqwDWBgHwPwgjAM6bYRhan3Fc/1qzX2v3FUqqWaBsbO+2untEZ13ckfkgAOpHGAHQZIZh6Ms9+Xp59X59l3VSkuTvZ9HE/u3121Gd1TUuwtwCAXgEwgiARqt2GPp0R45e+fKAduUUSZKCAvx088Ak/WZkZyXFhJpcIQBPQhgB0GAOh6FPd+Tq+c/3al9+iSQpLMhfv760o+4ckay4iGCTKwTgiQgjAM7JMAx9tjNPz3++V7tziyVJkcEBumN4sqYM7aTo0CCTKwTgyQgjAOplGIa+2JWv5z7fq51Ha4ZjIqwBunNEsu4YnqzI4ECTKwTgDQgjAM5gGIbW7C3Qc6v2avthm6Sa4Zg7hifrruGdFRVKCAHgOoQRALVsyTqhpz/d7bxvTEigvyYP7aTfjOysmDCGYwC4HmEEgCTpQEGJ/v7ZHn26I1dSzdUxky7tqHtHdVFsuNXk6gB4M8II4OPyi8r1whf79O7GbFU7DPlZpBsuStQDV3ZXQjT3jQHQ/AgjgI8qLq/Ua2kZ+u/aTJ2qrJYkXdErTrOu6qke7VisDEDLIYwAPqbaYei9Tdn6x8o9KiypuYvugA7ReuhnPXVJ59YmVwfAFxFGAB+yPuOYnvwwXemnV03tHBumP/ysp67q05Z7xwAwDWEE8AHZx8v01092OSenRgYH6HdXdNekIR0VyF10AZiMMAJ4sRJ7lV75cr9eX5upimqH/CzSrZd00Mwre3CZLgC3QRgBvJBhGFq+7aie+niXCortkqRhXVvrT9f0Vs92kSZXBwC1EUYAL7M/v1h/WrZT32QckyR1bB2qR67upSt7My8EgHsijABe4lRFtV5avU//WZuhympD1gA/TR/dVXeP7CxrgL/Z5QFAvQgjgBdYlZ6nx5fv1JGTpyRJo3vG6Ylr+ygpJtTkygDg3AgjgAc7cvKUHvtghz7flS9Jah8doscm9GZIBoBHIYwAHsjhMPT2t4f0zKe7VVpRrQA/i+4e2VnTR3dVaBD/rAF4Ft61AA+zP79EDy3erk2HTkiSBnZspZTrL1C3tizhDsAzEUYAD1FZ7dC/Uw/oxS/2q6LaobAgf/1xXE/9+pKO8vNjSAaA5yKMAB7g+8M2zXp/m3bnFkuSRvVoo79cd4Hac1ddAF6AMAK4sYoqh178Yp/mpB5QtcNQq9BAPTqhtyb2b88EVQBegzACuKk9ucV6YOFW503trrkwXo9f20ex4VaTKwMA1yKMAG6m2mHov2sz9I+Ve1VR7VCr0EA9NfECjb8w3uzSAKBZEEYAN3LoWKkeXLRNGw/WXCkzpmecUm64QHERwSZXBgDNhzACuAHDMLRgQ7ae+jhdZRXVCgvy12MT+ujGgYnMDQHg9QgjgMlOllXoocXfa8XOXEnS4OQY/ePGfizlDsBnEEYAE32bcUz3L9yqHFu5Av0tmnVVD901vDPrhgDwKYQRwARV1Q69tHq/Xlq9Tw5D6tQ6VC/98iJdkBhldmkA0OIII0ALO3LylB54d6s2HDwuSbrhokQ98fM+CrfyzxGAb+LdD2hBq9Lz9OCibbKdqlS4NUB/ua6vft6/vdllAYCpCCNAC6iqduhvK/fo36kZkqR+SdF66ZYB6tCaSaoAQBgBmll+cbmmz9+ibzNrhmXuGJash8b1VFCAn8mVAYB7aNS7YUpKigYNGqSIiAjFxcVp4sSJ2rNnT4P3/+qrrxQQEKD+/fs3tk7AI23IPK7xL67Tt5nHFRbkr1duvUiPTuhNEAGAn2jUO2JqaqqmTp2q9evXa9WqVaqqqtLYsWNVWlp6zn1tNpsmTZqkMWPGNLlYwFMYhqHX0g7ol/9Zr4Jiu7q3Ddfy6cNZ0h0A6mAxDMNo6s4FBQWKi4tTamqqRo4ceda2t9xyi7p16yZ/f38tW7ZMW7dubfDrFBUVKSoqSjabTZGRkU0tF2gRpfYqPbhomz7dUbOI2XUD2usv1/VVaBCjogB8S0M/v8+rr9hms0mSYmJiztpu7ty5OnDggB577LEG/Vy73a6ioqJaG+AJso6V6YY5X+vTHbkK9LfozxP76p839SOIAMBZNPkd0jAMzZw5U8OHD1ffvn3rbbdv3z499NBDWrt2rQICGvZyKSkpeuKJJ5paGmCKr/cX6r753+lkWaXaRFj16q8v1sUdW5ldFgC4vSb3jEybNk3bt2/XggUL6m1TXV2tW2+9VU888YS6d+/e4J89e/Zs2Ww255adnd3UMoFmZxiG5n2Vqdve2KCTZZXqlxilD6cNJ4gAQAM1ac7I9OnTtWzZMqWlpSk5ObnedidPnlSrVq3k7+/vfM7hcMgwDPn7+2vlypUaPXr0OV+POSMwW0FBgVavXq3i4mIlJSVp9OjRCgwMlL2qWo8u26mFm2oC83UD2ivl+gsUHOh/jp8IAN6voZ/fjRqmMQxD06dP19KlS7VmzZqzBhFJioyM1Pfff1/ruX/9619avXq13n///XPuD5jNZrNp5syZevvtt1VRUeF8PiEhQffPekjfBF2k77JOys8iPTSup+4e0VkWCze5A4DGaFQYmTp1qubPn68PPvhAERERys2tuVogKipKISEhkmqGWI4cOaI333xTfn5+Z8wniYuLU3Bw8FnnmQDuoKSkRGPGjNHmzZvP+N7Ro0f1hwdmqN2oXyv2sl/rpV8O0KgecSZUCQCer1FzRubMmSObzaZRo0YpPj7euS1cuNDZJicnR1lZWS4vFGhpf/3rX51BJCIiQg888IDmzJmja6+91tkmd83bShkZQRABgPNwXuuMtBTmjKCl2e12JSYmqrCwUAEBAdqyZUut3rwnn3zSean63Xffrddee82sUgHAbbXIOiOAt9q9e7cKCwslSRMmTDhjWPGBBx5QYGCgJCktLa3F6wMAb0IYAerw08mqdS3qFxYWpuDg4DPaAgAajzAC1KFLly7ORfo++OADHTt2TLNmzdKsWbNUUVGhDz/8UMXFxZKkXr16mVkqAHg81qgG6hATE6Prr79e7733ngoLCzV+/Hh9++23kqQOHTrUurXB3XffbVaZAOAV6BkB6pB9vExZydcoLKJmwtUPQUSSZsyYoRMnTkiSRo8erQkTJphSIwB4C8II8D/25xfrxle/UY5i1OfOZ5WYmFhnu/Hjx2vp0qW1VhgGADQeYQT4ie8P23Tjq98ot6hc3eLCtfyJyTpw4IDeeOMNZ5t77rlHGzZs0EcffcSl5gDgAqwzApz2bcYx3fl/m1Rir1K/xCjNu32wWoUFSZJKS0sVHh4uqWZl1rCwMDNLBQCP0Cz3pgG81Ze783Xv25tlr3Lo0s4x+u/kQQq38s8DAFoC77bweZ/tzNW0+d+pstrQFb3i9PKtF3HXXQBoQYQR+LRPvs/RjAVbVOUwdM2F8Xru5v4K9D9zKlVISIh27NjhfAwAcB3CCHzWh9uO6v6FW1XtMDSxf4L+fmM/BdQRRCTJz89Pffr0aeEKAcA3EEbgkz7YekQPLNwqhyHdcFGinv3FhfL3s5hdFgD4JMIIfM7izYc16/1tchjSzQOTlHL9BfI7RxCpqKjQX//6V0nSww8/rKCgoJYoFQB8Apf2wqe8fzqIGIb0y8Ed9JeJfc8ZRCQu7QWApmjo5zeLnsFnLN92VH84HURuu7Rjg4MIAKB5MUwDn7BiR65zjsitl3TQkz/vI4uFIAIA7oCeEXi9NXvyNX3Bd6p2GLr+ovZ66ud9CSIA4EYII/BqX+8v1D1vbVZltaHxF8br2RsuZGgGANwMYQRea9PB47rrzU2yVzl0Ra+2ev7m/vWuIwIAMA/vzPBKO47YdPvcjSqrqNaIbrF65VcD6lxZFQBgPiawwuscLCzVlLkbVGyv0iXJMXrttoGyBpzfvWaCg4O1YcMG52MAgOsQRuBV8ovLNemNDSosqVCfhEj9d/JAhQSd/03v/P39NWjQIBdUCAD4X/Rbw2sUlVdq8hsblXW8TB1bh2re7YMVERxodlkAgHOgZwReobyyWnf/3ybtyilSbLhVb91xidpEWF328ysqKvTCCy9Ikn73u9+xHDwAuBDLwcPjVTsM3ffOZn22M08R1gC9e8+l6pMQ5dLXYDl4AGg8loOHTzAMQ3/6YIc+25mnoAA/vTZpoMuDCACgeRFG4NH+nZah+d9myWKRXrylv4Z0aW12SQCARiKMwGN9vD1HT3+6W5L06DW99bO+8SZXBABoCsIIPNLmQyf0wHtbJUlThnbS7cOSzS0IANBkhBF4nKxjZbr7zU2qqHLoil5x+tM1vc0uCQBwHggj8Cgnyyo0Zd4GHS+tUN/2kXrhlgHy58Z3AODRWGcEHqOiyqF73tqsjIJSJUQF643JgxRmbZlTODg4WF9++aXzMQDAdQgj8AiGYejRD3bo28zjCrcG6I3bBykusuVCgb+/v0aNGtVirwcAvoRhGniEN785pHc3ZsvPIr106wD1bMfidwDgLegZgdv7en+hnvwoXZL00LieurxHXIvXUFlZqddee02S9Jvf/EaBgdzzBgBcheXg4dayjpXp2lfW6WRZpa4f0F7/uKmfLJaWn7DKcvAA0HgsBw+PV2Kv0l1vbtTJskr1S4rWX6+/wJQgAgBoXoQRuCWHw9ADC7dqb16J4iKseu22ixUc6G92WQCAZkAYgVt6/ot9WpVec/O7f992sdq24JUzAICWRRiB21m9O08vfrFPkpRy3QUa0KGVyRUBAJoTYQRuJft4mR5YuE2SNGlIR91wcaLJFQEAmlujwkhKSooGDRqkiIgIxcXFaeLEidqzZ89Z91myZImuvPJKtWnTRpGRkRoyZIg+++yz8yoa3qm8slr3vr1ZtlOV6p8UrUfG9zK7JABAC2hUGElNTdXUqVO1fv16rVq1SlVVVRo7dqxKS0vr3SctLU1XXnmlPvnkE23evFmXX365JkyYoC1btpx38fAujy/fqZ1HixQTFqR//eoiWQPcZ8Kq1WrVRx99pI8++khWq9XscgDAq5zXOiMFBQWKi4tTamqqRo4c2eD9+vTpo5tvvlmPPvpog9qzzoj3e29jtv6weLssFumtOy7R8G6xZpcEADhPDf38Pq8VWG02myQpJiamwfs4HA4VFxefdR+73S673e78uqioqOlFwu3tOGLTnz7YIUn6/ZXdCSIA4GOaPIHVMAzNnDlTw4cPV9++fRu83z/+8Q+VlpbqpptuqrdNSkqKoqKinFtSUlJTy4SbKyqv1G/f2Sx7lUOje8bpvlFdzS6pTpWVlZo3b57mzZunyspKs8sBAK/S5GGaqVOn6uOPP9a6deuUmNiwKx4WLFigu+66Sx988IGuuOKKetvV1TOSlJTEMI2XMQxD0+Zv0cff5yixVYg+nj5CUaHuec8XloMHgMZr1mGa6dOna/ny5UpLS2twEFm4cKHuvPNOLVq06KxBRKqZLMgkQe/37sZsffx9jgL8LHrplwPcNogAAJpXo8KIYRiaPn26li5dqjVr1ig5OblB+y1YsEB33HGHFixYoPHjxzepUHiXvXnFenz5TknSrKt6sLAZAPiwRoWRqVOnav78+frggw8UERGh3NxcSVJUVJRCQkIkSbNnz9aRI0f05ptvSqoJIpMmTdILL7ygSy+91LlPSEiIoqKiXPm7wEOcqqjWtPnfyV7l0GXd2+juEZ3NLgkAYKJGTWCdM2eObDabRo0apfj4eOe2cOFCZ5ucnBxlZWU5v/73v/+tqqoqTZ06tdY+v/vd71z3W8CjPPlRuvbmlahNhFX/uKmf/Py4Ey8A+LJGD9Ocy7x582p9vWbNmsa8BLzcR9uPasGGLFks0vM391dsOHODAMDXcW8atJjs42Wavfh7SdK0y7tqWFfWEwEAnOeiZ0BDVTsMPbBwq4rtVRrYsZV+N6ab2SU1itVq1Xvvved8DABwHcIIWsSrqQe06dAJhVsD9NzN/RXg71mdcgEBAbrxxhvNLgMAvJJnfSLAI+04YtNzq/ZKkh6/to+SYkJNrggA4E7oGUGzKq+s1v0Lt6rKYWhc33a64aL2ZpfUJFVVVVq6dKkk6brrrlNAAP90AMBVeEdFs3r6093an19zGe9frrtAFotnXsZrt9ud91MqKSkhjACACzFMg2azdl+B5n19UJL0t19cqJiwIHMLAgC4JcIImsXJsgo9uGibJGnSkI4a1SPO5IoAAO6KMIJm8acPdiqvyK7ObcI0e1wvs8sBALgxwghcbsWOHH247aj8/Sx67qb+CgnyN7skAIAbI4zApU6UVuj/LdshSbr3ss7qlxRtbkEAALdHGIFLPf7hThWWVKhbXLhmeNgqqwAAc3B9Ilxm5c5cfbD1qPws0t9v7CdrgPcMzwQFBWnu3LnOxwAA1yGMwCVOllXokdPDM78Z2cXrhmcCAwM1ZcoUs8sAAK/EMA1c4skP01VQbFeXNmG6/wqGZwAADUfPCM7b5+l5WrLliPws0t9u7KfgQO8ZnvlBVVWVPvvsM0nSVVddxQqsAOBCvKPivNhOVerhpd9Lku4e0VkXdWhlckXNw26365prrpHEcvAA4GoM0+C8PLtit/KL7eocG6YHruxudjkAAA9EGEGTbTp4XO98myVJ+uv1F3jl8AwAoPkRRtAkFVUOzV5SMzxz08BEXdq5tckVAQA8FWEETfJa2gHtyy9R67AgPXw1954BADQdYQSNllFQohdX75ckPTqht6JDWQQMANB0hBE0imEYemTpDlVUOTSiW6yu7ZdgdkkAAA/H9YlolMXfHdE3GccUHOinv0y8QBaLxeySWkRQUJBefvll52MAgOsQRtBgx0rseurjdEnS/Vd0V4fWoSZX1HICAwM1depUs8sAAK/EMA0a7OlPd+tkWaV6xUfqzuHJZpcDAPAS9IygQTYfOq5Fmw9Lkv5yXV8F+vtWjq2urtbatWslSSNGjJC/P2uqAICrEEZwTlXVDv1p2U5J0s0Dk7x2yfezKS8v1+WXXy6pZjn4sLAwkysCAO/hW3/eokne+TZL6TlFigoJ1B9+1sPscgAAXoYwgrMqKLbr7yv3SJJmXdVDrcOtJlcEAPA2hBGc1dOf7lZxeZUuaB+lXw7uYHY5AAAvRBhBvTYePK7F3x2WxSL9eWJf+fv5xpoiAICWRRhBnWomre6QJN0yKEn9k6LNLQgA4LUII6jTW+sPaXdusaJDAzXrqp5mlwMA8GJc2oszFJbY9c+VeyXVTFqNCWP588DAQD377LPOxwAA1yGM4Az/WLlXxfYq9W0fqVsGMWlVqrkfzaxZs8wuAwC8EsM0qCX9aJEWbsySJD16TR8mrQIAmh09I3AyDENPfrRTDkMaf2G8BifHmF2S26iurtZ3330nSbroootYDh4AXIgwAqfPduZpfcZxWQP8NHsck1Z/qry8XIMHD5bEcvAA4GoM00CSZK+q1l8/2SVJ+s3IzkpsFWpyRQAAX0EYgSTpjXUHlXW8THERVt17WRezywEA+BDCCJRfXK5XvtwvSfrjz3oqzMroHQCg5TQqjKSkpGjQoEGKiIhQXFycJk6cqD179pxzv9TUVF188cUKDg5W586d9eqrrza5YLjePz7bqxJ7lfolReu6Ae3NLgcA4GMaFUZSU1M1depUrV+/XqtWrVJVVZXGjh2r0tLSevfJzMzU1VdfrREjRmjLli16+OGHNWPGDC1evPi8i8f523HEpvc2Z0uSHr2mt/y4lBcA0MIa1R+/YsWKWl/PnTtXcXFx2rx5s0aOHFnnPq+++qo6dOig559/XpLUq1cvbdq0SX//+991ww03NK1quIRhGPrzR+kyDOnafgm6uGMrs0sCAPig85ocYLPZJEkxMfWvR/HNN99o7NixtZ676qqr9Prrr6uysrLOpbXtdrvsdrvz66KiovMpE/X4Yle+vs2suZT3j1zKe1aBgYF67LHHnI8BAK7T5DBiGIZmzpyp4cOHq2/fvvW2y83NVdu2bWs917ZtW1VVVamwsFDx8fFn7JOSkqInnniiqaWhAaqqHXp6xW5J0h3Dk9U+OsTkitxbUFCQHn/8cbPLAACv1OSraaZNm6bt27drwYIF52xrsdSeh2AYRp3P/2D27Nmy2WzOLTs7u6lloh7vbTqs/fklahUaqN+O4lJeAIB5mtQzMn36dC1fvlxpaWlKTEw8a9t27dopNze31nP5+fkKCAhQ69at69zHarXKarU2pTQ0QKm9Ss99XnNX3hljuikymGGHc3E4HNq1q2ZRuF69esnPj6viAcBVGhVGDMPQ9OnTtXTpUq1Zs0bJycnn3GfIkCH68MMPaz23cuVKDRw4kLF3k/xnbYYKiu3qEBOqX13S0exyPMKpU6ecw5EsBw8ArtWoP++mTp2qt99+W/Pnz1dERIRyc3OVm5urU6dOOdvMnj1bkyZNcn5977336tChQ5o5c6Z27dqlN954Q6+//roefPBB1/0WaLD84nK9lpYhSfrDz3ooKIC/8AEA5mrUJ9GcOXNks9k0atQoxcfHO7eFCxc62+Tk5CgrK8v5dXJysj755BOtWbNG/fv315///Ge9+OKLXNZrkuc/36eyimr1S4rW+AvOnDwMAEBLa/QwzbnMmzfvjOcuu+wy5+3XYZ79+cVauLFmMvAjV/eqdwIxAAAtiT56H/L0p3tU7TB0Ze+2Gpxc/9owAAC0JMKIj/g245g+35Unfz+L/vgzFjgDALgPwogPMAxDKZ/WLHB286AkdY0LN7kiAAB+xL3ifcBnO/O0NfukQgL9df8V3cwuxyMFBgY6rwDjknQAcC3CiJerdhj6x8o9kqQ7hycrLiLY5Io8U1BQkP72t7+ZXQYAeCWGabzcsi1HtC+/RFEhgbp7ZGezywEA4Az0jHixiiqHc9n3ey/roqgQhheayuFwONfP6dChA8vBA4ALEUa82IINWTp84pTaRFg1ZWgns8vxaKdOnXLe/oDl4AHAtfjzzkuVVVTppdX7JUkzRndVSJC/yRUBAFA3woiXmvvVQRWW2JUUE6KbB3UwuxwAAOpFGPFCtrJK/Tv1gCRp5pXduRkeAMCt8Snlhf6ddkBF5VXq0TZC1/Zrb3Y5AACcFWHEy+QXl2vuVwclSb8f213+ftwMDwDg3ggjXubl1ft1qrJaAzpE68rebc0uBwCAc+LSXi+SfbxMCzbUrIUx66oesljoFXGVgIAA3Xfffc7HAADX4V3Vizz3+V5VVhsa0S1WQ7vEml2OV7FarXrllVfMLgMAvBLDNF7iQEGJlm05Ikl6cGwPk6sBAKDh6BnxEi9+sU8OQ7qiV1v1S4o2uxyvYxiGCgsLJUmxsbEMgQGACxFGvMC+vGIt33ZUknT/Fd1MrsY7lZWVKS4uThLLwQOAqzFM4wWe/2KfDEP6WZ926ts+yuxyAABoFMKIh9udW6SPt+dIku6/kl4RAIDnIYx4uOdX7ZMkjb8wXj3bRZpcDQAAjUcY8WA7jti0YmeuLBbp/jH0igAAPBNhxIM9/3lNr8i1/RLUrW2EydUAANA0hBEPtf3wSX2+K09+FmkGvSIAAA/Gpb0e6rlVeyVJE/u3V5c24SZX4/0CAgI0efJk52MAgOvwruqBtmSd0Jd7CuTvZ9F0ekVahNVq1bx588wuAwC8EsM0Hui503NFrh/QXsmxLL4FAPBs9Ix4mE0Hjyttb4EC/CyaPppekZZiGIbKysokSaGhoSwHDwAuRM+Ih3nu85q5IjcOTFSH1qEmV+M7ysrKFB4ervDwcGcoAQC4BmHEg6zPOKav9h9ToL9FUy/vanY5AAC4BGHEQxiGoX+evoLm5kFJSmxFrwgAwDsQRjzENweOaUPmcQX5+9ErAgDwKoQRD/DTXpFbL+mg+KgQkysCAMB1CCMeYN3+Qm06dELWAD/9dlQXs8sBAMClCCNu7qe9Ir+6pKPaRgabXBEAAK7FOiNuLnVvgbZknVRwoJ/uHdXZ7HJ8lr+/v37xi184HwMAXIcw4sYMw3CutnrbpR0VF0GviFmCg4O1aNEis8sAAK/EMI0bW7OnQNuya3pFfjOSuSIAAO9EGHFThmHo+dOrrU4a0kltIqwmVwQAQPMgjLipL/fka9thm0IC/fWbkcwVMVtpaaksFossFotKS0vNLgcAvAphxA3V9IrUzBWZNKSjYsPpFQEAeK9Gh5G0tDRNmDBBCQkJslgsWrZs2Tn3eeedd9SvXz+FhoYqPj5et99+u44dO9aUen3C6t352n7YptAgekUAAN6v0WGktLRU/fr108svv9yg9uvWrdOkSZN05513aufOnVq0aJE2btyou+66q9HF+oLavSKd1JpeEQCAl2v0pb3jxo3TuHHjGtx+/fr16tSpk2bMmCFJSk5O1j333KNnn322sS/tE77Yla/vj9ArAgDwHc0+Z2To0KE6fPiwPvnkExmGoby8PL3//vsaP358vfvY7XYVFRXV2nyBYRh6/ouaK2gmD+2kmLAgkysCAKD5tUgYeeedd3TzzTcrKChI7dq1U3R0tF566aV690lJSVFUVJRzS0pKau4y3cKq9DztOFKksCB/3T2CXhEAgG9o9jCSnp6uGTNm6NFHH9XmzZu1YsUKZWZm6t577613n9mzZ8tmszm37Ozs5i7TdD+dK0KviPvx9/fX1Vdfrauvvprl4AHAxZp9OfiUlBQNGzZMs2bNkiRdeOGFCgsL04gRI/TUU08pPj7+jH2sVqusVt+auLkyPU/pOfSKuKvg4GB9/PHHZpcBAF6p2XtGysrK5OdX+2V++MvSMIzmfnmPYBiGXjjdKzJlWCe1olcEAOBDGh1GSkpKtHXrVm3dulWSlJmZqa1btyorK0tSzRDLpEmTnO0nTJigJUuWaM6cOcrIyNBXX32lGTNmaPDgwUpISHDNb+HhPttZ0ysSbg3QXcPpFQEA+JZGD9Ns2rRJl19+ufPrmTNnSpImT56sefPmKScnxxlMJGnKlCkqLi7Wyy+/rN///veKjo7W6NGj9cwzz7igfM/ncPx4D5opQ+kVcVelpaWKi4uTJOXn5yssLMzkigDAe1gMDxgrKSoqUlRUlGw2myIjI80ux6VW7MjRvW9/pwhrgNb+8XJFhxJG3FFpaanCw8Ml1fQOEkYA4Nwa+vnNvWlMVNMrUjNX5PZhnQgiAACfRBgx0YqdudqdW6wIa4DuZK4IAMBHEUZM4nD8eAXN7cOTFRUaaHJFAACYgzBikk935GpPXrEiggN05/Bks8sBAMA0hBETVDsM/XPVHknSHcOSFRVCrwgAwHc1+wqsONMHW4/oQEGpokMDdecIekU8gZ+fny677DLnYwCA6xBGWlhltcN5Bc09I7soMpheEU8QEhKiNWvWmF0GAHgl/sRrYYs2HVbW8TLFhls1eWhHs8sBAMB0hJEWVF5ZrZdW1/SKTL28i0KD6JgCAIAw0oLmf5ulHFu54qOC9cvBHcwuB41QWlqqNm3aqE2bNiotLTW7HADwKvxp3kLKKqr0rzX7JUnTR3dTcKC/yRWhsQoLC80uAQC8Ej0jLeT/vj6kwpIKdYgJ1Y0DE80uBwAAt0EYaQFF5ZV6NfWAJOn+K7op0J/DDgDAD/hUbAGvr82U7VSlurQJ08/7tze7HAAA3AphpJmdKK3Q6+syJUkzr+whfz+LyRUBAOBeCCPN7NXUAyqxV6l3fKTG9W1ndjkAALgdrqZpRkdPntLcrw9Kkh68qrv86BXxWH5+fho4cKDzMQDAdQgjzei5VXtVUeXQJckxurxHnNnl4DyEhIRo48aNZpcBAF6JP/GayZ7cYi3+7rAk6aFxPWWx0CsCAEBdCCPN5G+f7ZbDkMb1bacBHVqZXQ4AAG6LMNIMNmQe1+e78uXvZ9GDV/Uwuxy4QFlZmTp16qROnTqprKzM7HIAwKswZ8TFDMPQ05/ukiTdPChJXdqEm1wRXMEwDB06dMj5GADgOvSMuNjK9Dx9l3VSIYH+un9MN7PLAQDA7RFGXKiq2qFnV+yWJN01IllxkcEmVwQAgPsjjLjQe5sO60BBqVqFBuo3IzubXQ4AAB6BMOIixeWV+ueqPZKk6aO7KSI40OSKAADwDIQRF3nlywMqLKlQ5zZhum1IR7PLAQDAY3A1jQtkHSvTG6dvhvfI1b0U6E/G8zYWi0W9e/d2PgYAuA5hxAWeXrFLFdUOjegWq9E9WfbdG4WGhmrnzp1mlwEAXok/4c/TtxnH9Mn3ufKzSP9vfG/+agYAoJEII+fB4TD01Mc1C5z9cnAH9WgXYXJFAAB4HsLIeViy5Yi+P2JThDVAM6/sbnY5aEZlZWXq06eP+vTpw3LwAOBizBlpouLySj1zeoGzaaO7qnW41eSK0JwMw1B6errzMQDAdegZaaLnP9+ngmK7kmPDNGVYJ7PLAQDAYxFGmmB3bpHmfX1QkvT4tX1kDfA3tyAAADwYYaSRDMPQn5btULXD0Li+7XRZ9zZmlwQAgEcjjDTS0i1HtPHgCYUE+uv/XdPb7HIAAPB4hJFGsJ2q1F8/qbmUd/qYrmofHWJyRQAAeD6upmmE51btdd5/5q7h3JXXl1gsFnXs2NH5GADgOoSRBtqafVJvfnNQkvTktX0VFECnki8JDQ3VwYMHzS4DALwSn6gNUFHl0EOLt8thSBP7J2h4t1izSwIAwGsQRhrgtbQD2p1brJiwID06oY/Z5QAA4FUaHUbS0tI0YcIEJSQkyGKxaNmyZefcx26365FHHlHHjh1ltVrVpUsXvfHGG02pt8Xtzy/Ri1/slyQ9ek1vxYQFmVwRzHDq1CkNGjRIgwYN0qlTp8wuBwC8SqPnjJSWlqpfv366/fbbdcMNNzRon5tuukl5eXl6/fXX1bVrV+Xn56uqqqrRxbY0h8PQ7CXbVVHt0KgebfTz/glmlwSTOBwObdq0yfkYAOA6jQ4j48aN07hx4xrcfsWKFUpNTVVGRoZiYmIkSZ06dWrsy5pi/oYsbTx4QqFB/npqYl+uogAAoBk0+5yR5cuXa+DAgXr22WfVvn17de/eXQ8++OBZu7rtdruKiopqbS0t+3iZUk6vKfKHq3oosVVoi9cAAIAvaPZLezMyMrRu3ToFBwdr6dKlKiws1H333afjx4/XO28kJSVFTzzxRHOXVq9qh6Hfv7dNpRXVGtwpRrcN6WRaLQAAeLtm7xlxOByyWCx65513NHjwYF199dX65z//qXnz5tXbOzJ79mzZbDbnlp2d3dxl1vLGukxtOHhcYUH++vuN/eTvx/AMAADNpdl7RuLj49W+fXtFRUU5n+vVq5cMw9Dhw4fVrVu3M/axWq2yWq3NXVqd9uQW62+f7ZEk/ema3urQmuEZAACaU7P3jAwbNkxHjx5VSUmJ87m9e/fKz89PiYmJzf3yjVJR5dDM97aqotqh0T3jdPOgJLNLghuJjY1VbCwL3gGAqzU6jJSUlGjr1q3aunWrJCkzM1Nbt25VVlaWpJohlkmTJjnb33rrrWrdurVuv/12paenKy0tTbNmzdIdd9yhkBD3utHci1/s086jRYoODdTT11/A1TNwCgsLU0FBgQoKChQWFmZ2OQDgVRodRjZt2qQBAwZowIABkqSZM2dqwIABevTRRyVJOTk5zmAiSeHh4Vq1apVOnjypgQMH6le/+pUmTJigF1980UW/gmt8faBQr6ypWdzsLxMvUFxksMkVAQDgGyyGYRhmF3EuRUVFioqKks1mU2RkpMt/fmGJXVe/sFb5xXbdPDBJz/ziQpe/BgAAvqahn98+f28ah8PQg4u2Kb/Yrq5x4Xr8Wu49gzOdOnVKo0aN0qhRo1gOHgBcrNmvpnF3r6/L1Jo9BbIG+OnlWwcoJMjf7JLghhwOh1JTU52PAQCu49M9I9uyT+qZFbslSY9O6K2e7Vw/BAQAAM7OZ8OIw2Hoj4u3q8ph6OoL2unWwR3MLgkAAJ/ks2HEz8+il28doCt6xSnl+gu5jBcAAJP49JyRrnER+u/kQWaXAQCAT/PZnhEAAOAefLpnBGiM0FDuUwQAzYEwAjRAWFiYSktLzS4DALwSwzQAAMBUhBEAAGAqwgjQAOXl5Ro/frzGjx+v8vJys8sBAK/CnBGgAaqrq/XJJ584HwMAXIeeEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAU3FpL9AAYWFhMgzD7DIAwCvRMwIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwVYDZBTSEYRiSpKKiIpMrAQAADfXD5/YPn+P18YgwUlxcLElKSkoyuRIAANBYxcXFioqKqvf7FuNcccUNOBwOHT16VBEREbJYLLW+V1RUpKSkJGVnZysyMtKkCj0Px63xOGZNw3FrGo5b03DcGq85j5lhGCouLlZCQoL8/OqfGeIRPSN+fn5KTEw8a5vIyEhOvCbguDUex6xpOG5Nw3FrGo5b4zXXMTtbj8gPmMAKAABMRRgBAACm8vgwYrVa9dhjj8lqtZpdikfhuDUex6xpOG5Nw3FrGo5b47nDMfOICawAAMB7eXzPCAAA8GyEEQAAYCrCCAAAMBVhBAAAmMojwsi//vUvJScnKzg4WBdffLHWrl171vapqam6+OKLFRwcrM6dO+vVV19toUrdR2OO2Zo1a2SxWM7Ydu/e3YIVmy8tLU0TJkxQQkKCLBaLli1bds59ONcaf9w436SUlBQNGjRIERERiouL08SJE7Vnz55z7ufL51tTjhnnmjRnzhxdeOGFzgXNhgwZok8//fSs+5hxnrl9GFm4cKHuv/9+PfLII9qyZYtGjBihcePGKSsrq872mZmZuvrqqzVixAht2bJFDz/8sGbMmKHFixe3cOXmaewx+8GePXuUk5Pj3Lp169ZCFbuH0tJS9evXTy+//HKD2nOu1WjscfuBL59vqampmjp1qtavX69Vq1apqqpKY8eOVWlpab37+Pr51pRj9gNfPtcSExP19NNPa9OmTdq0aZNGjx6tn//859q5c2ed7U07zww3N3jwYOPee++t9VzPnj2Nhx56qM72f/jDH4yePXvWeu6ee+4xLr300mar0d009ph9+eWXhiTjxIkTLVCdZ5BkLF269KxtONfO1JDjxvl2pvz8fEOSkZqaWm8bzrfaGnLMONfq1qpVK+O///1vnd8z6zxz656RiooKbd68WWPHjq31/NixY/X111/Xuc8333xzRvurrrpKmzZtUmVlZbPV6i6acsx+MGDAAMXHx2vMmDH68ssvm7NMr+Dr59r54nz7kc1mkyTFxMTU24bzrbaGHLMfcK7VqK6u1rvvvqvS0lINGTKkzjZmnWduHUYKCwtVXV2ttm3b1nq+bdu2ys3NrXOf3NzcOttXVVWpsLCw2Wp1F005ZvHx8Xrttde0ePFiLVmyRD169NCYMWOUlpbWEiV7LF8/15qK8602wzA0c+ZMDR8+XH379q23Hefbjxp6zDjXanz//fcKDw+X1WrVvffeq6VLl6p37951tjXrPPOIu/ZaLJZaXxuGccZz52pf1/PerDHHrEePHurRo4fz6yFDhig7O1t///vfNXLkyGat09NxrjUe51tt06ZN0/bt27Vu3bpztuV8q9HQY8a5VqNHjx7aunWrTp48qcWLF2vy5MlKTU2tN5CYcZ65dc9IbGys/P39z/iLPj8//4zk9oN27drV2T4gIECtW7dutlrdRVOOWV0uvfRS7du3z9XleRVfP9dcyVfPt+nTp2v58uX68ssvlZiYeNa2nG81GnPM6uKL51pQUJC6du2qgQMHKiUlRf369dMLL7xQZ1uzzjO3DiNBQUG6+OKLtWrVqlrPr1q1SkOHDq1znyFDhpzRfuXKlRo4cKACAwObrVZ30ZRjVpctW7YoPj7e1eV5FV8/11zJ1843wzA0bdo0LVmyRKtXr1ZycvI59/H1860px6wuvnau1cUwDNnt9jq/Z9p51qzTY13g3XffNQIDA43XX3/dSE9PN+6//34jLCzMOHjwoGEYhvHQQw8Zt912m7N9RkaGERoaajzwwANGenq68frrrxuBgYHG+++/b9av0OIae8yee+45Y+nSpcbevXuNHTt2GA899JAhyVi8eLFZv4IpiouLjS1bthhbtmwxJBn//Oc/jS1bthiHDh0yDINzrT6NPW6cb4bx29/+1oiKijLWrFlj5OTkOLeysjJnG8632ppyzDjXDGP27NlGWlqakZmZaWzfvt14+OGHDT8/P2PlypWGYbjPeeb2YcQwDOOVV14xOnbsaAQFBRkXXXRRrUu5Jk+ebFx22WW12q9Zs8YYMGCAERQUZHTq1MmYM2dOC1dsvsYcs2eeecbo0qWLERwcbLRq1coYPny48fHHH5tQtbl+uAzwf7fJkycbhsG5Vp/GHjfON6PO4yXJmDt3rrMN51ttTTlmnGuGcccddzg/C9q0aWOMGTPGGUQMw33OM4thnJ6ZAgAAYAK3njMCAAC8H2EEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKb6/6u9vnR+owB4AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "psis = np.linspace(0.1, 3, 10000)\n",
+    "fig, ax = plt.subplots()\n",
+    "Y_ces = f_ces(psis, 0.33, 1, 2, 1)\n",
+    "ax.plot(psis[psis < 0.97], Y_ces[psis < 0.97], color=\"tab:blue\")\n",
+    "ax.plot(psis[psis > 1.03], Y_ces[psis > 1.03], color=\"tab:blue\")\n",
+    "ax.scatter(1.0, f_cd(0.33, 1, 2, 1), lw=2, facecolor=\"none\", edgecolor=\"k\", zorder=100)\n",
+    "ax.vlines(1.0, ax.get_ylim()[0], f_cd(0.33, 1, 2, 1), ls=\"--\", color=\"k\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b74dbd24",
+   "metadata": {},
+   "source": [
+    "# Maximum Value of $\\psi$\n",
+    "\n",
+    "The equation for $N_{ss}$, the steady-state capital-labor ratio, is:\n",
+    "\n",
+    "$$N_{ss} = \\left ( \\frac{\\left ( \\frac{1}{\\beta} - (1 - \\delta) \\right ) ^{\\psi - 1} \\alpha ^ {\\frac{1 - \\psi}{\\psi}}  (A_{ss} mc_{ss}) ^ {1 - \\psi} - \\alpha ^ {\\frac{1}{\\psi}}}{(1 - \\alpha) ^ {\\frac{1}{\\psi}}} \\right) ^ {\\frac{\\psi}{1 - \\psi}}$$\n",
+    "\n",
+    "This needs to be strictly positive. It is possible to have negative values in the numerator, when:\n",
+    "\n",
+    "$$\\left ( \\frac{1}{\\beta} - (1 - \\delta) \\right ) ^{\\psi - 1} \\alpha ^ {\\frac{1 - \\psi}{\\psi}}  (A_{ss} mc_{ss}) ^ {1 - \\psi} \\lt \\alpha ^ {\\frac{1}{\\psi}} $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8c5e7688",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psi_zero = sp.solve(\n",
+    "    sp.Eq(\n",
+    "        (1 / beta - (1 - delta)) ** (psi - 1) * alpha ** ((1 - psi) / psi),\n",
+    "        alpha ** (1 / psi),\n",
+    "    ),\n",
+    "    psi,\n",
+    ")[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d623a6b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{\\log{\\left(\\frac{\\beta}{\\alpha} \\right)} - \\log{\\left(\\beta \\delta - \\beta + 1 \\right)}}{\\log{\\left(\\beta \\right)} - \\log{\\left(\\beta \\delta - \\beta + 1 \\right)}}$"
+      ],
+      "text/plain": [
+       "(log(beta/alpha) - log(beta*delta - beta + 1))/(log(beta) - log(beta*delta - beta + 1))"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b8c9a935",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = sp.lambdify([alpha, beta, delta], psi_zero)\n",
+    "alphas = np.linspace(1e-4, 0.99, 100)\n",
+    "betas = np.linspace(0.6, 0.99, 100)\n",
+    "deltas = np.linspace(0.01, 0.1, 100)\n",
+    "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "for axis, var, values in zip(\n",
+    "    fig.axes, [\"alpha\", \"beta\", \"delta\"], [alphas, betas, deltas]\n",
+    "):\n",
+    "    input_dict = mod.parameters().copy()\n",
+    "    input_dict[var] = values\n",
+    "    axis.plot(\n",
+    "        values,\n",
+    "        f(\n",
+    "            alpha=input_dict[\"alpha\"],\n",
+    "            beta=input_dict[\"beta\"],\n",
+    "            delta=input_dict[\"delta\"],\n",
+    "        ),\n",
+    "    )\n",
+    "    axis.set(xlabel=var)\n",
+    "    if axis == fig.axes[0]:\n",
+    "        axis.set_ylabel(r\"Maximum $\\psi$ allowed\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b506b88c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle 1.29967548484066$"
+      ],
+      "text/plain": [
+       "1.29967548484066"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_zero.subs(mod.parameters().to_sympy())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e0b90547",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
+     ]
+    }
+   ],
+   "source": [
+    "psis = np.linspace(0.1, 1.2, 50)\n",
+    "df = pd.DataFrame({psi: mod.steady_state(psi=psi) for psi in psis}).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1c7e64d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 3, figsize=(14, 4))\n",
+    "for i, var in enumerate([\"K_ss\", \"L_ss\", \"w_ss\"]):\n",
+    "    df.plot.line(y=var, ax=ax[i], legend=False)\n",
+    "    ax[i].axhline(mod_cd.steady_state()[var], ls=\"--\")\n",
+    "    ax[i].set(title=var, xlabel=r\"$\\psi$\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9be4eb38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "irfs = [\n",
+    "    ge.impulse_response_function(\n",
+    "        mod, psi=psi, shock_size={\"epsilon_A\": 0.1}, verbose=False\n",
+    "    )\n",
+    "    for psi in psis\n",
+    "]\n",
+    "irfs = xr.concat(irfs, coords=\"all\", dim=\"psi\").assign_coords(psi=psis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50921fa9",
+   "metadata": {},
+   "source": [
+    "# Effect of $\\psi$ on IRF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "aea6d5ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "norm = Normalize(vmin=psis.min(), vmax=psis.max())\n",
+    "cmap = plt.get_cmap(\"viridis\")\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 5, figsize=(14, 4), layout=\"constrained\")\n",
+    "for axis, variable in zip(fig.axes, [\"Y\", \"C\", \"L\", \"w\", \"r\"]):\n",
+    "    axis.set_prop_cycle(cycler(\"color\", [plt.colormaps[\"viridis\"](i) for i in psis]))\n",
+    "    irfs.sel(variable=variable, shock=\"epsilon_A\").plot.line(\n",
+    "        x=\"time\", hue=\"psi\", add_legend=False, ax=axis\n",
+    "    )\n",
+    "    axis.set_title(variable)\n",
+    "\n",
+    "    if axis == fig.axes[-1]:\n",
+    "        sm = plt.cm.ScalarMappable(cmap=\"viridis\", norm=norm)\n",
+    "        cbar = fig.colorbar(sm, ax=axis)\n",
+    "        cbar.set_label(\"Psi\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25e3b22e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/gEconpy/__init__.py b/gEconpy/__init__.py
index 8cea6ec..79e9ad6 100644
--- a/gEconpy/__init__.py
+++ b/gEconpy/__init__.py
@@ -1,26 +1,51 @@
-from gEconpy import (
-    classes,
-    estimation,
-    parser,
-    plotting,
-    sampling,
-    shared,
-    solvers,
+import logging
+import sys
+
+from gEconpy import classes, numbaf, parser, plotting, solvers, utilities
+from gEconpy._version import get_versions
+from gEconpy.dynare_convert import make_mod_file
+from gEconpy.model.build import model_from_gcn, statespace_from_gcn
+from gEconpy.model.model import (
+    autocorrelation_matrix,
+    autocovariance_matrix,
+    check_bk_condition,
+    impulse_response_function,
+    matrix_to_dataframe,
+    simulate,
+    stationary_covariance_matrix,
+    summarize_perturbation_solution,
 )
-from gEconpy.classes import gEconModel
-from gEconpy.shared import compile_to_statsmodels, make_mod_file
+from gEconpy.model.steady_state import print_steady_state
+
+_log = logging.getLogger(__name__)
+
+if not logging.root.handlers:
+    _log.setLevel(logging.INFO)
+    if len(_log.handlers) == 0:
+        handler = logging.StreamHandler(sys.stderr)
+        _log.addHandler(handler)
+
+
+__version__ = get_versions()["version"]
 
-__version__ = "1.2.1"
 __all__ = [
-    "gEconModel",
+    "model_from_gcn",
+    "statespace_from_gcn",
+    "simulate",
+    "impulse_response_function",
+    "summarize_perturbation_solution",
+    "stationary_covariance_matrix",
+    "autocovariance_matrix",
+    "autocorrelation_matrix",
+    "check_bk_condition",
+    "matrix_to_dataframe",
+    "print_steady_state",
     "classes",
-    "estimation",
     "exceptions",
     "parser",
     "plotting",
-    "sampling",
-    "shared",
+    "utilities",
     "solvers",
     "make_mod_file",
-    "compile_to_statsmodels",
+    "numbaf",
 ]
diff --git a/gEconpy/_version.py b/gEconpy/_version.py
new file mode 100644
index 0000000..32875b6
--- /dev/null
+++ b/gEconpy/_version.py
@@ -0,0 +1,716 @@
+# This file helps to compute a version number in source trees obtained from
+# git-archive tarball (such as those provided by githubs download-from-tag
+# feature). Distribution tarballs (built by setup.py sdist) and build
+# directories (produced by setup.py build) will contain a much shorter file
+# that just contains the computed version number.
+
+# This file is released into the public domain.
+# Generated by versioneer-0.29
+# https://github.com/python-versioneer/python-versioneer
+
+"""Git implementation of _version.py."""
+
+import errno
+import functools
+import os
+import re
+import subprocess
+import sys
+
+from collections.abc import Callable
+from typing import Any
+
+
+def get_keywords() -> dict[str, str]:
+    """Get the keywords needed to look up the version information."""
+    # these strings will be replaced by git during git-archive.
+    # setup.py/versioneer.py will grep for the variable names, so they must
+    # each be defined on a line of their own. _version.py will just call
+    # get_keywords().
+    git_refnames = "$Format:%d$"
+    git_full = "$Format:%H$"
+    git_date = "$Format:%ci$"
+    keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
+    return keywords
+
+
+class VersioneerConfig:
+    """Container for Versioneer configuration parameters."""
+
+    VCS: str
+    style: str
+    tag_prefix: str
+    parentdir_prefix: str
+    versionfile_source: str
+    verbose: bool
+
+
+def get_config() -> VersioneerConfig:
+    """Create, populate and return the VersioneerConfig() object."""
+    # these strings are filled in when 'setup.py versioneer' creates
+    # _version.py
+    cfg = VersioneerConfig()
+    cfg.VCS = "git"
+    cfg.style = "pep440"
+    cfg.tag_prefix = "v"
+    cfg.parentdir_prefix = "None"
+    cfg.versionfile_source = "gEconpy/_version.py"
+    cfg.verbose = False
+    return cfg
+
+
+class NotThisMethod(Exception):
+    """Exception raised if a method is not valid for the current scenario."""
+
+
+LONG_VERSION_PY: dict[str, str] = {}
+HANDLERS: dict[str, dict[str, Callable]] = {}
+
+
+def register_vcs_handler(vcs: str, method: str) -> Callable:  # decorator
+    """Create decorator to mark a method as the handler of a VCS."""
+
+    def decorate(f: Callable) -> Callable:
+        """Store f in HANDLERS[vcs][method]."""
+        if vcs not in HANDLERS:
+            HANDLERS[vcs] = {}
+        HANDLERS[vcs][method] = f
+        return f
+
+    return decorate
+
+
+def run_command(
+    commands: list[str],
+    args: list[str],
+    cwd: str | None = None,
+    verbose: bool = False,
+    hide_stderr: bool = False,
+    env: dict[str, str] | None = None,
+) -> tuple[str | None, int | None]:
+    """Call the given command(s)."""
+    assert isinstance(commands, list)
+    process = None
+
+    popen_kwargs: dict[str, Any] = {}
+    if sys.platform == "win32":
+        # This hides the console window if pythonw.exe is used
+        startupinfo = subprocess.STARTUPINFO()
+        startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW
+        popen_kwargs["startupinfo"] = startupinfo
+
+    for command in commands:
+        try:
+            dispcmd = str([command, *args])
+            # remember shell=False, so use git.cmd on windows, not just git
+            process = subprocess.Popen(
+                [command, *args],
+                cwd=cwd,
+                env=env,
+                stdout=subprocess.PIPE,
+                stderr=(subprocess.PIPE if hide_stderr else None),
+                **popen_kwargs,
+            )
+            break
+        except OSError as e:
+            if e.errno == errno.ENOENT:
+                continue
+            if verbose:
+                print(f"unable to run {dispcmd}")
+                print(e)
+            return None, None
+    else:
+        if verbose:
+            print(f"unable to find command, tried {commands}")
+        return None, None
+    stdout = process.communicate()[0].strip().decode()
+    if process.returncode != 0:
+        if verbose:
+            print(f"unable to run {dispcmd} (error)")
+            print(f"stdout was {stdout}")
+        return None, process.returncode
+    return stdout, process.returncode
+
+
+def versions_from_parentdir(
+    parentdir_prefix: str,
+    root: str,
+    verbose: bool,
+) -> dict[str, Any]:
+    """Try to determine the version from the parent directory name.
+
+    Source tarballs conventionally unpack into a directory that includes both
+    the project name and a version string. We will also support searching up
+    two directory levels for an appropriately named parent directory
+    """
+    rootdirs = []
+
+    for _ in range(3):
+        dirname = os.path.basename(root)
+        if dirname.startswith(parentdir_prefix):
+            return {
+                "version": dirname[len(parentdir_prefix) :],
+                "full-revisionid": None,
+                "dirty": False,
+                "error": None,
+                "date": None,
+            }
+        rootdirs.append(root)
+        root = os.path.dirname(root)  # up a level
+
+    if verbose:
+        print(
+            f"Tried directories {rootdirs!s} but none started with prefix {parentdir_prefix}"
+        )
+    raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
+
+
+@register_vcs_handler("git", "get_keywords")
+def git_get_keywords(versionfile_abs: str) -> dict[str, str]:
+    """Extract version information from the given file."""
+    # the code embedded in _version.py can just fetch the value of these
+    # keywords. When used from setup.py, we don't want to import _version.py,
+    # so we do it with a regexp instead. This function is not used from
+    # _version.py.
+    keywords: dict[str, str] = {}
+    try:
+        with open(versionfile_abs) as fobj:
+            for line in fobj:
+                if line.strip().startswith("git_refnames ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["refnames"] = mo.group(1)
+                if line.strip().startswith("git_full ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["full"] = mo.group(1)
+                if line.strip().startswith("git_date ="):
+                    mo = re.search(r'=\s*"(.*)"', line)
+                    if mo:
+                        keywords["date"] = mo.group(1)
+    except OSError:
+        pass
+    return keywords
+
+
+@register_vcs_handler("git", "keywords")
+def git_versions_from_keywords(
+    keywords: dict[str, str],
+    tag_prefix: str,
+    verbose: bool,
+) -> dict[str, Any]:
+    """Get version information from git keywords."""
+    if "refnames" not in keywords:
+        raise NotThisMethod("Short version file found")
+    date = keywords.get("date")
+    if date is not None:
+        # Use only the last line.  Previous lines may contain GPG signature
+        # information.
+        date = date.splitlines()[-1]
+
+        # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
+        # datestamp. However we prefer "%ci" (which expands to an "ISO-8601
+        # -like" string, which we must then edit to make compliant), because
+        # it's been around since git-1.5.3, and it's too difficult to
+        # discover which version we're using, or to work around using an
+        # older one.
+        date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+    refnames = keywords["refnames"].strip()
+    if refnames.startswith("$Format"):
+        if verbose:
+            print("keywords are unexpanded, not using")
+        raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
+    refs = {r.strip() for r in refnames.strip("()").split(",")}
+    # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
+    # just "foo-1.0". If we see a "tag: " prefix, prefer those.
+    TAG = "tag: "
+    tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}
+    if not tags:
+        # Either we're using git < 1.8.3, or there really are no tags. We use
+        # a heuristic: assume all version tags have a digit. The old git %d
+        # expansion behaves like git log --decorate=short and strips out the
+        # refs/heads/ and refs/tags/ prefixes that would let us distinguish
+        # between branches and tags. By ignoring refnames without digits, we
+        # filter out many common branch names like "release" and
+        # "stabilization", as well as "HEAD" and "master".
+        tags = {r for r in refs if re.search(r"\d", r)}
+        if verbose:
+            print("discarding '{}', no digits".format(",".join(refs - tags)))
+    if verbose:
+        print("likely tags: {}".format(",".join(sorted(tags))))
+    for ref in sorted(tags):
+        # sorting will prefer e.g. "2.0" over "2.0rc1"
+        if ref.startswith(tag_prefix):
+            r = ref[len(tag_prefix) :]
+            # Filter out refs that exactly match prefix or that don't start
+            # with a number once the prefix is stripped (mostly a concern
+            # when prefix is '')
+            if not re.match(r"\d", r):
+                continue
+            if verbose:
+                print(f"picking {r}")
+            return {
+                "version": r,
+                "full-revisionid": keywords["full"].strip(),
+                "dirty": False,
+                "error": None,
+                "date": date,
+            }
+    # no suitable tags, so version is "0+unknown", but full hex is still there
+    if verbose:
+        print("no suitable tags, using unknown + full revision id")
+    return {
+        "version": "0+unknown",
+        "full-revisionid": keywords["full"].strip(),
+        "dirty": False,
+        "error": "no suitable tags",
+        "date": None,
+    }
+
+
+@register_vcs_handler("git", "pieces_from_vcs")
+def git_pieces_from_vcs(
+    tag_prefix: str, root: str, verbose: bool, runner: Callable = run_command
+) -> dict[str, Any]:
+    """Get version from 'git describe' in the root of the source tree.
+
+    This only gets called if the git-archive 'subst' keywords were *not*
+    expanded, and _version.py hasn't already been rewritten with a short
+    version string, meaning we're inside a checked out source tree.
+    """
+    GITS = ["git"]
+    if sys.platform == "win32":
+        GITS = ["git.cmd", "git.exe"]
+
+    # GIT_DIR can interfere with correct operation of Versioneer.
+    # It may be intended to be passed to the Versioneer-versioned project,
+    # but that should not change where we get our version from.
+    env = os.environ.copy()
+    env.pop("GIT_DIR", None)
+    runner = functools.partial(runner, env=env)
+
+    _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=not verbose)
+    if rc != 0:
+        if verbose:
+            print(f"Directory {root} not under git control")
+        raise NotThisMethod("'git rev-parse --git-dir' returned error")
+
+    # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
+    # if there isn't one, this yields HEX[-dirty] (no NUM)
+    describe_out, rc = runner(
+        GITS,
+        [
+            "describe",
+            "--tags",
+            "--dirty",
+            "--always",
+            "--long",
+            "--match",
+            f"{tag_prefix}[[:digit:]]*",
+        ],
+        cwd=root,
+    )
+    # --long was added in git-1.5.5
+    if describe_out is None:
+        raise NotThisMethod("'git describe' failed")
+    describe_out = describe_out.strip()
+    full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
+    if full_out is None:
+        raise NotThisMethod("'git rev-parse' failed")
+    full_out = full_out.strip()
+
+    pieces: dict[str, Any] = {}
+    pieces["long"] = full_out
+    pieces["short"] = full_out[:7]  # maybe improved later
+    pieces["error"] = None
+
+    branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root)
+    # --abbrev-ref was added in git-1.6.3
+    if rc != 0 or branch_name is None:
+        raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
+    branch_name = branch_name.strip()
+
+    if branch_name == "HEAD":
+        # If we aren't exactly on a branch, pick a branch which represents
+        # the current commit. If all else fails, we are on a branchless
+        # commit.
+        branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
+        # --contains was added in git-1.5.4
+        if rc != 0 or branches is None:
+            raise NotThisMethod("'git branch --contains' returned error")
+        branches = branches.split("\n")
+
+        # Remove the first line if we're running detached
+        if "(" in branches[0]:
+            branches.pop(0)
+
+        # Strip off the leading "* " from the list of branches.
+        branches = [branch[2:] for branch in branches]
+        if "master" in branches:
+            branch_name = "master"
+        elif not branches:
+            branch_name = None
+        else:
+            # Pick the first branch that is returned. Good or bad.
+            branch_name = branches[0]
+
+    pieces["branch"] = branch_name
+
+    # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
+    # TAG might have hyphens.
+    git_describe = describe_out
+
+    # look for -dirty suffix
+    dirty = git_describe.endswith("-dirty")
+    pieces["dirty"] = dirty
+    if dirty:
+        git_describe = git_describe[: git_describe.rindex("-dirty")]
+
+    # now we have TAG-NUM-gHEX or HEX
+
+    if "-" in git_describe:
+        # TAG-NUM-gHEX
+        mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe)
+        if not mo:
+            # unparsable. Maybe git-describe is misbehaving?
+            pieces["error"] = f"unable to parse git-describe output: '{describe_out}'"
+            return pieces
+
+        # tag
+        full_tag = mo.group(1)
+        if not full_tag.startswith(tag_prefix):
+            if verbose:
+                fmt = "tag '%s' doesn't start with prefix '%s'"
+                print(fmt % (full_tag, tag_prefix))
+            pieces["error"] = (
+                f"tag '{full_tag}' doesn't start with prefix '{tag_prefix}'"
+            )
+            return pieces
+        pieces["closest-tag"] = full_tag[len(tag_prefix) :]
+
+        # distance: number of commits since tag
+        pieces["distance"] = int(mo.group(2))
+
+        # commit: short hex revision ID
+        pieces["short"] = mo.group(3)
+
+    else:
+        # HEX: no tags
+        pieces["closest-tag"] = None
+        out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root)
+        pieces["distance"] = len(out.split())  # total number of commits
+
+    # commit date: see ISO-8601 comment in git_versions_from_keywords()
+    date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
+    # Use only the last line.  Previous lines may contain GPG signature
+    # information.
+    date = date.splitlines()[-1]
+    pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
+
+    return pieces
+
+
+def plus_or_dot(pieces: dict[str, Any]) -> str:
+    """Return a + if we don't already have one, else return a ."""
+    if "+" in pieces.get("closest-tag", ""):
+        return "."
+    return "+"
+
+
+def render_pep440(pieces: dict[str, Any]) -> str:
+    """Build up version string, with post-release "local version identifier".
+
+    Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
+    get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
+
+    Exceptions:
+    1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_branch(pieces: dict[str, Any]) -> str:
+    """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
+
+    The ".dev0" means not master branch. Note that .dev0 sorts backwards
+    (a feature branch will appear "older" than the master branch).
+
+    Exceptions:
+    1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0"
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+untagged.%d.g%s" % (pieces["distance"], pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def pep440_split_post(ver: str) -> tuple[str, int | None]:
+    """Split pep440 version string at the post-release segment.
+
+    Returns the release segments before the post-release and the
+    post-release version number (or -1 if no post-release segment is present).
+    """
+    vc = str.split(ver, ".post")
+    return vc[0], int(vc[1] or 0) if len(vc) == 2 else None
+
+
+def render_pep440_pre(pieces: dict[str, Any]) -> str:
+    """TAG[.postN.devDISTANCE] -- No -dirty.
+
+    Exceptions:
+    1: no tags. 0.post0.devDISTANCE
+    """
+    if pieces["closest-tag"]:
+        if pieces["distance"]:
+            # update the post release segment
+            tag_version, post_version = pep440_split_post(pieces["closest-tag"])
+            rendered = tag_version
+            if post_version is not None:
+                rendered += ".post%d.dev%d" % (post_version + 1, pieces["distance"])
+            else:
+                rendered += ".post0.dev%d" % (pieces["distance"])
+        else:
+            # no commits, use the tag as the version
+            rendered = pieces["closest-tag"]
+    else:
+        # exception #1
+        rendered = "0.post0.dev%d" % pieces["distance"]
+    return rendered
+
+
+def render_pep440_post(pieces: dict[str, Any]) -> str:
+    """TAG[.postDISTANCE[.dev0]+gHEX] .
+
+    The ".dev0" means dirty. Note that .dev0 sorts backwards
+    (a dirty tree will appear "older" than the corresponding clean one),
+    but you shouldn't be releasing software with -dirty anyways.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g{}".format(pieces["short"])
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+        rendered += "+g{}".format(pieces["short"])
+    return rendered
+
+
+def render_pep440_post_branch(pieces: dict[str, Any]) -> str:
+    """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
+
+    The ".dev0" means not master branch.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["branch"] != "master":
+                rendered += ".dev0"
+            rendered += plus_or_dot(pieces)
+            rendered += "g{}".format(pieces["short"])
+            if pieces["dirty"]:
+                rendered += ".dirty"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["branch"] != "master":
+            rendered += ".dev0"
+        rendered += "+g{}".format(pieces["short"])
+        if pieces["dirty"]:
+            rendered += ".dirty"
+    return rendered
+
+
+def render_pep440_old(pieces: dict[str, Any]) -> str:
+    """TAG[.postDISTANCE[.dev0]] .
+
+    The ".dev0" means dirty.
+
+    Exceptions:
+    1: no tags. 0.postDISTANCE[.dev0]
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"] or pieces["dirty"]:
+            rendered += ".post%d" % pieces["distance"]
+            if pieces["dirty"]:
+                rendered += ".dev0"
+    else:
+        # exception #1
+        rendered = "0.post%d" % pieces["distance"]
+        if pieces["dirty"]:
+            rendered += ".dev0"
+    return rendered
+
+
+def render_git_describe(pieces: dict[str, Any]) -> str:
+    """TAG[-DISTANCE-gHEX][-dirty].
+
+    Like 'git describe --tags --dirty --always'.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        if pieces["distance"]:
+            rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render_git_describe_long(pieces: dict[str, Any]) -> str:
+    """TAG-DISTANCE-gHEX[-dirty].
+
+    Like 'git describe --tags --dirty --always -long'.
+    The distance/hash is unconditional.
+
+    Exceptions:
+    1: no tags. HEX[-dirty]  (note: no 'g' prefix)
+    """
+    if pieces["closest-tag"]:
+        rendered = pieces["closest-tag"]
+        rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
+    else:
+        # exception #1
+        rendered = pieces["short"]
+    if pieces["dirty"]:
+        rendered += "-dirty"
+    return rendered
+
+
+def render(pieces: dict[str, Any], style: str) -> dict[str, Any]:
+    """Render the given version pieces into the requested style."""
+    if pieces["error"]:
+        return {
+            "version": "unknown",
+            "full-revisionid": pieces.get("long"),
+            "dirty": None,
+            "error": pieces["error"],
+            "date": None,
+        }
+
+    if not style or style == "default":
+        style = "pep440"  # the default
+
+    if style == "pep440":
+        rendered = render_pep440(pieces)
+    elif style == "pep440-branch":
+        rendered = render_pep440_branch(pieces)
+    elif style == "pep440-pre":
+        rendered = render_pep440_pre(pieces)
+    elif style == "pep440-post":
+        rendered = render_pep440_post(pieces)
+    elif style == "pep440-post-branch":
+        rendered = render_pep440_post_branch(pieces)
+    elif style == "pep440-old":
+        rendered = render_pep440_old(pieces)
+    elif style == "git-describe":
+        rendered = render_git_describe(pieces)
+    elif style == "git-describe-long":
+        rendered = render_git_describe_long(pieces)
+    else:
+        raise ValueError(f"unknown style '{style}'")
+
+    return {
+        "version": rendered,
+        "full-revisionid": pieces["long"],
+        "dirty": pieces["dirty"],
+        "error": None,
+        "date": pieces.get("date"),
+    }
+
+
+def get_versions() -> dict[str, Any]:
+    """Get version information or return default if unable to do so."""
+    # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
+    # __file__, we can work backwards from there to the root. Some
+    # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
+    # case we can only use expanded keywords.
+
+    cfg = get_config()
+    verbose = cfg.verbose
+
+    try:
+        return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose)
+    except NotThisMethod:
+        pass
+
+    try:
+        root = os.path.realpath(__file__)
+        # versionfile_source is the relative path from the top of the source
+        # tree (where the .git directory might live) to this file. Invert
+        # this to find the root from __file__.
+        for _ in cfg.versionfile_source.split("/"):
+            root = os.path.dirname(root)
+    except NameError:
+        return {
+            "version": "0+unknown",
+            "full-revisionid": None,
+            "dirty": None,
+            "error": "unable to find root of source tree",
+            "date": None,
+        }
+
+    try:
+        pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
+        return render(pieces, cfg.style)
+    except NotThisMethod:
+        pass
+
+    try:
+        if cfg.parentdir_prefix:
+            return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
+    except NotThisMethod:
+        pass
+
+    return {
+        "version": "0+unknown",
+        "full-revisionid": None,
+        "dirty": None,
+        "error": "unable to compute version",
+        "date": None,
+    }
diff --git a/gEconpy/classes/__init__.py b/gEconpy/classes/__init__.py
index f644944..d692150 100644
--- a/gEconpy/classes/__init__.py
+++ b/gEconpy/classes/__init__.py
@@ -1,3 +1,3 @@
-from gEconpy.classes.model import gEconModel
-
-__all__ = ["gEconModel"]
+# from gEconpy.model.model import gEconModel
+#
+# __all__ = ["gEconModel"]
diff --git a/gEconpy/classes/containers.py b/gEconpy/classes/containers.py
index 3890f07..edad855 100644
--- a/gEconpy/classes/containers.py
+++ b/gEconpy/classes/containers.py
@@ -1,7 +1,8 @@
 from collections import defaultdict
-from typing import Any
+from typing import Any, cast
 
 import sympy as sp
+
 from sympy.polys.domains.mpelements import ComplexElement
 
 from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
@@ -23,7 +24,7 @@ def safe_string_to_sympy(s, assumptions=None):
 
     name = "_".join(name)
     time_index = SAFE_STRING_TO_INDEX_DICT[time_index_str]
-    symbol = TimeAwareSymbol(name, time_index, **assumptions[name])
+    symbol = TimeAwareSymbol(name, time_index, **assumptions.get(name, {}))
 
     return symbol
 
@@ -35,16 +36,21 @@ def symbol_to_string(symbol: str | sp.Symbol):
         return symbol.safe_name if isinstance(symbol, TimeAwareSymbol) else symbol.name
 
 
-def string_keys_to_sympy(d, assumptions=None):
+def string_keys_to_sympy(d, assumptions=None, is_variable=None):
+    def has_time_suffix(s):
+        suffixes = ["_t", "_tp1", "_tm1", "_ss"]
+        return any(s.endswith(suffix) for suffix in suffixes)
+
     result = {}
-    assumptions = assumptions or defaultdict(dict)
+    assumptions = assumptions if assumptions is not None else defaultdict(dict)
+    is_variable = is_variable if is_variable is not None else defaultdict(bool)
 
     for key, value in d.items():
         if isinstance(key, sp.Symbol):
             result[key] = value
             continue
 
-        if "_" not in key:
+        if not is_variable.get(key, True) or not has_time_suffix(key):
             result[sp.Symbol(key, **assumptions.get(key, {}))] = value
             continue
 
@@ -73,7 +79,7 @@ def sympy_number_values_to_floats(d: dict[sp.Symbol, Any]):
 
 def float_values_to_sympy_float(d: dict[sp.Symbol, Any]):
     for var, value in d.items():
-        if isinstance(value, (float, int)):
+        if isinstance(value, float | int):
             d[var] = sp.Float(value)
         elif isinstance(value, complex):
             d[var] = sp.CC(value)
@@ -108,9 +114,10 @@ def __init__(self, *args, **kwargs):
 
         self.is_sympy: bool = False
         self._assumptions: dict = {}
+        self._is_variable: dict = {}
 
         keys = list(self.keys())
-        if any([not isinstance(x, (sp.Symbol, str)) for x in keys]):
+        if any([not isinstance(x, sp.Symbol | str) for x in keys]):
             raise KeyError("All keys should be either string or Sympy symbols")
 
         if len(keys) > 0:
@@ -122,23 +129,26 @@ def __init__(self, *args, **kwargs):
                 raise KeyError("Cannot mix sympy and string keys")
 
         self._save_assumptions(keys)
+        self._save_is_variable(keys)
 
-    def __or__(self, other):
+    def __or__(self, other: dict):
         if not isinstance(other, dict):
             raise ValueError("__or__ not defined on non-dictionary objects")
         if not isinstance(other, SymbolDictionary):
             other = SymbolDictionary(other)
 
-        d_copy = self.copy()
+        d_copy = cast(SymbolDictionary, self.copy())
 
         # If one dict or the other is empty, only merge assumptions
         if len(d_copy.keys()) == 0:
-            other_copy = other.copy()
+            other_copy = cast(SymbolDictionary, other.copy())
             other_copy._assumptions.update(self._assumptions)
+            other_copy._is_variable.update(self._is_variable)
             return other_copy
 
         if len(other.keys()) == 0:
             d_copy._assumptions.update(other._assumptions)
+            d_copy._is_variable.update(self._is_variable)
             return d_copy
 
         # If both are populated but of different types, raise an error
@@ -149,16 +159,19 @@ def __or__(self, other):
 
         # Full merge
         other_assumptions = getattr(other, "_assumptions", {})
+        other_is_variable = getattr(other, "_is_variable", {})
 
         d_copy.update(other)
         d_copy._assumptions.update(other_assumptions)
+        d_copy._is_variable.update(other_is_variable)
 
         return d_copy
 
-    def copy(self):
+    def copy(self) -> "SymbolDictionary":
         new_d = SymbolDictionary(super().copy())
         new_d.is_sympy = self.is_sympy
         new_d._assumptions = self._assumptions
+        new_d._is_variable = self._is_variable
 
         return new_d
 
@@ -173,6 +186,17 @@ def _save_assumptions(self, keys):
             else:
                 self._assumptions[key.name] = key.assumptions0
 
+    def _save_is_variable(self, keys):
+        if not self.is_sympy:
+            return
+        if not isinstance(keys, list):
+            keys = [keys]
+        for key in keys:
+            if isinstance(key, TimeAwareSymbol):
+                self._is_variable[key.base_name] = True
+            else:
+                self._is_variable[key.name] = False
+
     def __setitem__(self, key, value):
         if len(self.keys()) == 0:
             self.is_sympy = isinstance(key, sp.Symbol)
@@ -180,24 +204,32 @@ def __setitem__(self, key, value):
             raise KeyError("Cannot add string key to dictionary in sympy mode")
         elif not self.is_sympy and isinstance(key, sp.Symbol):
             raise KeyError("Cannot add sympy key to dictionary in string mode")
+
         super().__setitem__(key, value)
         self._save_assumptions(key)
+        self._save_is_variable(key)
 
     def _clean_update(self, d):
         self.clear()
         self._assumptions.clear()
+        self._is_variable.clear()
 
         self.update(d)
         self._assumptions.update(d._assumptions)
+        self._is_variable.update(d._is_variable)
         self.is_sympy = d.is_sympy
 
-    def to_sympy(self, inplace=False, new_assumptions=None):
-        new_assumptions = new_assumptions or {}
+    def to_sympy(self, inplace=False, new_assumptions=None, new_is_variable=None):
+        new_assumptions = new_assumptions if new_assumptions is not None else {}
+        new_is_variable = new_is_variable if new_is_variable is not None else {}
 
         assumptions = self._assumptions.copy()
+        is_variable = self._is_variable.copy()
+
         assumptions.update(new_assumptions)
+        is_variable.update(new_is_variable)
 
-        d = SymbolDictionary(string_keys_to_sympy(self, assumptions))
+        d = SymbolDictionary(string_keys_to_sympy(self, assumptions, is_variable))
 
         if inplace:
             self._clean_update(d)
@@ -255,6 +287,7 @@ def to_string(self, inplace=False):
         copy_dict = self.copy()
         d = SymbolDictionary(sympy_keys_to_strings(copy_dict))
         d._assumptions = copy_dict._assumptions.copy()
+        d._is_variable = copy_dict._is_variable.copy()
 
         if inplace:
             self._clean_update(d)
@@ -266,6 +299,7 @@ def sort_keys(self, inplace=False):
         is_sympy = self.is_sympy
         d = SymbolDictionary(sort_dictionary(self.copy().to_string()))
         d._assumptions = self._assumptions.copy()
+        d._is_variable = self._is_variable.copy()
 
         if is_sympy:
             d = d.to_sympy()
@@ -280,6 +314,7 @@ def values_to_float(self, inplace=False):
         d = self.copy()
         d = sympy_number_values_to_floats(d)
         d._assumptions = self._assumptions.copy()
+        d._is_variable = self._is_variable.copy()
 
         if inplace:
             self._clean_update(d)
@@ -291,9 +326,16 @@ def float_to_values(self, inplace=False):
         d = self.copy()
         d = float_values_to_sympy_float(d)
         d._assumptions = self._assumptions.copy()
+        d._is_variable = self._is_variable.copy()
 
         if inplace:
             self._clean_update(d)
             return
 
         return d
+
+
+class SteadyStateResults(SymbolDictionary):
+    def __init__(self, *args, **kwargs):
+        self.success = False
+        super().__init__(*args, **kwargs)
diff --git a/gEconpy/classes/model.py b/gEconpy/classes/model.py
deleted file mode 100644
index 54eccaf..0000000
--- a/gEconpy/classes/model.py
+++ /dev/null
@@ -1,1791 +0,0 @@
-from collections import defaultdict
-from functools import reduce
-from typing import Any, Union
-from collections.abc import Callable
-from warnings import catch_warnings, simplefilter, warn
-
-import arviz as az
-import emcee
-import numpy as np
-import pandas as pd
-import sympy as sp
-import xarray as xr
-from numpy.typing import ArrayLike
-from scipy import linalg, stats
-
-from gEconpy.classes.block import Block
-from gEconpy.classes.containers import SymbolDictionary
-from gEconpy.classes.progress_bar import ProgressBar
-from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.estimation.estimate import build_Z_matrix, evaluate_logp2
-from gEconpy.estimation.estimation_utilities import extract_prior_dict
-from gEconpy.exceptions.exceptions import (
-    GensysFailedException,
-    MultipleSteadyStateBlocksException,
-    PerturbationSolutionNotFoundException,
-    SteadyStateNotSolvedError,
-    VariableNotFoundException,
-)
-from gEconpy.numba_tools.utilities import numba_lambdify
-from gEconpy.parser import file_loaders, gEcon_parser
-from gEconpy.parser.constants import STEADY_STATE_NAMES
-from gEconpy.parser.parse_distributions import create_prior_distribution_dictionary
-from gEconpy.parser.parse_equations import single_symbol_to_sympy
-from gEconpy.shared.utilities import (
-    build_Q_matrix,
-    compute_autocorrelation_matrix,
-    expand_subs_for_all_times,
-    get_shock_std_priors_from_hyperpriors,
-    is_variable,
-    make_all_var_time_combos,
-    split_random_variables,
-    substitute_all_equations,
-    unpack_keys_and_values,
-)
-from gEconpy.solvers.gensys import interpret_gensys_output
-from gEconpy.solvers.perturbation import PerturbationSolver
-from gEconpy.solvers.steady_state import SteadyStateSolver
-
-VariableType = Union[sp.Symbol, TimeAwareSymbol]
-
-
-class gEconModel:
-    def __init__(
-        self,
-        model_filepath: str,
-        verbose: bool = True,
-        simplify_blocks=True,
-        simplify_constants=True,
-        simplify_tryreduce=True,
-    ) -> None:
-        """
-        Initialize a DSGE model object from a GCN file.
-
-        Parameters
-        ----------
-        model_filepath : str
-            Filepath to the GCN file
-        verbose : bool, optional
-            Flag for verbose output, by default True
-        simplify_blocks : bool, optional
-            Flag to simplify blocks, by default True
-        simplify_constants : bool, optional
-            Flag to simplify constants, by default True
-        simplify_tryreduce : bool, optional
-            Flag to simplify using `try_reduce_vars`, by default True
-        """
-        self.model_filepath: str = model_filepath
-
-        # Model metadata
-        self.options: dict[str, bool] = {}
-        self.try_reduce_vars: list[TimeAwareSymbol] = []
-
-        self.blocks: dict[str, Block] = {}
-        self.n_blocks: int = 0
-
-        # Model components
-        self.variables: list[TimeAwareSymbol] = []
-        self.assumptions: dict[str, dict] = defaultdict(SymbolDictionary)
-        self.shocks: list[TimeAwareSymbol] = []
-        self.system_equations: list[sp.Add] = []
-        self.calibrating_equations: list[sp.Add] = []
-        self.params_to_calibrate: list[sp.Symbol] = []
-
-        self.deterministic_relationships: list[sp.Add] = []
-        self.deterministic_params: list[sp.Symbol] = []
-
-        self.free_param_dict: SymbolDictionary[sp.Symbol, float] = SymbolDictionary()
-        self.calib_param_dict: SymbolDictionary[sp.Symbol, float] = SymbolDictionary()
-        self.det_param_dict: SymbolDictionary[sp.Symbol, float] = SymbolDictionary()
-        self.steady_state_relationships: SymbolDictionary[VariableType, sp.Add] = (
-            SymbolDictionary()
-        )
-
-        self.param_priors: SymbolDictionary[str, Any] = SymbolDictionary()
-        self.shock_priors: SymbolDictionary[str, Any] = SymbolDictionary()
-        self.hyper_priors: SymbolDictionary[str, Any] = SymbolDictionary()
-        self.observation_noise_priors: SymbolDictionary[str, Any] = SymbolDictionary()
-
-        self.n_variables: int = 0
-        self.n_shocks: int = 0
-        self.n_equations: int = 0
-        self.n_calibrating_equations: int = 0
-
-        # Functional representations of the model
-        self.f_ss: Callable | None = None
-        self.f_ss_resid: Callable | None = None
-
-        # Steady state information
-        self.steady_state_solved: bool = False
-        self.steady_state_system: list[sp.Add] = []
-        self.steady_state_dict: SymbolDictionary[sp.Symbol, float] = SymbolDictionary()
-        self.residuals: list[float] = []
-
-        # Functional representation of the perturbation system
-        self.build_perturbation_matrices: Callable | None = None
-
-        # Perturbation solution information
-        self.perturbation_solved: bool = False
-        self.T: pd.DataFrame = None
-        self.R: pd.DataFrame = None
-        self.P: pd.DataFrame = None
-        self.Q: pd.DataFrame = None
-        self.R: pd.DataFrame = None
-        self.S: pd.DataFrame = None
-
-        self.build(
-            verbose=verbose,
-            simplify_blocks=simplify_blocks,
-            simplify_constants=simplify_constants,
-            simplify_tryreduce=simplify_tryreduce,
-        )
-
-        # Assign Solvers
-        self.steady_state_solver = SteadyStateSolver(self)
-        self.perturbation_solver = PerturbationSolver(self)
-
-        # TODO: Here I copy the assumptions from the model (which should be the only source of truth for assumptions)
-        #  into every SymbolDictionary. This setup is really bad; if these dictionaries go out of sync there could be
-        #  disagreements about what the assumptions for a variable should be.
-
-        for d in [
-            self.free_param_dict,
-            self.calib_param_dict,
-            self.steady_state_relationships,
-            self.param_priors,
-            self.shock_priors,
-            self.observation_noise_priors,
-        ]:
-            d._assumptions.update(self.assumptions)
-
-    def build(
-        self,
-        verbose: bool,
-        simplify_blocks: bool,
-        simplify_constants: bool,
-        simplify_tryreduce: bool,
-    ) -> None:
-        """
-        Main parsing function for the model. Build loads the GCN file, decomposes it into blocks, solves optimization
-        problems contained in each block, then extracts parameters, equations, calibrating equations, calibrated
-        parameters, and exogenous shocks into their respective class attributes.
-
-        Priors declared in the GCN file are converted into scipy distribution objects and stored in two dictionaries:
-        self.param_priors and self.shock_priors.
-
-        Gathering block information is done for convenience. For diagnostic purposes the block structure is retained
-        as well.
-
-        Parameters
-        ----------
-        verbose : bool, optional
-            When True, print a build report describing the model structure and warning the user if the number of
-            variables does not match the number of equations.
-        simplify_blocks : bool, optional
-            If True, simplify equations in the model blocks.
-        simplify_constants : bool, optional
-            If True, simplify constants in the model equations.
-        simplify_tryreduce : bool, optional
-            If True, try to reduce the number of variables in the model by eliminating unnecessary equations.
-
-        Returns
-        -------
-        None
-        """
-
-        raw_model = file_loaders.load_gcn(self.model_filepath)
-        parsed_model, prior_dict = gEcon_parser.preprocess_gcn(raw_model)
-
-        self._build_model_blocks(parsed_model, simplify_blocks)
-        self._get_all_block_equations()
-        self._get_all_block_parameters()
-        self._get_all_block_params_to_calibrate()
-        self._get_all_block_deterministic_parameters()
-        self._get_variables_and_shocks()
-        self._build_prior_dict(prior_dict)
-
-        self._make_deterministic_substitutions()
-        self._verify_no_orphan_params()
-
-        reduced_vars = None
-        singletons = None
-
-        if simplify_tryreduce:
-            reduced_vars = self._try_reduce()
-        if simplify_constants:
-            singletons = self._simplify_singletons()
-
-        self.build_report(reduced_vars, singletons, verbose=verbose)
-
-    def build_report(
-        self, reduced_vars: list[str], singletons: list[str], verbose: bool = True
-    ) -> None:
-        """
-        Write a diagnostic message after building the model. Note that successfully building the model does not
-        guarantee that the model is correctly specified. For example, it is possible to build a model with more
-        equations than parameters. This message will warn the user in this case.
-
-        Parameters
-        ----------
-        reduced_vars: list
-            A list of variables reduced by the `try_reduce` method. Used to print the names of eliminated variables.
-
-        singletons: list
-            A list of "singleton" variables -- those defined as time-invariant constants. Used ot print the sames of
-            eliminated variables.
-
-        verbose: bool
-            Flag to print the build report to the terminal. Default is True. Regardless of the flag, the function will
-            always issue a warning to the user if the system is not fully defined.
-        Returns
-        -------
-        None
-        """
-
-        if singletons and len(singletons) == 0:
-            singletons = None
-
-        eq_str = "equation" if self.n_equations == 1 else "equations"
-        var_str = "variable" if self.n_variables == 1 else "variables"
-        shock_str = "shock" if self.n_shocks == 1 else "shocks"
-        cal_eq_str = "equation" if self.n_calibrating_equations == 1 else "equations"
-        free_par_str = "parameter" if len(self.free_param_dict) == 1 else "parameters"
-        calib_par_str = "parameter" if self.n_params_to_calibrate == 1 else "parameters"
-
-        n_params = len(self.free_param_dict) + len(self.calib_param_dict)
-
-        param_priors = self.param_priors.keys()
-        shock_priors = self.shock_priors.keys()
-
-        report = "Model Building Complete.\nFound:\n"
-        report += f"\t{self.n_equations} {eq_str}\n"
-        report += f"\t{self.n_variables} {var_str}\n"
-
-        if reduced_vars:
-            report += "\tThe following variables were eliminated at user request:\n"
-            report += "\t\t" + ",".join(reduced_vars) + "\n"
-
-        if singletons:
-            report += '\tThe following "variables" were defined as constants and have been substituted away:\n'
-            report += "\t\t" + ",".join(singletons) + "\n"
-
-        report += f"\t{self.n_shocks} stochastic {shock_str}\n"
-        report += (
-            f'\t\t {len(shock_priors)} / {self.n_shocks} {"have" if len(shock_priors) == 1 else "has"}'
-            f" a defined prior. \n"
-        )
-
-        report += f"\t{n_params} {free_par_str}\n"
-        report += (
-            f'\t\t {len(param_priors)} / {n_params} {"have" if len(param_priors) == 1 else "has"} '
-            f"a defined prior. \n"
-        )
-        report += f"\t{self.n_calibrating_equations} calibrating {cal_eq_str}\n"
-        report += f"\t{self.n_params_to_calibrate} {calib_par_str} to calibrate\n "
-
-        if self.n_equations == self.n_variables:
-            report += "Model appears well defined and ready to proceed to solving.\n"
-        else:
-            message = (
-                f"The model does not appear correctly specified, there are {self.n_equations} {eq_str} but "
-                f"{self.n_variables} {var_str}. It will not be possible to solve this model. Please check the "
-                f"specification using available diagnostic tools, and check the GCN file for typos."
-            )
-            warn(message)
-
-        if verbose:
-            print(report)
-
-    def steady_state(
-        self,
-        verbose: bool | None = True,
-        model_is_linear: bool | None = False,
-        apply_user_simplifications=True,
-        method: str | None = "root",
-        optimizer_kwargs: dict[str, Any] | None = None,
-        use_jac: bool | None = True,
-        use_hess: bool | None = True,
-        tol: float | None = 1e-6,
-    ) -> None:
-        """
-        Solves for a function f(params) that computes steady state values and calibrated parameter values given
-        parameter values, stores results, and verifies that the residuals of the solution are zero.
-
-        Parameters
-        ----------
-        verbose: bool
-            Flag controlling whether to print results of the steady state solver. Default is True.
-        model_is_linear: bool, optional
-            If True, the model is assumed to have been linearized by the user. A specialized solving routine is used
-            to find the steady state, which is likely all zeros. If True, all other arguments to this function
-            have no effect (except verbose). Default is False.
-        apply_user_simplifications: bool
-            Whether to simplify system equations using the user-defined steady state relationships defined in the GCN
-            before passing the system to the numerical solver. Default is True.
-        method: str
-            One of "root" or "minimize". Indicates which family of solution algorithms should be used to find a
-            numerical steady state: direct root finding or minimization of squared error. Not that "root" is not
-            suitable if the number of inputs is not equal to the number of outputs, for example if user-provided
-            steady state relationships do not result in elimination of model equations. Default is "root".
-        optimizer_kwargs: dict
-            Dictionary of arguments to be passed to scipy.optimize.root or scipy.optimize.minimize, see those
-            functions for more details.
-        use_jac: bool
-            Whether to symbolically compute the Jacobian matrix of the steady state system (when method is "root") or
-            the Jacobian vector of the loss function (when method is "minimize"). Strongly recommended. Default is True
-        use_hess: bool
-            Whether to symbolically compute the Hessian matrix of the loss function. Ignored if method is "root".
-            If "False", the default BFGS solver will compute a numerical approximation, so not necessarily required.
-            Still recommended. Default is True.
-        tol: float
-            Numerical tolerance for declaring a steady-state solution valid. Default is 1e-6. Note that this only used
-            by the gEconpy model to decide if a steady state has been found, and is **NOT** passed to the scipy
-            solution algorithms. To adjust solution tolerance for these algorithms, use optimizer_kwargs.
-
-        Returns
-        -------
-        None
-        """
-
-        if self.options.get("linear", False):
-            model_is_linear = True
-
-        if not self.steady_state_solved:
-            self.f_ss = self.steady_state_solver.solve_steady_state(
-                apply_user_simplifications=apply_user_simplifications,
-                model_is_linear=model_is_linear,
-                method=method,
-                optimizer_kwargs=optimizer_kwargs,
-                use_jac=use_jac,
-                use_hess=use_hess,
-            )
-
-        self._process_steady_state_results(verbose, tol=tol)
-
-    def _process_steady_state_results(self, verbose=True, tol=1e-6) -> None:
-        """Process results from steady state solver.
-
-        This function sets the steady state dictionary, calibrated parameter dictionary, and residuals attribute
-        based on the results of the steady state solver. It also sets the `steady_state_solved` attribute to
-        indicate whether the steady state was successfully found. If `verbose` is True, it prints a message
-        indicating whether the steady state was found and the sum of squared residuals.
-
-        Parameters
-        ----------
-        verbose : bool, optional
-            If True, print a message indicating whether the steady state was found and the sum of squared residuals.
-            Default is True.
-        tol: float, optional
-            Numerical tolerance for declaring a steady-state solution has been found. Default is 1e-6.
-
-        Returns
-        -------
-        None
-        """
-        results = self.f_ss(self.free_param_dict)
-        self.steady_state_dict = results["ss_dict"]
-        self.calib_param_dict = results["calib_dict"]
-        self.residuals = results["resids"]
-
-        self.steady_state_system = self.steady_state_solver.steady_state_system
-        self.steady_state_solved = (
-            np.allclose(self.residuals, 0, atol=tol) & results["success"]
-        )
-
-        if verbose:
-            if self.steady_state_solved:
-                print(
-                    f"Steady state found! Sum of squared residuals is {(self.residuals ** 2).sum()}"
-                )
-            else:
-                print(
-                    f"Steady state NOT found. Sum of squared residuals is {(self.residuals ** 2).sum()}"
-                )
-
-    def print_steady_state(self):
-        """
-        Prints the steady state values for the model's variables and calibrated parameters.
-
-        Prints an error message if a valid steady state has not yet been found.
-        """
-        if len(self.steady_state_dict) == 0:
-            print(
-                "Run the steady_state method to find a steady state before calling this method."
-            )
-            return
-
-        output = []
-        if not self.steady_state_solved:
-            output.append(
-                "Values come from the latest solver iteration but are NOT a valid steady state."
-            )
-
-        max_var_name = (
-            max(
-                len(x)
-                for x in list(self.steady_state_dict.keys())
-                + list(self.calib_param_dict.keys())
-            )
-            + 5
-        )
-
-        for key, value in self.steady_state_dict.items():
-            output.append(f"{key:{max_var_name}}{value:>10.3f}")
-
-        if len(self.params_to_calibrate) > 0:
-            output.append("\n")
-            output.append(
-                "In addition, the following parameter values were calibrated:"
-            )
-            for key, value in self.calib_param_dict.items():
-                output.append(f"{key:{max_var_name}}{value:>10.3f}")
-
-        print("\n".join(output))
-
-    def solve_model(
-        self,
-        solver="cycle_reduction",
-        not_loglin_variable: list[str] | None = None,
-        order: int = 1,
-        model_is_linear: bool = False,
-        tol: float = 1e-8,
-        max_iter: int = 1000,
-        verbose: bool = True,
-        on_failure="error",
-    ) -> None:
-        """
-        Solve for the linear approximation to the policy function via perturbation. Adapted from R code in the gEcon
-        package by Grzegorz Klima, Karol Podemski, and Kaja Retkiewicz-Wijtiwiak., http://gecon.r-forge.r-project.org/.
-
-        Parameters
-        ----------
-        solver: str, default: 'cycle_reduction'
-            Name of the algorithm to solve the linear solution. Currently "cycle_reduction" and "gensys" are supported.
-            Following Dynare, cycle_reduction is the default, but note that gEcon uses gensys.
-        not_loglin_variable: List, default: None
-            Variables to not log linearize when solving the model. Variables with steady state values close to zero
-            will be automatically selected to not log linearize.
-        order: int, default: 1
-            Order of taylor expansion to use to solve the model. Currently only 1st order approximation is supported.
-        model_is_linear: bool, default: False
-            Flag indicating whether a model has already been linearized by the user.
-        tol: float, default 1e-8
-            Desired level of floating point accuracy in the solution
-        max_iter: int, default: 1000
-            Maximum number of cycle_reduction iterations. Not used if solver is 'gensys'.
-        verbose: bool, default: True
-            Flag indicating whether to print solver results to the terminal
-        on_failure: str, one of ['error', 'ignore'], default: 'error'
-            Instructions on what to do if the algorithm to find a linearized policy matrix. "Error" will raise an error,
-            while "ignore" will return None. "ignore" is useful when repeatedly solving the model, e.g. when sampling.
-
-        Returns
-        -------
-        None
-        """
-
-        if on_failure not in ["error", "ignore"]:
-            raise ValueError(
-                f'Parameter on_failure must be one of "error" or "ignore", found {on_failure}'
-            )
-
-        if self.options.get("linear", False):
-            model_is_linear = True
-
-        param_dict = self.free_param_dict | self.calib_param_dict
-        steady_state_dict = self.steady_state_dict
-
-        if self.build_perturbation_matrices is None:
-            self._perturbation_setup(
-                not_loglin_variable, order, model_is_linear, verbose, bool
-            )
-
-        A, B, C, D = self.build_perturbation_matrices(
-            np.array(list(param_dict.values())),
-            np.array(list(steady_state_dict.values())),
-        )
-        _, variables, _ = self.perturbation_solver.make_all_variable_time_combinations()
-
-        if solver == "gensys":
-            gensys_results = self.perturbation_solver.solve_policy_function_with_gensys(
-                A, B, C, D, tol, verbose
-            )
-            G_1, constant, impact, f_mat, f_wt, y_wt, gev, eu, loose = gensys_results
-
-            success = all([x == 1 for x in eu[:2]])
-
-            if not success:
-                if on_failure == "error":
-                    raise GensysFailedException(eu)
-                elif on_failure == "ignore":
-                    if verbose:
-                        message = interpret_gensys_output(eu)
-                        print(message)
-                    self.P = None
-                    self.Q = None
-                    self.R = None
-                    self.S = None
-
-                    self.perturbation_solved = False
-
-                    return
-
-            if verbose:
-                message = interpret_gensys_output(eu)
-                print(message)
-                print(
-                    "Policy matrices have been stored in attributes model.P, model.Q, model.R, and model.S"
-                )
-
-            T = G_1[: self.n_variables, :][:, : self.n_variables]
-            R = impact[: self.n_variables, :]
-
-        elif solver == "cycle_reduction":
-            (
-                T,
-                R,
-                result,
-                log_norm,
-            ) = self.perturbation_solver.solve_policy_function_with_cycle_reduction(
-                A, B, C, D, max_iter, tol, verbose
-            )
-            if T is None:
-                if on_failure == "error":
-                    raise GensysFailedException(result)
-        else:
-            raise NotImplementedError(
-                'Only "cycle_reduction" and "gensys" are valid values for solver'
-            )
-
-        gEcon_matrices = self.perturbation_solver.statespace_to_gEcon_representation(
-            A, T, R, variables, tol
-        )
-        P, Q, _, _, A_prime, R_prime, S_prime = gEcon_matrices
-
-        resid_norms = self.perturbation_solver.residual_norms(
-            B, C, D, Q, P, A_prime, R_prime, S_prime
-        )
-        norm_deterministic, norm_stochastic = resid_norms
-
-        if verbose:
-            print(f"Norm of deterministic part: {norm_deterministic:0.9f}")
-            print(f"Norm of stochastic part:    {norm_deterministic:0.9f}")
-
-        self.T = pd.DataFrame(
-            T,
-            index=[
-                x.base_name for x in sorted(self.variables, key=lambda x: x.base_name)
-            ],
-            columns=[
-                x.base_name for x in sorted(self.variables, key=lambda x: x.base_name)
-            ],
-        )
-        self.R = pd.DataFrame(
-            R,
-            index=[
-                x.base_name for x in sorted(self.variables, key=lambda x: x.base_name)
-            ],
-            columns=[
-                x.base_name for x in sorted(self.shocks, key=lambda x: x.base_name)
-            ],
-        )
-
-        self.perturbation_solved = True
-
-    def _perturbation_setup(
-        self,
-        not_loglin_variables=None,
-        order=1,
-        model_is_linear=False,
-        verbose=True,
-        return_F_matrices=False,
-        tol=1e-8,
-    ):
-        """
-        This function is used to set up the perturbation matrices needed to simulate the model. It linearizes the model
-        around the steady state and constructs matrices A, B, C, and D needed to solve the system.
-
-        Parameters
-        ----------
-        not_loglin_variables: list of str
-            List of variables that should not be log-linearized. This is useful when a variable has a zero or negative
-             steady state value and cannot be log-linearized.
-        order: int
-            The order of the approximation. Currently only order 1 is implemented.
-        model_is_linear: bool
-            If True, assumes that the model is already linearized in the GCN file and directly
-             returns the matrices A, B, C, D.
-        verbose: bool
-            If True, prints warning messages.
-        return_F_matrices: bool
-            If True, returns the matrices A, B, C, D.
-        tol: float
-            The tolerance used to determine if a steady state value is close to zero.
-
-        Returns
-        -------
-        None or list of sympy matrices
-            If return_F_matrices is True, returns the F matrices. Otherwise, does not return anything.
-
-        """
-
-        if self.options.get("linear", False):
-            model_is_linear = True
-
-        free_param_dict = self.free_param_dict.copy()
-
-        parameters = list(free_param_dict.to_sympy().keys())
-        variables = list(self.steady_state_dict.to_sympy().keys())
-        params_to_calibrate = list(self.calib_param_dict.to_sympy().keys())
-
-        all_params = parameters + params_to_calibrate
-
-        shocks = self.shocks
-        shock_ss_dict = dict(zip([x.to_ss() for x in shocks], np.zeros(self.n_shocks)))
-        variables_and_shocks = self.variables + shocks
-        valid_names = [x.base_name for x in variables_and_shocks]
-
-        steady_state_dict = self.steady_state_dict.copy()
-
-        if not model_is_linear:
-            # We need shocks to be zero in A, B, C, D but 1 in T; can abuse the T_dummies to accomplish that.
-            if not_loglin_variables is None:
-                not_loglin_variables = []
-
-            not_loglin_variables += [x.base_name for x in shocks]
-
-            # Validate that all user-supplied variables are in the model
-            for variable in not_loglin_variables:
-                if variable not in valid_names:
-                    raise VariableNotFoundException(variable)
-
-            # Variables that are zero at the SS can't be log-linearized, check for these here.
-            close_to_zero_warnings = []
-            for variable in variables_and_shocks:
-                if variable.base_name in not_loglin_variables:
-                    continue
-
-                if abs(steady_state_dict[variable.to_ss().name]) < tol:
-                    not_loglin_variables.append(variable.base_name)
-                    close_to_zero_warnings.append(variable)
-
-            if len(close_to_zero_warnings) > 0 and verbose:
-                warn(
-                    "The following variables have steady state values close to zero and will not be log linearized: "
-                    + ", ".join(x.base_name for x in close_to_zero_warnings)
-                )
-
-        if order != 1:
-            raise NotImplementedError
-
-        if not self.steady_state_solved:
-            raise SteadyStateNotSolvedError()
-
-        if model_is_linear:
-            Fs = self.perturbation_solver.convert_linear_system_to_matrices()
-
-        else:
-            Fs = self.perturbation_solver.log_linearize_model(
-                not_loglin_variables=not_loglin_variables
-            )
-
-        Fs_subbed = [F.subs(shock_ss_dict) for F in Fs]
-        self.build_perturbation_matrices = numba_lambdify(
-            exog_vars=all_params, endog_vars=variables, expr=Fs_subbed
-        )
-
-        if return_F_matrices:
-            return Fs_subbed
-
-    def check_bk_condition(
-        self,
-        free_param_dict: dict[str, float] | None = None,
-        system_matrices: list[ArrayLike] | None = None,
-        verbose: bool | None = True,
-        return_value: str | None = "df",
-        tol=1e-8,
-    ) -> bool | pd.DataFrame:
-        """
-        Compute the generalized eigenvalues of system in the form presented in [1]. Per [2], the number of
-        unstable eigenvalues (:math:`|v| > 1`) should not be greater than the number of forward-looking variables.
-        Failing this test suggests timing problems in the definition of the model.
-
-        Parameters
-        ----------
-        free_param_dict: dict, optional
-            A dictionary of parameter values. If None, the current stored values are used.
-        system_matrices: list, optional
-            A list of matrices A, B, C, D to be used to compute the bk_condition. If none, the current
-            stored values are used.
-        verbose: bool, default: True
-            Flag to print the results of the test, otherwise the eigenvalues are returned without comment.
-        return_value: string, default: 'df'
-            Controls what is returned by the function. Valid values are 'df', 'bool', and 'none'.
-            If df, a dataframe containing eigenvalues is returned. If 'bool', a boolean indicating whether the BK
-            condition is satisfied. If None, nothing is returned.
-        tol: float, 1e-8
-            Convergence tolerance for the gensys solver
-
-        Returns
-        -------
-        None
-            If "return_value" is 'none'
-        condition_satisfied: bool
-            Flag indicating whether the BK condition was met, if "return_value" is "bool"
-        eigenvalues: pd.DataFrame
-            Dataframe of eigenvalues with three columns: real part, imaginary part, and modulus, if
-            "return_value" is "df"
-        """
-        if self.build_perturbation_matrices is None:
-            raise PerturbationSolutionNotFoundException()
-
-        if return_value not in ["df", "bool", "none"]:
-            raise ValueError(
-                f'return_value must be one of "df", "bool", or "none". Found {return_value} '
-            )
-
-        if free_param_dict is not None:
-            results = self.f_ss(self.free_param_dict)
-            self.steady_state_dict = results["ss_dict"]
-            self.calib_param_dict = results["calib_dict"]
-
-        param_dict = self.free_param_dict | self.calib_param_dict
-        steady_state_dict = self.steady_state_dict
-
-        if system_matrices is not None:
-            A, B, C, D = system_matrices
-        else:
-            A, B, C, D = self.build_perturbation_matrices(
-                np.array(list(param_dict.values())),
-                np.array(list(steady_state_dict.values())),
-            )
-
-        n_forward = (C.sum(axis=0) > 0).sum().astype(int)
-        n_eq, n_vars = A.shape
-
-        # TODO: Compute system eigenvalues -- avoids calling the whole Gensys routine, but there is code duplication
-        #   building Gamma_0 and Gamma_1
-        lead_var_idx = np.where(np.sum(np.abs(C), axis=0) > tol)[0]
-
-        eqs_and_leads_idx = np.r_[np.arange(n_vars), lead_var_idx + n_vars].tolist()
-
-        Gamma_0 = np.vstack(
-            [np.hstack([B, C]), np.hstack([-np.eye(n_eq), np.zeros((n_eq, n_eq))])]
-        )
-
-        Gamma_1 = np.vstack(
-            [
-                np.hstack([A, np.zeros((n_eq, n_eq))]),
-                np.hstack([np.zeros((n_eq, n_eq)), np.eye(n_eq)]),
-            ]
-        )
-        Gamma_0 = Gamma_0[eqs_and_leads_idx, :][:, eqs_and_leads_idx]
-        Gamma_1 = Gamma_1[eqs_and_leads_idx, :][:, eqs_and_leads_idx]
-
-        # A, B, Q, Z = qzdiv(1.01, *linalg.qz(-Gamma_0, Gamma_1, 'complex'))
-
-        # Using scipy instead of qzdiv appears to offer a huge speedup for nearly the same answer; some eigenvalues
-        # have sign flip relative to qzdiv -- does it matter?
-        A, B, alpha, beta, Q, Z = linalg.ordqz(
-            -Gamma_0, Gamma_1, sort="ouc", output="complex"
-        )
-
-        gev = np.c_[np.diagonal(A), np.diagonal(B)]
-
-        eigenval = gev[:, 1] / (gev[:, 0] + tol)
-        pos_idx = np.where(np.abs(eigenval) > 0)
-        eig = np.zeros(((np.abs(eigenval) > 0).sum(), 3))
-        eig[:, 0] = np.abs(eigenval)[pos_idx]
-        eig[:, 1] = np.real(eigenval)[pos_idx]
-        eig[:, 2] = np.imag(eigenval)[pos_idx]
-
-        sorted_idx = np.argsort(eig[:, 0])
-        eig = pd.DataFrame(eig[sorted_idx, :], columns=["Modulus", "Real", "Imaginary"])
-
-        n_g_one = (eig["Modulus"] > 1).sum()
-        condition_not_satisfied = n_forward > n_g_one
-        if verbose:
-            print(
-                f"Model solution has {n_g_one} eigenvalues greater than one in modulus and {n_forward} "
-                f"forward-looking variables."
-                f'\nBlanchard-Kahn condition is{" NOT" if condition_not_satisfied else ""} satisfied.'
-            )
-
-        if return_value == "none":
-            return
-        if return_value == "df":
-            return eig
-        elif return_value == "bool":
-            return ~condition_not_satisfied
-
-    def compute_stationary_covariance_matrix(
-        self,
-        shock_dict: dict[str, float] | None = None,
-        shock_cov_matrix: ArrayLike | None = None,
-    ):
-        """
-        Compute the stationary covariance matrix of the solved system by solving the associated discrete lyapunov
-        equation. In order to construct the shock covariance matrix, exactly one or zero of shock_dict or
-        shock_cov_matrix should be provided. If neither is provided, the prior means on the shocks will be used. If no
-        information about a shock is available, the standard deviation will be set to 0.01.
-
-        Parameters
-        ----------
-        shock_dict, dict of str: float, optional
-            A dictionary of shock sizes to be used to compute the stationary covariance matrix.
-        shock_cov_matrix: array, optional
-            An (n_shocks, n_shocks) covariance matrix describing the exogenous shocks
-
-        Returns
-        -------
-        sigma: DataFrame
-        """
-        if not self.perturbation_solved:
-            raise PerturbationSolutionNotFoundException()
-        shock_std_priors = get_shock_std_priors_from_hyperpriors(
-            self.shocks, self.hyper_priors, out_keys="parent"
-        )
-
-        if (
-            shock_dict is None
-            and shock_cov_matrix is None
-            and len(shock_std_priors) < self.n_shocks
-        ):
-            unknown_shocks_list = [
-                shock.base_name
-                for shock in self.shocks
-                if shock not in self.shock_priors.to_sympy()
-            ]
-            unknown_shocks = ", ".join(unknown_shocks_list)
-            warn(
-                f"No standard deviation provided for shocks {unknown_shocks}. Using default of std = 0.01. Explicity"
-                f"pass variance information for these shocks or set their priors to silence this warning."
-            )
-
-        Q = build_Q_matrix(
-            model_shocks=[x.base_name for x in self.shocks],
-            shock_dict=shock_dict,
-            shock_cov_matrix=shock_cov_matrix,
-            shock_std_priors=shock_std_priors,
-        )
-
-        T, R = self.T, self.R
-        sigma = linalg.solve_discrete_lyapunov(T.values, R.values @ Q @ R.values.T)
-
-        return pd.DataFrame(sigma, index=T.index, columns=T.index)
-
-    def compute_autocorrelation_matrix(
-        self,
-        shock_dict: dict[str, float] | None = None,
-        shock_cov_matrix: ArrayLike | None = None,
-        n_lags=10,
-    ):
-        """
-        Computes autocorrelations for each model variable using the stationary covariance matrix. See doc string for
-        compute_stationary_covariance_matrix for more information.
-
-        In order to construct the shock covariance matrix, exactly one or zero of shock_dict or
-        shock_cov_matrix should be provided. If neither is provided, the prior means on the shocks will be used. If no
-        information about a shock is available, the standard deviation will be set to 0.01.
-
-        Parameters
-        ----------
-        shock_dict, dict of str: float, optional
-            A dictionary of shock sizes to be used to compute the stationary covariance matrix.
-        shock_cov_matrix: array, optional
-            An (n_shocks, n_shocks) covariance matrix describing the exogenous shocks
-        n_lags: int
-            Number of lags over which to compute the autocorrelation
-
-        Returns
-        -------
-        acorr_mat: DataFrame
-        """
-        if not self.perturbation_solved:
-            raise PerturbationSolutionNotFoundException()
-
-        T = self.T
-
-        Sigma = self.compute_stationary_covariance_matrix(
-            shock_dict=shock_dict, shock_cov_matrix=shock_cov_matrix
-        )
-        acorr_mat = compute_autocorrelation_matrix(
-            T.values, Sigma.values, n_lags=n_lags
-        )
-
-        return pd.DataFrame(acorr_mat, index=T.index, columns=np.arange(n_lags))
-
-    def fit(
-        self,
-        data,
-        estimate_a0=False,
-        estimate_P0=False,
-        a0_prior=None,
-        P0_prior=None,
-        filter_type="univariate",
-        draws=5000,
-        n_walkers=36,
-        moves=None,
-        emcee_x0=None,
-        verbose=True,
-        return_inferencedata=True,
-        burn_in=None,
-        thin=None,
-        skip_initial_state_check=False,
-        compute_sampler_stats=True,
-        **sampler_kwargs,
-    ):
-        """
-        Estimate model parameters via Bayesian inference. Parameter likelihood is computed using the Kalman filter.
-        Posterior distributions are estimated using Markov Chain Monte Carlo (MCMC), specifically the Affine-Invariant
-        Ensemble Sampler algorithm of [1].
-
-        A "traditional" Random Walk Metropolis can be achieved using the moves argument, but by default this function
-        will use a mix of two Differential Evolution (DE) proposal algorithms that have been shown to work well on
-        weakly multi-modal problems. DSGE estimation can be multi-modal in the sense that regions of the posterior
-        space are separated by the constraints on the ability to solve the perturbation problem.
-
-        This function will start all MCMC chains around random draws from the prior distribution. This is in contrast
-        to Dynare and gEcon.estimate, which start MCMC chains around the Maximum Likelihood estimate for parameter
-        values.
-
-        Parameters
-        ----------
-        data: dataframe
-            A pandas dataframe of observed values, with column names corresponding to DSGE model states names.
-        estimate_a0: bool, default: False
-            Whether to estimate the initial values of the DSGE process. If False, x0 will be deterministically set to
-            a vector of zeros, corresponding to the steady state. If True, you must provide a
-        estimate_P0: bool, default: False
-            Whether to estimate the intial covariance matrix of the DSGE process. If False, P0 will be set to the
-            Kalman Filter steady state value by solving the associated discrete Lyapunov equation.
-        a0_prior: dict, optional
-            A dictionary with (variable name, scipy distribution) key-value pairs. If a key "initial_vector" is found,
-            all other keys will be ignored, and the single distribution over all initial states will be used. Otherwise,
-            n_states independent distributions should be included in the dictionary.
-            If estimate_a0 is False, this will be ignored.
-        P0_prior: dict, optional
-            A dictionary with (variable name, scipy distribution) key-value pairs. If a key "initial_covariance" is
-            found, all other keys will be ignored, and this distribution will be taken as over the entire covariance
-            matrix. Otherwise, n_states independent distributions are expected, and are used to construct a diagonal
-            initial covariance matrix.
-        filter_type: string, default: "standard"
-            Select a kalman filter implementation to use. Currently "standard" and "univariate" are supported. Try
-            univariate if you run into errors inverting the P matrix during filtering.
-        draws: integer
-            Number of draws from each MCMC chain, or "walker" in the jargon of emcee.
-        n_walkers: integer
-            The number of "walkers", which roughly correspond to chains in other MCMC packages. Note that one needs
-            many more walkers than chains; [1] recommends as many as possible.
-        cores: integer
-            The number of processing cores, which is passed to Multiprocessing.Pool to do parallel inference. To
-            maintain detailed balance, the pool of walkers must be split, resulting in n_walkers / cores sub-ensembles.
-            Be sure to raise the number of walkers to compensate.
-        moves: List of emcee.moves objects
-            Moves tell emcee how to generate MCMC proposals. See the emcee docs for details.
-        emcee_x0: array
-            An (n_walkers, k_parameters) array of initial values. Emcee will check the condition number of the matrix
-            to ensure all walkers begin in different regions of the parameter space. If MLE estimates are used, they
-            should be jittered to start walkers in a ball around the desired initial point.
-        return_inferencedata: bool, default: True
-            If true, return an Arviz InferenceData object containing posterior samples. If False, the fitted Emcee
-            sampler is returned.
-        burn_in: int, optional
-            Number of initial samples to discard from all chains. This is ignored if return_inferencedata is False.
-        thin: int, optional
-            Return only every n-th sample from each chain. This is done to reduce storage requirements in highly
-            autocorrelated chains by discarding redundant information. Ignored if return_inferencedata is False.
-
-        Returns
-        -------
-        sampler, emcee.Sampler object
-            An emcee.Sampler object with the estimated posterior over model parameters, as well as other diagnotic
-            information.
-
-        References
-        ----------
-        ..[1] Foreman-Mackey, Daniel, et al. “Emcee: The MCMC Hammer.” Publications of the Astronomical Society of the
-              Pacific, vol. 125, no. 925, Mar. 2013, pp. 306–12. arXiv.org, https://doi.org/10.1086/670067.
-        """
-        observed_vars = data.columns.tolist()
-        n_obs = len(observed_vars)
-        n_shocks = self.n_shocks
-        model_var_names = [x.base_name for x in self.variables]
-        n_noise_priors = len(self.observation_noise_priors)
-
-        if n_obs > (n_noise_priors + n_shocks):
-            raise ValueError(
-                f"Number of observed parameters in data ({n_obs}) is greater than the number of sources "
-                f"of stochastic variance - shocks ({n_shocks}) and observation noise ({n_noise_priors}). "
-                f"The model cannot be fit due to stochastic singularity."
-            )
-
-        if burn_in is None:
-            burn_in = 0
-
-        if not all([x in model_var_names for x in observed_vars]):
-            orphans = [x for x in observed_vars if x not in model_var_names]
-            raise ValueError(
-                f"Columns of data must correspond to states of the DSGE model. Found the following columns"
-                f'with no associated model state: {", ".join(orphans)}'
-            )
-
-        # sparse_data = extract_sparse_data_from_model(self)
-        prior_dict = extract_prior_dict(self)
-
-        if estimate_a0 is False:
-            a0 = None  #  noqa
-        else:
-            if a0_prior is None:
-                raise ValueError(
-                    "If estimate_a0 is True, you must provide a dictionary of prior distributions for"
-                    "the initial values of all individual states"
-                )
-            if not all([var in a0_prior.keys() for var in model_var_names]):
-                missing_keys = set(model_var_names) - set(list(a0_prior.keys()))
-                raise ValueError(
-                    "You must provide one key for each state in the model. "
-                    f'No keys found for: {", ".join(missing_keys)}'
-                )
-            for var in model_var_names:
-                prior_dict[f"{var}__initial"] = a0_prior[var]
-
-        moves = moves or [
-            (emcee.moves.DEMove(), 0.6),
-            (emcee.moves.DESnookerMove(), 0.4),
-        ]
-
-        shock_names = [x.base_name for x in self.shocks]
-
-        k_params = len(prior_dict)
-        Z = build_Z_matrix(observed_vars, model_var_names)
-
-        args = [
-            data.values,
-            self.f_ss,
-            self.build_perturbation_matrices,
-            self.free_param_dict,
-            Z,
-            prior_dict,
-            shock_names,
-            observed_vars,
-            filter_type,
-        ]
-
-        arg_names = [
-            "observed_data",
-            "f_ss",
-            "f_pert",
-            "free_params",
-            "Z",
-            "prior_dict",
-            "shock_names",
-            "observed_vars",
-            "filter_type",
-        ]
-
-        if emcee_x0:
-            x0 = emcee_x0
-        else:
-            x0 = np.stack([x.rvs(n_walkers) for x in prior_dict.values()]).T
-
-        param_names = list(prior_dict.keys())
-
-        sampler = emcee.EnsembleSampler(
-            n_walkers,
-            k_params,
-            evaluate_logp2,
-            args=args,
-            moves=moves,
-            parameter_names=param_names,
-            **sampler_kwargs,
-        )
-
-        with catch_warnings():
-            simplefilter("ignore")
-            _ = sampler.run_mcmc(
-                x0,
-                draws + burn_in,
-                progress=verbose,
-                skip_initial_state_check=skip_initial_state_check,
-            )
-
-        if return_inferencedata:
-            idata = az.from_emcee(
-                sampler,
-                var_names=param_names,
-                blob_names=["log_likelihood"],
-                arg_names=arg_names,
-            )
-
-            if compute_sampler_stats:
-                sampler_stats = xr.Dataset(
-                    data_vars=dict(
-                        acceptance_fraction=(["chain"], sampler.acceptance_fraction),
-                        autocorrelation_time=(
-                            ["parameters"],
-                            sampler.get_autocorr_time(discard=burn_in or 0, quiet=True),
-                        ),
-                    ),
-                    coords=dict(chain=np.arange(n_walkers), parameters=param_names),
-                )
-
-                idata["sample_stats"].update(sampler_stats)
-            idata.observed_data = idata.observed_data.drop_vars(["prior_dict"])
-
-            return idata.sel(draw=slice(burn_in, None, thin))
-
-        return sampler
-
-    def sample_param_dict_from_prior(self, n_samples=1, seed=None, param_subset=None):
-        """
-        Sample parameters from the parameter prior distributions.
-
-        Parameters
-        ----------
-        n_samples: int, default: 1
-            Number of samples to draw from the prior distributions.
-        seed: int, default: None
-            Seed for the random number generator.
-        param_subset: list, default: None
-            List of parameter names to sample. If None, all parameters are sampled.
-
-        Returns
-        -------
-        new_param_dict: dict
-            Dictionary of sampled parameters.
-        """
-        shock_std_priors = get_shock_std_priors_from_hyperpriors(
-            self.shocks, self.hyper_priors
-        )
-
-        all_priors = (
-            self.param_priors.to_sympy()
-            | shock_std_priors
-            | self.observation_noise_priors.to_sympy()
-        )
-
-        if len(all_priors) == 0:
-            raise ValueError("No model priors found, cannot sample.")
-
-        if param_subset is None:
-            n_variables = len(all_priors)
-            priors_to_sample = all_priors
-        else:
-            n_variables = len(param_subset)
-            priors_to_sample = SymbolDictionary(
-                {k: v for k, v in all_priors.items() if k.name in param_subset}
-            )
-
-        if seed is not None:
-            seed_sequence = np.random.SeedSequence(seed)
-            child_seeds = seed_sequence.spawn(n_variables)
-            streams = [np.random.default_rng(s) for s in child_seeds]
-        else:
-            streams = [None] * n_variables
-
-        new_param_dict = {}
-        for i, (key, d) in enumerate(priors_to_sample.items()):
-            new_param_dict[key] = d.rvs(size=n_samples, random_state=streams[i])
-
-        free_param_dict, shock_dict, obs_dict = split_random_variables(
-            new_param_dict, self.shocks, self.variables
-        )
-
-        return free_param_dict.to_string(), shock_dict.to_string(), obs_dict.to_string()
-
-    def impulse_response_function(
-        self, simulation_length: int = 40, shock_size: float = 1.0
-    ):
-        """
-        Compute the impulse response functions of the model.
-
-        Parameters
-        ----------
-        simulation_length : int, optional
-            The number of periods to compute the IRFs over. The default is 40.
-        shock_size : float, optional
-            The size of the shock. The default is 1.0.
-
-        Returns
-        -------
-        pandas.DataFrame
-            The IRFs for each variable in the model. The DataFrame has a multi-index
-            with the variable names as the first level and the timestep as the second.
-            The columns are the shocks.
-
-        Raises
-        ------
-        PerturbationSolutionNotFoundException
-            If a perturbation solution has not been found.
-        """
-
-        if not self.perturbation_solved:
-            raise PerturbationSolutionNotFoundException()
-
-        T, R = self.T, self.R
-
-        timesteps = simulation_length
-
-        data = np.zeros((self.n_variables, timesteps, self.n_shocks))
-
-        for i in range(self.n_shocks):
-            shock_path = np.zeros((self.n_shocks, timesteps))
-            shock_path[i, 0] = shock_size
-
-            for t in range(1, timesteps):
-                stochastic = R.values @ shock_path[:, t - 1]
-                deterministic = T.values @ data[:, t - 1, i]
-                data[:, t, i] = deterministic + stochastic
-
-        index = pd.MultiIndex.from_product(
-            [R.index, np.arange(timesteps), R.columns],
-            names=["Variables", "Time", "Shocks"],
-        )
-
-        df = (
-            pd.DataFrame(data.ravel(), index=index, columns=["Values"])
-            .unstack([1, 2])
-            .droplevel(axis=1, level=0)
-            .sort_index(axis=1)
-        )
-
-        return df
-
-    def simulate(
-        self,
-        simulation_length: int = 40,
-        n_simulations: int = 100,
-        shock_dict: dict[str, float] | None = None,
-        shock_cov_matrix: ArrayLike | None = None,
-        show_progress_bar: bool = False,
-    ):
-        """
-        Simulate the model over a certain number of time periods.
-
-        Parameters
-        ----------
-        simulation_length : int, optional(default=40)
-            The number of time periods to simulate.
-        n_simulations : int, optional(default=100)
-            The number of simulations to run.
-        shock_dict : dict, optional(default=None)
-            Dictionary of shocks to use.
-        shock_cov_matrix : arraylike, optional(default=None)
-            Covariance matrix of shocks to use.
-        show_progress_bar : bool, optional(default=False)
-            Whether to show a progress bar for the simulation.
-
-        Returns
-        -------
-        df : pandas.DataFrame
-            The simulated data.
-        """
-
-        if not self.perturbation_solved:
-            raise PerturbationSolutionNotFoundException()
-
-        T, R = self.T, self.R
-        timesteps = simulation_length
-
-        n_shocks = R.shape[1]
-        shock_std_priors = get_shock_std_priors_from_hyperpriors(
-            self.shocks, self.hyper_priors, out_keys="parent"
-        )
-
-        if (
-            shock_dict is None
-            and shock_cov_matrix is None
-            and len(shock_std_priors) < self.n_shocks
-        ):
-            unknown_shocks_list = [
-                shock.base_name
-                for shock in self.shocks
-                if shock not in self.shock_priors.to_sympy()
-            ]
-            unknown_shocks = ", ".join(unknown_shocks_list)
-            warn(
-                f"No standard deviation provided for shocks {unknown_shocks}. Using default of std = 0.01. Explicity"
-                f"pass variance information for these shocks or set their priors to silence this warning."
-            )
-
-        Q = build_Q_matrix(
-            model_shocks=[x.base_name for x in self.shocks],
-            shock_dict=shock_dict,
-            shock_cov_matrix=shock_cov_matrix,
-            shock_std_priors=shock_std_priors,
-        )
-
-        d_epsilon = stats.multivariate_normal(mean=np.zeros(n_shocks), cov=Q)
-        epsilons = np.r_[[d_epsilon.rvs(timesteps) for _ in range(n_simulations)]]
-
-        data = np.zeros((self.n_variables, timesteps, n_simulations))
-        if epsilons.ndim == 2:
-            epsilons = epsilons[:, :, None]
-
-        progress_bar = ProgressBar(timesteps - 1, verb="Sampling")
-
-        for t in range(1, timesteps):
-            progress_bar.start()
-            stochastic = np.einsum("ij,sj", R.values, epsilons[:, t - 1, :])
-            deterministic = T.values @ data[:, t - 1, :]
-            data[:, t, :] = deterministic + stochastic
-
-            if show_progress_bar:
-                progress_bar.stop()
-
-        index = pd.MultiIndex.from_product(
-            [R.index, np.arange(timesteps), np.arange(n_simulations)],
-            names=["Variables", "Time", "Simulation"],
-        )
-        df = (
-            pd.DataFrame(data.ravel(), index=index, columns=["Values"])
-            .unstack([1, 2])
-            .droplevel(axis=1, level=0)
-        )
-
-        return df
-
-    def _build_prior_dict(self, prior_dict: dict[str, str]) -> None:
-        """
-        Parameters
-        ----------
-        prior_dict: dict
-            Dictionary of variable_name: distribution_string pairs, prepared by the parse_gcn function.
-
-        Returns
-        -------
-        self.param_dict: dict
-            Dictionary of variable:distribution pairs. Distributions are scipy rv_frozen objects, unless the
-            distribution is parameterized by another distribution, in which case a "CompositeDistribution" object
-            with methods .rvs, .pdf, and .logpdf is returned.
-        """
-
-        priors, hyper_priors = create_prior_distribution_dictionary(prior_dict)
-        hyper_parameters = set(prior_dict.keys()) - set(priors.keys())
-
-        # Remove hyperparameters from the free parameters
-        for parameter in hyper_parameters:
-            del self.free_param_dict[parameter]
-
-        param_priors = SymbolDictionary()
-        shock_priors = SymbolDictionary()
-        hyper_priors_final = SymbolDictionary()
-
-        for key, value in priors.items():
-            sympy_key = single_symbol_to_sympy(key, assumptions=self.assumptions)
-            if isinstance(sympy_key, TimeAwareSymbol):
-                shock_priors[sympy_key.base_name] = value
-            else:
-                param_priors[sympy_key.name] = value
-
-        for key, value in hyper_priors.items():
-            parent_rv, param_type, dist = value
-            parent_key = single_symbol_to_sympy(parent_rv, assumptions=self.assumptions)
-            param_key = single_symbol_to_sympy(key, assumptions=self.assumptions)
-
-            hyper_priors_final[param_key] = (parent_key, param_type, dist)
-
-        self.param_priors = param_priors
-        self.shock_priors = shock_priors
-        self.hyper_priors = hyper_priors_final
-
-    def _build_model_blocks(self, parsed_model, simplify_blocks: bool):
-        """
-        Builds blocks of the gEconpy model using strings parsed from the GCN file.
-
-        Parameters
-        ----------
-        parsed_model : str
-            The GCN model as a string.
-        simplify_blocks : bool
-            Whether to try to simplify equations or not.
-        """
-
-        raw_blocks = gEcon_parser.split_gcn_into_block_dictionary(parsed_model)
-
-        if raw_blocks["options"] is not None:
-            self.options = raw_blocks["options"]
-        self.try_reduce_vars = raw_blocks["tryreduce"]
-        self.assumptions = raw_blocks["assumptions"]
-
-        del raw_blocks["options"]
-        del raw_blocks["tryreduce"]
-        del raw_blocks["assumptions"]
-
-        self._get_steady_state_equations(raw_blocks)
-
-        for block_name, block_content in raw_blocks.items():
-            block_dict = gEcon_parser.parsed_block_to_dict(block_content)
-            block = Block(
-                name=block_name, block_dict=block_dict, assumptions=self.assumptions
-            )
-            block.solve_optimization(try_simplify=simplify_blocks)
-
-            self.blocks[block.name] = block
-
-        self.n_blocks = len(self.blocks)
-
-    def _get_all_block_equations(self) -> None:
-        """
-        Extract all equations from the blocks in the model.
-
-        Parameters
-        ----------
-        self : `Model`
-            The model object whose block system equations will be extracted.
-
-        Returns
-        -------
-        None
-
-        Notes
-        -----
-        Updates the `system_equations` attribute of `self` with the extracted equations.
-        Also updates the `n_equations` attribute of `self` with the number of extracted equations.
-        """
-
-        _, blocks = unpack_keys_and_values(self.blocks)
-        for block in blocks:
-            self.system_equations.extend(block.system_equations)
-        self.n_equations = len(self.system_equations)
-
-    def _get_all_block_parameters(self) -> None:
-        """
-        Extract all parameters from all blocks and store them in the model's free_param_dict attribute. The
-        `free_param_dict` attribute is updated in place.
-        """
-
-        _, blocks = unpack_keys_and_values(self.blocks)
-        for block in blocks:
-            self.free_param_dict = self.free_param_dict | block.param_dict
-
-        self.free_param_dict = (
-            self.free_param_dict.sort_keys().to_string().values_to_float()
-        )
-
-    def _get_all_block_params_to_calibrate(self) -> None:
-        """
-        Retrieve the list of parameters to calibrate and the list of
-        equations used to calibrate the parameters from each block of
-        the model.
-        """
-        _, blocks = unpack_keys_and_values(self.blocks)
-        for block in blocks:
-            if block.params_to_calibrate is None:
-                continue
-
-            if len(self.params_to_calibrate) == 0:
-                self.params_to_calibrate = block.params_to_calibrate
-            else:
-                self.params_to_calibrate.extend(block.params_to_calibrate)
-
-            if block.calibrating_equations is None:
-                continue
-
-            if len(self.calibrating_equations) == 0:
-                self.calibrating_equations = block.calibrating_equations
-            else:
-                self.calibrating_equations.extend(block.calibrating_equations)
-
-        alpha_sort_idx = np.argsort([x.name for x in self.params_to_calibrate])
-        self.params_to_calibrate = [self.params_to_calibrate[i] for i in alpha_sort_idx]
-        self.calibrating_equations = [
-            self.calibrating_equations[i] for i in alpha_sort_idx
-        ]
-
-        self.n_calibrating_equations = len(self.calibrating_equations)
-        self.n_params_to_calibrate = len(self.params_to_calibrate)
-
-    def _get_all_block_deterministic_parameters(self) -> None:
-        _, blocks = unpack_keys_and_values(self.blocks)
-        for block in blocks:
-            if block.deterministic_params is None:
-                continue
-
-            if len(self.deterministic_params) == 0:
-                self.deterministic_params = block.deterministic_params
-            else:
-                self.deterministic_params.extend(block.deterministic_params)
-
-            if block.deterministic_relationships is None:
-                continue
-
-            if len(self.deterministic_relationships) == 0:
-                self.deterministic_relationships = block.deterministic_relationships
-            else:
-                self.deterministic_relationships.extend(
-                    block.deterministic_relationships
-                )
-
-        alpha_sort_idx = np.argsort([x.name for x in self.deterministic_params])
-        self.deterministic_params = [
-            self.deterministic_params[i] for i in alpha_sort_idx
-        ]
-        self.deterministic_relationships = [
-            self.deterministic_relationships[i] for i in alpha_sort_idx
-        ]
-
-    def _get_variables_and_shocks(self) -> None:
-        """
-        Collect all variables and shocks from the blocks and set their counts.
-
-        This method is called after the blocks have been processed. It collects all the shocks and variables from the
-        blocks, sorts them, and sets the n_shocks and n_variables properties.
-        """
-
-        all_shocks = []
-        _, blocks = unpack_keys_and_values(self.blocks)
-
-        for block in blocks:
-            if block.shocks is not None:
-                all_shocks.extend([x for x in block.shocks])
-        self.shocks = all_shocks
-        self.n_shocks = len(all_shocks)
-
-        for eq in self.system_equations:
-            atoms = eq.atoms()
-            variables = [x for x in atoms if is_variable(x)]
-            for variable in variables:
-                if (
-                    variable.set_t(0) not in self.variables
-                    and variable not in all_shocks
-                ):
-                    self.variables.append(variable.set_t(0))
-
-        self.n_variables = len(self.variables)
-
-        self.variables = sorted(self.variables, key=lambda x: x.name)
-        self.shocks = sorted(self.shocks, key=lambda x: x.name)
-
-    def _get_steady_state_equations(self, raw_blocks: dict[str, list[str]]):
-        """
-        Extract user-provided steady state equations from the `raw_blocks` dictionary and store the resulting
-        relationships in self.steady_state_relationships.
-
-        Parameters
-        ----------
-        raw_blocks : dict
-            Dictionary of block names and block contents extracted from a gEcon model.
-
-        Raises
-        ------
-        MultipleSteadyStateBlocksException
-            If there is more than one block in `raw_blocks` with a name from `STEADY_STATE_NAMES`.
-        """
-
-        block_names = raw_blocks.keys()
-        ss_block_names = [name for name in block_names if name in STEADY_STATE_NAMES]
-        n_ss_blocks = len(ss_block_names)
-
-        if n_ss_blocks == 0:
-            return
-        if n_ss_blocks > 1:
-            raise MultipleSteadyStateBlocksException(ss_block_names)
-
-        block_content = raw_blocks[ss_block_names[0]]
-        block_dict = gEcon_parser.parsed_block_to_dict(block_content)
-        block = Block(
-            name="steady_state", block_dict=block_dict, assumptions=self.assumptions
-        )
-
-        sub_dict = SymbolDictionary()
-        steady_state_dict = SymbolDictionary()
-
-        if block.definitions is not None:
-            _, definitions = unpack_keys_and_values(block.definitions)
-            sub_dict = SymbolDictionary({eq.lhs: eq.rhs for eq in definitions})
-
-        if block.identities is not None:
-            _, identities = unpack_keys_and_values(block.identities)
-            for eq in identities:
-                subbed_rhs = eq.rhs.subs(sub_dict)
-                steady_state_dict[eq.lhs] = subbed_rhs
-                sub_dict[eq.lhs] = subbed_rhs
-
-        for k, eq in steady_state_dict.items():
-            steady_state_dict[k] = eq.subs(steady_state_dict)
-
-        self.steady_state_relationships = (
-            steady_state_dict.sort_keys().to_string().values_to_float()
-        )
-
-        del raw_blocks[ss_block_names[0]]
-
-    def _try_reduce(self):
-        """
-        Attempt to reduce the number of equations in the system by removing equations requested in the `tryreduce`
-        block of the GCN file. Equations are considered safe to remove if they are "self-contained" that is, if
-        no other variables depend on their values.
-
-        Returns
-        -------
-        list
-            The names of the variables that were removed. If reduction was not possible, None is returned.
-        """
-        if self.n_equations != self.n_variables:
-            warn(
-                "Simplification via try_reduce was requested but not possible because the system is not well defined."
-            )
-            return
-
-        if self.try_reduce_vars is None:
-            return
-
-        self.try_reduce_vars = [
-            single_symbol_to_sympy(x, self.assumptions) for x in self.try_reduce_vars
-        ]
-
-        variables = self.variables
-        n_variables = self.n_variables
-
-        occurrence_matrix = np.zeros((n_variables, n_variables))
-        reduced_system = []
-
-        for i, eq in enumerate(self.system_equations):
-            for j, var in enumerate(self.variables):
-                if any([x in eq.atoms() for x in make_all_var_time_combos([var])]):
-                    occurrence_matrix[i, j] += 1
-
-        # Columns with a sum of 1 are variables that appear only in a single equations; these equations can be deleted
-        # without consequence w.r.t solving the system.
-
-        isolated_variables = np.array(variables)[occurrence_matrix.sum(axis=0) == 1]
-        to_remove = set(isolated_variables).intersection(set(self.try_reduce_vars))
-
-        for eq in self.system_equations:
-            if not any([var in eq.atoms() for var in to_remove]):
-                reduced_system.append(eq)
-
-        self.system_equations = reduced_system
-        self.n_equations = len(self.system_equations)
-
-        self.variables = {
-            atom.set_t(0)
-            for eq in reduced_system
-            for atom in eq.atoms()
-            if is_variable(atom)
-        }
-        self.variables -= set(self.shocks)
-        self.variables = sorted(list(self.variables), key=lambda x: x.name)
-        self.n_variables = len(self.variables)
-
-        eliminated_vars = [var.name for var in variables if var not in self.variables]
-
-        return eliminated_vars
-
-    def _simplify_singletons(self):
-        """
-        Simplify the system by removing variables that are deterministically defined as a known value. Common examples
-        include P[] = 1, setting the price level of the economy as the numeraire, or B[] = 0, putting the bond market
-        in net-zero supply.
-
-        In these cases, the variable can be replaced by the deterministic value after all FoC
-        have been computed.
-
-        Returns
-        -------
-        eliminated_vars : List[str]
-            The names of the variables that were removed.
-        """
-
-        if self.n_equations != self.n_variables:
-            warn(
-                "Removal of constant variables was requested but not possible because the system is not well defined."
-            )
-            return
-
-        system = self.system_equations
-
-        variables = self.variables
-        reduce_dict = {}
-
-        for eq in system:
-            if len(eq.atoms()) < 4:
-                var = [x for x in eq.atoms() if is_variable(x)]
-                if len(var) != 1:
-                    continue
-                var = var[0]
-                sub_dict = expand_subs_for_all_times(sp.solve(eq, var, dict=True)[0])
-                reduce_dict.update(sub_dict)
-
-        reduced_system = substitute_all_equations(system, reduce_dict)
-        reduced_system = [eq for eq in reduced_system if eq != 0]
-
-        self.system_equations = reduced_system
-        self.n_equations = len(reduced_system)
-
-        self.variables = {
-            atom.set_t(0)
-            for eq in reduced_system
-            for atom in eq.atoms()
-            if is_variable(atom)
-        }
-        self.variables -= set(self.shocks)
-        self.variables = sorted(list(self.variables), key=lambda x: x.name)
-        self.n_variables = len(self.variables)
-
-        eliminated_vars = [var.name for var in variables if var not in self.variables]
-
-        return eliminated_vars
-
-    def _make_deterministic_substitutions(self):
-        if self.deterministic_params is None:
-            return
-
-        all_atoms = reduce(
-            lambda left, right: left.union(right),
-            [eq.atoms() for eq in self.system_equations],
-        )
-
-        if not any([det_var in all_atoms for det_var in self.deterministic_params]):
-            return
-
-        det_sub_dict = dict(
-            zip(self.deterministic_params, self.deterministic_relationships)
-        )
-
-        # recursively substitute the dictionary on itself, in case there are any relationships between the relationships
-        for i in range(5):
-            all_atoms = reduce(
-                lambda left, right: left.union(right),
-                [eq.atoms() for eq in det_sub_dict.values()],
-            )
-            if any([det_param in all_atoms for det_param in self.deterministic_params]):
-                det_sub_dict = substitute_all_equations(det_sub_dict, det_sub_dict)
-            else:
-                break
-        if i == 5:
-            raise ValueError(
-                "Could not reduce deterministic relationships to functions of only free parameters after"
-                "five recursive substitutions. Check that there are not circular definitions among the"
-                "deterministic parameters."
-            )
-
-        self.system_equations = [eq.subs(det_sub_dict) for eq in self.system_equations]
-
-    def _verify_no_orphan_params(self):
-        all_atoms = reduce(
-            lambda left, right: left.union(right),
-            [eq.atoms() for eq in self.system_equations + self.calibrating_equations],
-        )
-
-        all_params = [
-            x
-            for x in all_atoms
-            if isinstance(x, sp.Symbol) and not isinstance(x, TimeAwareSymbol)
-        ]
-
-        orphans = [
-            x.name
-            for x in all_params
-            if (x.name not in self.free_param_dict)
-            and (x not in self.params_to_calibrate)
-        ]
-
-        if len(orphans) > 0:
-            raise ValueError(
-                "The following parameters were found among model equations, but were not found among "
-                f'defined defined or calibrated parameters: {", ".join(orphans)}.\n Verify that these '
-                f"parameters have been defined in a calibration block somewhere in your GCN file."
-            )
diff --git a/gEconpy/classes/progress_bar.py b/gEconpy/classes/progress_bar.py
deleted file mode 100644
index 55ba86c..0000000
--- a/gEconpy/classes/progress_bar.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import time
-
-import numpy as np
-
-
-class ProgressBar:
-    """
-    A utility class for displaying a progress bar in the command line.
-    """
-
-    def __init__(self, total, verb="", start_iters=0, bar_length=50):
-        """
-        Initialize a ProgressBar instance.
-
-        Parameters
-        ----------
-        total : int
-            Total number of iterations.
-        verb : str, optional
-            String to be displayed before the progress bar. The default is ''.
-        start_iters : int, optional
-            Number of iterations that have already been completed. The default is 0.
-        bar_length : int, optional
-            Length of the progress bar in characters. The default is 50.
-        """
-
-        self.total = total
-        self.verb = verb
-        self.start_iters = start_iters
-        self.bar_length = bar_length
-
-        self.start_time = None
-        self.mean_time = 0
-        self.n_iters = 0
-
-        self.init_time = time.time()
-        self.last_print_time = 0
-
-    def start(self):
-        """Start tracking time for a loop iteration."""
-        self.n_iters += 1
-        self.start_time = time.time()
-
-    def stop(self):
-        """Stop tracking time for a loop iteration and update mean time using the Robins-Monroe algorithm."""
-
-        alpha = 1 / (self.n_iters + 1)
-        elapsed = time.time() - self.start_time
-        self.mean_time = alpha * elapsed + (1 - alpha) * self.mean_time
-
-        if (time.time() - self.last_print_time > 0.25) or (self.n_iters == self.total):
-            self.print_progress()
-
-    @staticmethod
-    def _time_to_string(timestamp):
-        """
-        Convert a time in seconds to a string in the format "mm:ss".
-
-        Parameters
-        ----------
-        timestamp : float
-            Time in seconds.
-
-        Returns
-        -------
-        Tuple[str, str]
-            Tuple of strings in the format "mm:ss".
-        """
-
-        minutes, seconds = np.divmod(timestamp, 60)
-        minutes = int(minutes)
-        minutes = "0" * (2 - len(str(minutes))) + str(minutes)
-        seconds = int(seconds)
-        seconds = "0" * (2 - len(str(seconds))) + str(seconds)
-
-        return minutes, seconds
-
-    def print_progress(self):
-        """
-        Print the current progress and remaining time to completion.
-        """
-
-        remaining = self.mean_time * (self.total - self.n_iters)
-        elapsed = time.time() - self.init_time
-
-        remain_min, remain_sec = self._time_to_string(remaining)
-        elapse_min, elapse_sec = self._time_to_string(elapsed)
-
-        iter_per_sec = self.n_iters / (elapsed + 1e-8)
-
-        n_digits = len(str(self.total))
-
-        total_iters = self.start_iters + self.n_iters
-        pct_complete = int(total_iters / self.total * self.bar_length)
-
-        bar = f"{self.verb} {total_iters:<{n_digits}} / {self.total} ["
-        bar = bar + "=" * pct_complete + " " * (self.bar_length - pct_complete) + "]"
-
-        time_info = f"elapsed: {elapse_min}:{elapse_sec}, "
-        time_info += f"remaining: {remain_min}:{remain_sec}, "
-
-        if iter_per_sec < 1:
-            time_info += f"{1 / iter_per_sec:0.2f}sec/iter"
-        else:
-            time_info += f"{iter_per_sec:0.2f}iter/sec"
-
-        complete = self.n_iters == self.total
-        print(bar, time_info, end="\n" if complete else "\r")
-        self.last_print_time = time.time()
diff --git a/gEconpy/classes/time_aware_symbol.py b/gEconpy/classes/time_aware_symbol.py
index 846e250..9643e71 100644
--- a/gEconpy/classes/time_aware_symbol.py
+++ b/gEconpy/classes/time_aware_symbol.py
@@ -1,4 +1,5 @@
 import sympy as sp
+
 from sympy.core.cache import cacheit
 
 
@@ -6,6 +7,7 @@ class TimeAwareSymbol(sp.Symbol):
     __slots__ = ("time_index", "base_name", "__dict__")
     time_index: int | str
     base_name: str
+    safe_name: str
 
     def __new__(cls, name, time_index, **assumptions):
         cls._sanitize(assumptions, cls)
@@ -48,14 +50,14 @@ def _create_name_from_time_index(self):
         if idx == "ss":
             time_name = rf"{name}_{idx}"
         elif idx == "0":
-            time_name = r"%s_t" % name
+            time_name = rf"{name}_t"
         else:
             time_name = rf"{name}_t{operator}{idx}"
 
         return time_name
 
     def _hashable_content(self):
-        return super()._hashable_content() + (self.time_index,)
+        return (*super()._hashable_content(), self.time_index)
 
     def __getnewargs_ex__(self):
         return (
diff --git a/gEconpy/classes/transformers.py b/gEconpy/classes/transformers.py
deleted file mode 100644
index d89408d..0000000
--- a/gEconpy/classes/transformers.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from abc import ABC
-
-import numpy as np
-from scipy.special import expit
-
-
-class Transformer(ABC):
-    def constrain(self, x):
-        raise NotImplementedError
-
-    def unconstrain(self, x):
-        raise NotImplementedError
-
-
-class IdentityTransformer(Transformer):
-    def constrain(self, x):
-        return x
-
-    def unconstrain(self, x):
-        return x
-
-
-class PositiveTransformer(Transformer):
-    def __init__(self):
-        self.last_sign = 1
-
-    def constrain(self, x):
-        self.last_sign = np.sign(x)
-        return x**2
-
-    def unconstrain(self, x):
-        return x**0.5 * self.last_sign
-
-
-class IntervalTransformer(Transformer):
-    def __init__(self, low=0, high=1, slope=1):
-        self.low = low
-        self.high = high
-        self.slope = slope
-        self.eps = 1e-8
-
-    def constrain(self, x):
-        sigmoid_x = expit(self.slope * x)
-        return sigmoid_x * self.high + (1 - sigmoid_x) * self.low
-
-    def unconstrain(self, x):
-        low, high, k, eps = self.low, self.high, self.slope, self.eps
-        return np.log((x - low + eps) / (high - x + eps)) / k
diff --git a/gEconpy/dynare_convert.py b/gEconpy/dynare_convert.py
new file mode 100644
index 0000000..9169a8e
--- /dev/null
+++ b/gEconpy/dynare_convert.py
@@ -0,0 +1,315 @@
+from functools import reduce
+from typing import TYPE_CHECKING
+
+import sympy as sp
+
+from sympy.core import Mul, Pow, Rational, S
+from sympy.core.mul import _keep_coeff
+from sympy.core.numbers import equal_valued
+from sympy.printing.octave import OctaveCodePrinter, precedence
+
+from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
+
+if TYPE_CHECKING:
+    from gEconpy.model.model import Model
+
+OPERATORS = list("+-/*^()=")
+
+
+class DynareCodePrinter(OctaveCodePrinter):
+    def __init__(self, settings=None):
+        settings = {} if settings is None else settings
+        super().__init__(settings)
+
+    def _print_Mul(self, expr):
+        # print complex numbers nicely in Octave
+        if expr.is_number and expr.is_imaginary and (S.ImaginaryUnit * expr).is_Integer:
+            return f"{self._print(-S.ImaginaryUnit * expr)}i"
+
+        # cribbed from str.py
+        prec = precedence(expr)
+
+        c, e = expr.as_coeff_Mul()
+        if c < 0:
+            expr = _keep_coeff(-c, e)
+            sign = "-"
+        else:
+            sign = ""
+
+        a = []  # items in the numerator
+        b = []  # items that are in the denominator (if any)
+
+        pow_paren = []  # Will collect all pow with more than one base element and exp = -1
+
+        if self.order not in ("old", "none"):
+            args = expr.as_ordered_factors()
+        else:
+            # use make_args in case expr was something like -x -> x
+            args = Mul.make_args(expr)
+
+        # Gather args for numerator/denominator
+        for item in args:
+            if (
+                item.is_commutative
+                and item.is_Pow
+                and item.exp.is_Rational
+                and item.exp.is_negative
+            ):
+                if item.exp != -1:
+                    b.append(Pow(item.base, -item.exp, evaluate=False))
+                else:
+                    if len(item.args[0].args) != 1 and isinstance(
+                        item.base, Mul
+                    ):  # To avoid situations like #14160
+                        pow_paren.append(item)
+                    b.append(Pow(item.base, -item.exp))
+            elif item.is_Rational and item is not S.Infinity:
+                if item.p != 1:
+                    a.append(Rational(item.p))
+                if item.q != 1:
+                    b.append(Rational(item.q))
+            else:
+                a.append(item)
+
+        a = a or [S.One]
+
+        a_str = [self.parenthesize(x, prec) for x in a]
+        b_str = [self.parenthesize(x, prec) for x in b]
+
+        # To parenthesize Pow with exp = -1 and having more than one Symbol
+        for item in pow_paren:
+            if item.base in b:
+                b_str[b.index(item.base)] = f"({b_str[b.index(item.base)]})"
+
+        def multjoin(a, a_str):
+            # here we probably are assuming the constants will come first
+            r = a_str[0]
+            for i in range(1, len(a)):
+                mulsym = " * " if not expr.is_Matrix else " .* "
+                r = r + mulsym + a_str[i]
+            return r
+
+        if not b:
+            return sign + multjoin(a, a_str)
+        elif len(b) == 1:
+            divsym = " / " if not expr.is_Matrix else " ./ "
+            return sign + multjoin(a, a_str) + divsym + b_str[0]
+        else:
+            divsym = " / " if not expr.is_Matrix else " ./ "
+            return sign + multjoin(a, a_str) + divsym + f"({multjoin(b, b_str)})"
+
+    def _print_Pow(self, expr):
+        powsymbol = " ^ "
+
+        PREC = precedence(expr)
+
+        if equal_valued(expr.exp, 0.5):
+            return f"sqrt({self._print(expr.base)})"
+
+        if expr.is_commutative:
+            if equal_valued(expr.exp, -0.5):
+                sym = " / " if not expr.is_Matrix else " ./ "
+                return "1" + sym + f"sqrt({self._print(expr.base)})"
+            if equal_valued(expr.exp, -1):
+                sym = " / " if not expr.is_Matrix else " ./ "
+                return "1" + sym + f"{self.parenthesize(expr.base, PREC)}"
+
+        return f"{self.parenthesize(expr.base, PREC)}{powsymbol}{self.parenthesize(expr.exp, PREC)}"
+
+    def _print_TimeAwareSymbol(self, expr):
+        name = expr.base_name
+        t = expr.time_index
+
+        if t == "ss":
+            return f"{name}_{t}"
+        elif t == 0:
+            return f"{name}"
+        elif t > 0:
+            return f"{name}(+{t})"
+
+        return f"{name}({t})"
+
+
+def write_lines_from_list(items_to_write, linewidth=100, line_start=""):
+    lines = []
+    line = line_start
+
+    for item in items_to_write:
+        addition = f", {item}" if line != line_start else f" {item}"
+
+        # Add 1 to account for the final semicolon
+        if (len(line) + len(addition) + 1) > linewidth:
+            lines.append(line + ";")  # Add semicolon to complete the line
+            line = f"{line_start} {item}"
+        else:
+            line += addition
+
+    lines.append(line + ";")  # Add the final line with a semicolon
+    return "\n".join(lines)
+
+
+def write_variable_declarations(mod: "Model", linewidth=100):
+    var_names = [var.base_name for var in mod.variables]
+    return write_lines_from_list(var_names, linewidth=linewidth, line_start="var")
+
+
+def write_shock_declarations(mod: "Model", linewidth=100):
+    shock_names = [shock.base_name for shock in mod.shocks]
+    return write_lines_from_list(shock_names, linewidth=linewidth, line_start="varexo")
+
+
+def write_values_from_dict(d, round: int = 3):
+    out = ""
+    for name, value in d.items():
+        out += f"{name} = {value:0.{round}f};\n"
+    return out
+
+
+def write_param_names(mod: "Model", linewidth=100):
+    param_names = [param.name for param in mod.params]
+    param_string = write_lines_from_list(
+        param_names, linewidth=linewidth, line_start="parameters"
+    )
+
+    return param_string
+
+
+def write_parameter_declarations(mod: "Model", linewidth=100):
+    param_string = write_param_names(mod, linewidth=linewidth)
+    param_string += "\n\n"
+    param_string += write_values_from_dict(mod.parameters().to_string())
+
+    return param_string
+
+
+def find_ss_variables(mod: "Model"):
+    variables = reduce(
+        lambda s, eq: s.union(set(eq.free_symbols)), mod.equations, set()
+    )
+
+    return sorted(
+        [
+            x
+            for x in variables
+            if isinstance(x, TimeAwareSymbol) and (x.time_index == "ss")
+        ],
+        key=lambda x: x.base_name,
+    )
+
+
+def write_model_equations(mod: "Model"):
+    printer = DynareCodePrinter()
+
+    required_ss_values = find_ss_variables(mod)
+    defined_ss_values = [x.lhs for x in mod.steady_state_relationships]
+
+    if not all(ss_var in defined_ss_values for ss_var in required_ss_values):
+        ss_values = mod.steady_state(verbose=False, progressbar=False).to_sympy()
+        ss_dict = {k.name: v for k, v in ss_values.items() if k in required_ss_values}
+    else:
+        ss_dict = {
+            eq.lhs: eq.rhs
+            for eq in mod.steady_state_relationships
+            if eq.lhs in required_ss_values
+        }
+        ss_dict = {k.name: printer.doprint(v) for k, v in ss_dict.items()}
+
+    model_block = "model;\n\n"
+    for k, v in ss_dict.items():
+        model_block += f"#{k} = {v};\n"
+
+    model_block += "\n".join([printer.doprint(eq) + ";" for eq in mod.equations])
+    model_block += "\n\nend;"
+
+    return model_block
+
+
+def write_steady_state(mod: "Model", use_cse=True):
+    printer = DynareCodePrinter()
+
+    # Check for a full analytic steady state. If available, we can write a
+    # steady_state_model block
+    if len(mod.steady_state_relationships) == len(mod.variables):
+        out = "steady_state_model;\n"
+        eqs = mod.steady_state_relationships
+        if use_cse:
+            cse, eqs = sp.cse(eqs)
+            for var, expr in cse:
+                out += f"{var} = {printer.doprint(expr)};\n"
+            out += "\n\n"
+        for eq in eqs:
+            out += f"{eq.lhs.base_name} = {printer.doprint(eq.rhs)};\n"
+
+        out += "\n\nend;"
+
+    # Otherwise solve for a numeric steady state and use that as initial values to Dynare
+    else:
+        out = "initval;\n"
+        steady_state = mod.steady_state(verbose=False, progressbar=False)
+        ss_dict = {k.base_name: v for k, v in steady_state.to_sympy().items()}
+        out += write_values_from_dict(ss_dict)
+        out += "\nend;"
+
+    out += "\n\nsteady;\nresid;"
+    return out
+
+
+def write_shock_std(mod: "Model"):
+    out = "shocks;\n"
+    shock_names = [shock.base_name for shock in mod.shocks]
+
+    for shock in shock_names:
+        out += f"var {shock};\nstderr 0.01;\n\n"
+
+    out += "end;"
+    return out
+
+
+def make_mod_file(
+    model: "Model", linewidth=100, use_cse: bool = True, out_path=None
+) -> str | None:
+    """
+    Generate a string representation of a Dynare model file for a dynamic stochastic general equilibrium (DSGE) model.
+    For more information, see [1].
+
+    Parameters
+    ----------
+    model : Model
+        A DSGE model object
+    linewidth: int, default 100
+        Maximum number of characters per line before a break is insterted
+    use_cse: bool, default True
+        If True, use ``sp.cse`` to identify common sub expressions in the analytic steady state and rewrite equations
+        in terms of these sub expressions. This can make the steady state block more readable and provide modest
+        performance increase for large models.
+    out_path: str, optional
+        If None, the generated mod file is printed to the terminal. Otherwise, it is written to ``out_path``.
+
+    Returns
+    -------
+    str
+        A string representation of a Dynare model file.
+
+    References
+    ----------
+    ..[1] Adjemian, Stéphane, et al. "Dynare: Reference manual, version 4." (2011).
+    """
+
+    mod_blocks = [
+        write_variable_declarations(model, linewidth=linewidth),
+        write_shock_declarations(model, linewidth=linewidth),
+        write_parameter_declarations(model, linewidth=linewidth),
+        write_model_equations(model),
+        write_steady_state(model, use_cse=use_cse),
+        "check(qz_zero_threshold=1e-20);",
+        write_shock_std(model),
+        "stoch_simul(order=1, irf=100, qz_zero_threshold=1e-20);",
+    ]
+
+    mod_file = "\n\n".join(mod_blocks)
+
+    if out_path is None:
+        return mod_file
+
+    with open(out_path, "w") as f:
+        f.write(mod_file)
diff --git a/gEconpy/estimation/estimate.py b/gEconpy/estimation/estimate.py
deleted file mode 100644
index 16072f2..0000000
--- a/gEconpy/estimation/estimate.py
+++ /dev/null
@@ -1,354 +0,0 @@
-from typing import Any
-
-import numpy as np
-from numpy.typing import ArrayLike
-from scipy import stats
-
-from gEconpy.estimation.estimation_utilities import (
-    build_system_matrices,
-    check_bk_condition,
-    check_finite_matrix,
-)
-from gEconpy.estimation.kalman_filter import kalman_filter
-from gEconpy.shared.utilities import split_random_variables
-from gEconpy.solvers.cycle_reduction import cycle_reduction, solve_shock_matrix
-
-
-def build_and_solve(
-    param_dict: dict, sparse_datas: list, vars_to_estimate: list | None = None
-) -> tuple[ArrayLike, ArrayLike, ArrayLike]:
-    """
-    A collection of functionality already in the gEcon model object, extracted for speed and memory optimizations
-    when doing parallel fitting. Specifically, this function avoids the need of passing around the (potentially large)
-    model object when repeatedly solving the perturbation problem for policy matrices T and R.
-
-    Parameters
-    ----------
-    param_dict: dictionary of string keys float values
-        A dictionary that maps parameters to be estimated to point values.
-    sparse_datas: list of tuples
-        A list of the equations and CSR indicies needed to construct the A, B, C, and D matrices necessary to solve for
-        T and R.
-    vars_to_estimate: list of strings, default: None
-        The subset of variables to be estimated for. When None, the variable list is assumed to be those assigned
-        priors in the GCN file. This should only be used for debugging.
-
-    Returns
-    -------
-    T: array
-        The "policy matrix" describing how the linear system evolves with time
-    R: array
-        The "selection matrix" describing how exogenous shocks enter into the linear system
-    success: bool
-        A flag indicating whether the system has been successfully solved. This encodes three conditions: successful
-        converge of the perturbation algorithm, the size of the deterministic and stochastic norms of the
-        linear solution, and the blanchard-khan conditions.
-
-    TODO: njit this function by figuring out how to get rid of the sympy lambdify functions inside sparse_datas
-    """
-
-    res = build_system_matrices(
-        param_dict, sparse_datas, vars_to_estimate=vars_to_estimate
-    )
-    A, B, C, D = res
-
-    if not all([check_finite_matrix(x) for x in res]):
-        T = np.zeros_like(A)
-        R = np.zeros((T.shape[0], 1))
-        success = False
-        return T, R, success
-
-    bk_condition_met = check_bk_condition(A, B, C, tol=1e-8)
-
-    try:
-        T, result, log_norm = cycle_reduction(A, B, C, 1000, 1e-8, False)
-        R = solve_shock_matrix(B, C, D, T)
-    except np.linalg.LinAlgError:
-        T = np.zeros_like(A)
-        R = np.zeros((T.shape[0], 1))
-        success = False
-        return T, R, success
-
-    success = (
-        (result == "Optimization successful") & (log_norm < 1e-8) & bk_condition_met
-    )
-
-    T = np.ascontiguousarray(T)
-    R = np.ascontiguousarray(R)
-
-    return T, R, success
-
-
-def build_Z_matrix(obs_variables: list[str], state_variables: list[str]) -> np.ndarray:
-    """Constructs the design matrix Z for a state-space system.
-
-    Parameters
-    ----------
-    obs_variables : List[str]
-        The names of the observed variables.
-    state_variables : List[str]
-        The names of the state variables.
-
-    Returns
-    -------
-    Z : np.ndarray
-        The design matrix Z.
-    """
-
-    Z = np.array(
-        [[x == var for x in state_variables] for var in obs_variables], dtype="float64"
-    )
-    return Z
-
-
-def build_Q_and_H(
-    state_sigmas: dict[str, float],
-    shock_variables: list[str],
-    obs_variables: list[str],
-    obs_sigmas: dict[str, float] | None = None,
-) -> tuple[np.ndarray, np.ndarray]:
-    """
-    Build the Q and H matrices for a state-space system.
-
-    Parameters
-    ----------
-    state_sigmas : Dict[str, float]
-        A dictionary of variances associated with shocks in the state-space system.
-    shock_variables : List[str]
-        A list of strings representing shocks.
-    obs_variables : List[str]
-        A list of strings representing the observed variables.
-    obs_sigmas : Optional[Dict[str, float]]
-        A dictionary of variances associated with observed variables. If not provided, all variances are set to 0.
-
-    Returns
-    -------
-    Tuple[np.ndarray, np.ndarray]
-        A tuple containing the Q and H matrices.
-    """
-
-    k_posdef = len(shock_variables)
-    k_obs = len(obs_variables)
-
-    obs_sigmas = obs_sigmas or {}
-
-    i = 0
-    Q = np.zeros((k_posdef, k_posdef))
-    for v in shock_variables:
-        if v in state_sigmas.keys():
-            Q[i, i] = state_sigmas[v]
-        i += 1
-
-    i = 0
-    H = np.zeros((k_obs, k_obs))
-    for v in obs_variables:
-        if v in obs_sigmas.keys():
-            H[i, i] = obs_sigmas[v]
-        i += 1
-
-    Q = np.ascontiguousarray(Q)
-    H = np.ascontiguousarray(H)
-
-    return Q, H
-
-
-def evaluate_prior_logp(
-    all_param_dict: dict[str, float], prior_dict: dict[str, stats.rv_continuous]
-) -> float:
-    """
-    Evaluate the log probability density function (PDF) of the prior distribution for a given set of parameters.
-
-    Parameters
-    ----------
-    all_param_dict : dict
-        A dictionary containing the parameters (float values) for which the prior log PDF is to be evaluated.
-    prior_dict : dict
-        A dictionary containing the prior distributions (scipy.stats continuous random variables) for each parameter.
-
-    Returns
-    -------
-    float
-        The log probability of the parameters under the prior distribution.
-    """
-    ll = 0
-
-    for k, v in all_param_dict.items():
-        ll += prior_dict[k].logpdf(v).sum()
-
-    return ll
-
-
-def split_param_dict(
-    all_param_dict: dict[str, float],
-) -> tuple[dict[str, float], dict[str, float], dict[str, float]]:
-    """
-    Split a dictionary of parameters into three dictionaries based on their keys.
-
-    Parameters
-    ----------
-    all_param_dict : dict
-        A dictionary of parameters to be split.
-
-    Returns
-    -------
-    tuple
-        A tuple containing
-        (1) a dictionary of deep parameters,
-        (2) a numpy array containing the initial conditions for the state vector, and
-        (3) a numpy array containing the initial conditions for the state variance-covariance matrix.
-    """
-    param_dict = {}
-    a0_dict = {}
-    P0_dict = {}
-
-    initial_names = [x for x in all_param_dict.keys() if x.endswith("__initial")]
-    initial_cov_names = [
-        x for x in all_param_dict.keys() if x.endswith("__initial_cov")
-    ]
-
-    for k, v in all_param_dict.items():
-        if k in initial_names:
-            a0_dict[k] = v
-        elif k in initial_cov_names:
-            P0_dict[k] = v
-        else:
-            param_dict[k] = v
-
-    return param_dict, a0_dict, P0_dict
-
-
-def evaluate_logp(
-    all_param_dict: dict[str, Any],
-    df: np.ndarray,
-    sparse_datas: np.ndarray,
-    Z: np.ndarray,
-    priors: dict[str, Any],
-    shock_names: list[str],
-    observed_vars: list[str],
-    filter_type: str = "standard",
-) -> tuple[float, np.ndarray]:
-    """
-    Evaluate the log likelihood of a log-linearized DSGE model using the Kalman Filter.
-
-    Parameters
-    ----------
-    all_param_dict : dict
-        A dictionary of all parameters for the model.
-    df : pd.DataFrame
-        A 2D array of data for which the log probability should be computed.
-    sparse_datas : numpy array
-        A 3D array of sparse matrices for the model.
-    Z : numpy array
-        A 2D array of measurement errors for the model.
-    priors : dict
-        A dictionary of prior distributions for each parameter.
-    shock_names : list
-        A list of names of shocks in the model.
-    observed_vars : list
-        A list of names of observed variables in the model.
-    filter_type : str, optional
-        The type of Kalman Filter to use. The default is 'standard'.
-
-    Returns
-    -------
-    tuple
-        A tuple containing (1) the log likelihood of the model and (2) an array of log likelihoods for each observation.
-    """
-
-    ll = evaluate_prior_logp(all_param_dict, priors)
-    param_dict, a0_dict, P0_dict = split_param_dict(all_param_dict)
-
-    if not np.isfinite(ll):
-        return -np.inf, np.zeros(df.shape[0])
-
-    param_dict, shock_dict, obs_dict = split_random_variables(
-        param_dict, shock_names, observed_vars
-    )
-
-    T, R, success = build_and_solve(param_dict, sparse_datas)
-
-    if not success:
-        return -np.inf, np.zeros(df.shape[0])
-
-    a0 = np.array(list(a0_dict.values()))[:, None] if len(a0_dict) > 0 else None
-    P0 = (
-        np.eye(len(P0_dict)) * np.array(list(P0_dict.keys()))
-        if len(P0_dict) > 0
-        else None
-    )
-
-    Q, H = build_Q_and_H(shock_dict, shock_names, observed_vars, obs_dict)
-
-    *_, ll_obs = kalman_filter(
-        df.values, T, Z, R, H, Q, a0, P0, filter_type=filter_type
-    )
-    ll += ll_obs.sum()
-
-    return ll, ll_obs
-
-
-def evaluate_logp2(
-    all_param_dict,
-    data,
-    f_ss,
-    f_pert,
-    free_params,
-    Z,
-    priors,
-    shock_names,
-    observed_vars,
-    filter_type="standard",
-):
-    ll = evaluate_prior_logp(all_param_dict, priors)
-    param_dict, a0_dict, P0_dict = split_param_dict(all_param_dict)
-
-    if not np.isfinite(ll):
-        return -np.inf, np.zeros(data.shape[0])
-
-    param_dict, shock_dict, obs_dict = split_random_variables(
-        param_dict, shock_names, observed_vars
-    )
-
-    all_params = free_params.copy()
-    all_params.update(param_dict)
-
-    ss_results = f_ss(all_params)
-    if not ss_results["success"]:
-        return -np.inf, np.zeros(data.shape[0])
-
-    ss_values = ss_results["ss_dict"]
-    calib_dict = ss_results["calib_dict"]
-
-    endog = np.array(list(ss_values.values()))
-    exog = np.array(list((all_params | calib_dict).values()))
-
-    A, B, C, D = f_pert(exog, endog)
-
-    if any([np.any(~np.isfinite(X)) for X in [A, B, C, D]]):
-        return -np.inf, np.zeros(data.shape[0])
-
-    try:
-        T, result, log_norm = cycle_reduction(A, B, C, verbose=False)
-        T = np.ascontiguousarray(T)
-    except np.linalg.LinAlgError:
-        T = None
-        result = "Failed"
-
-    if result != "Optimization successful":
-        return -np.inf, np.zeros(data.shape[0])
-
-    R = solve_shock_matrix(B, C, D, T)
-
-    a0 = np.array(list(a0_dict.values()))[:, None] if len(a0_dict) > 0 else None
-    P0 = (
-        np.eye(len(P0_dict)) * np.array(list(P0_dict.keys()))
-        if len(P0_dict) > 0
-        else None
-    )
-
-    Q, H = build_Q_and_H(shock_dict, shock_names, observed_vars, obs_dict)
-
-    *_, ll_obs = kalman_filter(data, T, Z, R, H, Q, a0, P0, filter_type=filter_type)
-    ll += ll_obs.sum()
-
-    return ll, ll_obs
diff --git a/gEconpy/estimation/estimation_utilities.py b/gEconpy/estimation/estimation_utilities.py
deleted file mode 100644
index 5e0ce18..0000000
--- a/gEconpy/estimation/estimation_utilities.py
+++ /dev/null
@@ -1,340 +0,0 @@
-from collections.abc import Callable
-
-import numba as nb
-import numpy as np
-import sympy as sp
-from scipy import linalg
-
-
-@nb.njit
-def check_finite_matrix(a):
-    for v in np.nditer(a):
-        if not np.isfinite(v.item()):
-            return False
-    return True
-
-
-def numba_lambdify_scalar(inputs, expr, sig):
-    """
-    Convert a sympy expression into a Numba-compiled function.
-
-    Parameters
-    ----------
-    inputs : List[str]
-        A list of strings containing the names of the variables in the expression.
-    expr : sympy.Expr
-        The sympy expression to be converted.
-
-    Returns
-    -------
-    numba.types.function
-        A Numba-compiled function equivalent to the input expression.
-
-    Notes
-    -----
-    The function returned by this function is pickleable.
-    """
-    code = sp.printing.ccode(expr)
-    # The code string will contain a single line, so we add line breaks to make it a valid block of code
-    code = "@nb.njit('{}')\ndef f({}):\n{}\n    return {}".format(
-        sig, ",".join(inputs), " " * 4, code
-    )
-    # Compile the code and return the resulting function
-    exec(code)
-    return locals()["f"]
-
-
-def extract_sparse_data_from_model(
-    model, params_to_estimate: list | None = None
-) -> list:
-    """
-    Extract sparse data from a DSGE model.
-
-    Parameters
-    ----------
-    model : object
-        A gEconpy model object.
-    params_to_estimate : list, optional
-        A list of variables to estimate. The default is None, which estimates all variables.
-
-    Returns
-    -------
-    list
-        A list of sparse data.
-    """
-
-    if params_to_estimate is None:
-        params_to_estimate = list(model.param_priors.keys())
-    ss_vars = list(model.steady_state_dict.to_sympy().keys())
-
-    param_dict = model.free_param_dict.copy()
-    ss_sub_dict = model.steady_state_relationships.copy()
-    calib_dict = model.calib_param_dict.copy()
-
-    requires_numeric_solution = [x for x in ss_vars if x not in ss_sub_dict.to_sympy()]
-
-    not_estimated_dict = param_dict.copy()
-    for k in param_dict.keys():
-        if k in params_to_estimate:
-            del not_estimated_dict[k]
-
-    names = ["A", "B", "C", "D"]
-    A, B, C, D = (x.tolist() for x in model._perturbation_setup(return_F_matrices=True))
-
-    inputs = params_to_estimate + requires_numeric_solution
-    # n_inputs = len(inputs)
-
-    # signature_str = f"float64({', '.join(['float64'] * n_inputs)})"
-    # function_sig = nb.types.FunctionType(nb.types.float64(*(nb.types.float64,) * n_inputs))
-    #
-    # sparse_datas = nb.typed.List()
-    sparse_datas = []
-
-    for name, matrix in zip(names, [A, B, C, D]):
-        # data = nb.typed.List.empty_list(function_sig)
-        # idxs = nb.typed.List()
-        # pointers = nb.typed.List([0])
-
-        data = []
-        idxs = []
-        pointers = [0]
-
-        for row in matrix:
-            for i, value in enumerate(row):
-                if value != 0:
-                    expr = (
-                        value.subs(ss_sub_dict.to_sympy())
-                        .subs(calib_dict.to_sympy())
-                        .subs(not_estimated_dict.to_sympy())
-                    )
-                    # numba_func = numba_lambdify_scalar(inputs, expr, signature_str)
-                    func = sp.lambdify(inputs, expr)
-                    # data.append(numba_func)
-                    data.append(func)
-                    idxs.append(i)
-            pointers.append(len(idxs))
-
-        shape = (len(matrix), len(matrix[0]))
-        sparse_datas.append((data, idxs, pointers, shape))
-
-    return sparse_datas
-
-
-# @nb.njit
-def matrix_from_csr_data(
-    data: np.ndarray, indices: np.ndarray, idxptrs: np.ndarray, shape: tuple[int, int]
-) -> np.ndarray:
-    """
-    Convert a CSR matrix into a dense numpy array.
-
-    Parameters
-    ----------
-    data : np.ndarray
-        The data stored in the CSR matrix.
-    indices : np.ndarray
-        The column indices for the non-zero values in `data`.
-    idxptrs : np.ndarray
-        The index pointers for the CSR matrix.
-    shape : tuple[int, int]
-        The shape of the dense matrix to create.
-
-    Returns
-    -------
-    np.ndarray
-        The dense matrix representation of the CSR matrix.
-    """
-    out = np.zeros(shape)
-    for i in range(shape[0]):
-        start = idxptrs[i]
-        end = idxptrs[i + 1]
-        s = slice(start, end)
-        d_idx = range(start, end)
-        col_idxs = indices[s]
-        for j, d in zip(col_idxs, d_idx):
-            out[i, j] = data[d]
-
-    return out
-
-
-def build_system_matrices(
-    param_dict: dict[str, float],
-    sparse_datas: list[tuple[Callable, np.ndarray, np.ndarray, tuple[int, int]]],
-    vars_to_estimate: list[str] | None = None,
-) -> list[np.ndarray]:
-    """
-    Build system matrices for a DSGE model.
-
-    This function builds the A, B, C, and D matrices for a DSGE model given a set of parameters
-    and pre-computed sparse data.
-
-    Parameters
-    ----------
-    param_dict : dict
-        Dictionary of parameter values
-    sparse_datas : list of tuples
-        List of tuples, each tuple representing sparse data for a single matrix. The tuple contains the following
-        elements:
-        data : numpy array
-            Array of values to be placed in the matrix
-        indices : numpy array
-            Array of column indices for the non-zero values in the matrix
-        idxptrs : numpy array
-            Array of row pointers for the non-zero values in the matrix
-        shape : tuple
-            Shape of the matrix as a tuple (n_rows, n_cols)
-    vars_to_estimate : list of str, optional
-        List of parameter names to use in building the matrices, by default None
-    Returns
-    -------
-    list of numpy arrays
-        List of matrices A, B, C, and D
-    """
-
-    result = []
-    if vars_to_estimate:
-        params_to_use = {
-            k: v for k, v in param_dict.to_string().items() if k in vars_to_estimate
-        }
-    else:
-        params_to_use = param_dict.to_string()
-
-    for sparse_data in sparse_datas:
-        fs, indices, idxptrs, shape = sparse_data
-        data = np.zeros(len(fs))
-        i = 0
-        for f in fs:
-            data[i] = f(**params_to_use)
-            i += 1
-        M = matrix_from_csr_data(data, indices, idxptrs, shape)
-        result.append(M)
-    return result
-
-
-@nb.njit
-def compute_eigenvalues(A, B, C, tol=1e-8):
-    """
-    Given the log-linearized coefficient matrices A, B, and C at times t-1, t, and t+1 respectively, compute the
-    eigenvalues of the DSGE system. These eigenvalues are used to determine stability of the DSGE system.
-
-    Parameters
-    ----------
-    A : np.ndarray
-        The log-linearized coefficient matrix of the DSGE system at time t-1
-    B : np.ndarray
-        The log-linearized coefficient matrix of the DSGE system at time t
-    C : np.ndarray
-        The log-linearized coefficient matrix of the DSGE system at time t+1
-    tol : float, optional
-        The tolerance used to check for stability, by default 1e-8
-
-    Returns
-    -------
-    np.ndarray
-        The eigenvalues of the DSGE system, sorted by the magnitude of the real part. Each row of the output array
-        contains the magnitude, real part, and imaginary part of an eigenvalue.
-    """
-
-    n_eq, n_vars = A.shape
-
-    lead_var_idx = np.where(np.sum(np.abs(C), axis=0) > tol)[0]
-
-    eqs_and_leads_idx = np.hstack((np.arange(n_vars), lead_var_idx + n_vars))
-
-    Gamma_0 = np.vstack(
-        (np.hstack((B, C)), np.hstack((-np.eye(n_eq), np.zeros((n_eq, n_eq)))))
-    )
-
-    Gamma_1 = np.vstack(
-        (
-            np.hstack((A, np.zeros((n_eq, n_eq)))),
-            np.hstack((np.zeros((n_eq, n_eq)), np.eye(n_eq))),
-        )
-    )
-    Gamma_0 = Gamma_0[eqs_and_leads_idx, :][:, eqs_and_leads_idx]
-    Gamma_1 = Gamma_1[eqs_and_leads_idx, :][:, eqs_and_leads_idx]
-
-    A, B, alpha, beta, Q, Z = linalg.ordqz(
-        -Gamma_0, Gamma_1, sort="ouc", output="complex"
-    )
-
-    gev = np.vstack((np.diag(A), np.diag(B))).T
-
-    eigenval = gev[:, 1] / (gev[:, 0] + tol)
-    pos_idx = np.where(np.abs(eigenval) > 0)
-    eig = np.zeros(((np.abs(eigenval) > 0).sum(), 3))
-    eig[:, 0] = np.abs(eigenval)[pos_idx]
-    eig[:, 1] = np.real(eigenval)[pos_idx]
-    eig[:, 2] = np.imag(eigenval)[pos_idx]
-
-    sorted_idx = np.argsort(eig[:, 0])
-
-    return eig[sorted_idx, :]
-
-
-@nb.njit
-def check_bk_condition(A, B, C, tol=1e-8):
-    """
-    Check the Blanchard-Kahn condition for the DSGE model specified by the log linearized coefficient matrices
-    A (t-1), B (t), and C (t+1).
-
-    This function computes the eigenvalues of the DSGE system and checks if the number of forward-looking variables
-    is less than or equal to the number of eigenvalues greater than 1. The Blanchard-Kahn condition ensures the
-    stability of the rational expectations equilibrium of the model.
-
-    Parameters
-    ----------
-    A : numpy.ndarray
-        The log-linearized coefficient matrix at time t-1
-    B : numpy.ndarray
-        The log-linearized coefficient matrix at time t
-    C : numpy.ndarray
-        The log-linearized coefficient matrix at time t+1
-    tol : float, optional
-        The tolerance for eigenvalues that are considered equal to 1, by default 1e-8
-
-    Returns
-    -------
-    bool
-        True if the Blanchard-Kahn condition is satisfied, else False
-
-    References
-    ----------
-    Blanchard, Olivier Jean, and Charles M. Kahn. "The solution of linear difference models under rational
-    expectations." Econometrica: Journal of the Econometric Society (1980): 1305-1311.
-    """
-
-    n_forward = int((C.sum(axis=0) > 0).sum())
-
-    try:
-        eig = compute_eigenvalues(A, B, C, tol)
-    # TODO: ValueError is the correct exception to raise here, but numba complains
-    except Exception:
-        return False
-
-    n_g_one = (eig[:, 0] > 1).sum()
-    return n_forward <= n_g_one
-
-
-def extract_prior_dict(model):
-    """
-    Extract the prior distributions from a gEconModel object.
-
-    Parameters
-    ----------
-    model : gEconModel
-        The gEconModel object to extract priors from.
-
-    Returns
-    -------
-    prior_dict : dict
-        A dictionary containing the prior distributions for the model's parameters, shocks, and observation noise.
-    """
-    prior_dict = {}
-
-    prior_dict.update(model.param_priors)
-    prior_dict.update(
-        {k: model.shock_priors[k].rv_params["scale"] for k in model.shock_priors.keys()}
-    )
-    prior_dict.update(model.observation_noise_priors)
-
-    return prior_dict
diff --git a/gEconpy/estimation/kalman_filter.py b/gEconpy/estimation/kalman_filter.py
deleted file mode 100644
index 3df222f..0000000
--- a/gEconpy/estimation/kalman_filter.py
+++ /dev/null
@@ -1,319 +0,0 @@
-import numpy as np
-from numba import njit
-from numpy.typing import ArrayLike
-from scipy import linalg
-
-from gEconpy.numba_tools.overloads import solve_triangular_impl  #  noqa
-
-MVN_CONST = np.log(2.0 * np.pi)
-EPS = 1e-12
-
-
-@njit("float64[:, ::1](boolean[::1])")
-def build_mask_matrix(nan_mask: ArrayLike) -> ArrayLike:
-    """
-    The Kalman Filter can "natively" handle missing values by treating observed states as un-observed states for
-    iterations where data is not available. To do this, the Z and H matrices must be modified. This function creates
-    a matrix W such that W @ Z and W @ H have zeros where data is missing.
-    Parameters
-    ----------
-    nan_mask: array
-        A 1d array of boolean flags of length n, indicating whether a state is observed in the current iteration.
-    Returns
-    -------
-    W: array
-        An n x n matrix used to mask missing values in the Z and H matrices
-    """
-    n = nan_mask.shape[0]
-    W = np.eye(n)
-    i = 0
-    for flag in nan_mask:
-        if flag:
-            W[i, i] = 0
-        i += 1
-
-    W = np.ascontiguousarray(W)
-
-    return W
-
-
-@njit
-def standard_kalman_filter(
-    data: ArrayLike,
-    T: ArrayLike,
-    Z: ArrayLike,
-    R: ArrayLike,
-    H: ArrayLike,
-    Q: ArrayLike,
-    a0: ArrayLike,
-    P0: ArrayLike,
-) -> tuple:
-    """
-    Parameters
-    ----------
-    data: array
-        (T, k_observed) matrix of observed data. Data can include missing values.
-    a0: array
-        (k_states, 1) vector of initial states.
-    P0: array
-        (k_states, k_states) initial state covariance matrix
-    T: array
-        (k_states, k_states) transition matrix
-    Z: array
-        (k_states, k_observed) design matrix
-    R: array
-    H: array
-    Q: array
-    Returns
-    -------
-    """
-    n_steps, k_obs = data.shape
-    k_states, k_posdef = R.shape
-
-    filtered_states = np.zeros((n_steps, k_states))
-    predicted_states = np.zeros((n_steps + 1, k_states))
-    filtered_cov = np.zeros((n_steps, k_states, k_states))
-    predicted_cov = np.zeros((n_steps + 1, k_states, k_states))
-    log_likelihood = np.zeros(n_steps)
-
-    a = a0
-    P = P0
-
-    predicted_states[0] = a
-    predicted_cov[0] = P
-
-    for i in range(n_steps):
-        a_filtered, a_hat, P_filtered, P_hat, ll = kalman_step(
-            data[i].copy(), a, P, T, Z, R, H, Q
-        )
-
-        filtered_states[i] = a_filtered[:, 0]
-        predicted_states[i + 1] = a_hat[:, 0]
-        filtered_cov[i] = P_filtered
-        predicted_cov[i + 1] = P_hat
-        log_likelihood[i] = ll[0]
-
-        a = a_hat
-        P = P_hat
-
-    return (
-        filtered_states,
-        predicted_states,
-        filtered_cov,
-        predicted_cov,
-        log_likelihood,
-    )
-
-
-@njit
-def kalman_step(y, a, P, T, Z, R, H, Q):
-    y = y.reshape(-1, 1)
-    nan_mask = np.isnan(y).ravel()
-    W = build_mask_matrix(nan_mask)
-
-    Z_masked = W @ Z
-    H_masked = W @ H
-    y_masked = y.copy()
-    y_masked[nan_mask] = 0.0
-
-    a_filtered, P_filtered, ll = filter_step(y_masked, Z_masked, H_masked, a, P)
-
-    a_hat, P_hat = predict(a=a_filtered, P=P_filtered, T=T, R=R, Q=Q)
-
-    return a_filtered, a_hat, P_filtered, P_hat, ll
-
-
-@njit(
-    "Tuple((float64[:, ::1], float64[:, ::1], float64[::1]))(float64[:, ::1], float64[:, ::1], float64[:, ::1], "
-    "float64[:, ::1], float64[:, ::1])"
-)
-def filter_step(y, Z, H, a, P):
-    v = y - Z @ a
-
-    PZT = P @ Z.T
-    F = Z @ PZT + H
-
-    # Special case for if everything is missing. Abort before failing to invert F
-    if np.all(Z == 0):
-        a_filtered = np.atleast_2d(a).reshape((-1, 1))
-        P_filtered = P
-        ll = np.zeros(v.shape[0])
-
-        return a_filtered, P_filtered, ll
-
-    F_chol = np.linalg.cholesky(F)
-    K = linalg.solve_triangular(
-        F_chol, linalg.solve_triangular(F_chol, PZT.T, lower=True), trans=1, lower=True
-    ).T
-
-    I_KZ = np.eye(K.shape[0]) - K @ Z
-
-    a_filtered = a + K @ v
-    P_filtered = I_KZ @ P @ I_KZ.T + K @ H @ K.T
-    P_filtered = 0.5 * (P_filtered + P_filtered.T)
-
-    inner_term = linalg.solve_triangular(
-        F_chol, linalg.solve_triangular(F_chol, v, lower=True), lower=True, trans=1
-    )
-    n = y.shape[0]
-    ll = (
-        -0.5 * (n * MVN_CONST + (v.T @ inner_term).ravel())
-        - np.log(np.diag(F_chol)).sum()
-    )
-
-    return a_filtered, P_filtered, ll
-
-
-@njit
-def predict(a, P, T, R, Q):
-    a_hat = T @ a
-
-    P_hat = T @ P @ T.T + R @ Q @ R.T
-    P_hat = 0.5 * (P_hat + P_hat.T)
-
-    return a_hat, P_hat
-
-
-@njit
-def univariate_kalman_filter(
-    data: ArrayLike,
-    T: ArrayLike,
-    Z: ArrayLike,
-    R: ArrayLike,
-    H: ArrayLike,
-    Q: ArrayLike,
-    a0: ArrayLike,
-    P0: ArrayLike,
-) -> tuple:
-    n_steps, k_obs = data.shape
-    k_states, k_posdef = R.shape
-
-    filtered_states = np.zeros((n_steps, k_states))
-    predicted_states = np.zeros((n_steps + 1, k_states))
-    filtered_cov = np.zeros((n_steps, k_states, k_states))
-    predicted_cov = np.zeros((n_steps + 1, k_states, k_states))
-    log_likelihood = np.zeros(n_steps)
-
-    a = a0
-    P = P0
-
-    predicted_states[0] = a[:, 0]
-    predicted_cov[0] = P
-
-    for i in range(n_steps):
-        a_filtered, a_hat, P_filtered, P_hat, ll = univariate_kalman_step(
-            data[i].copy(), a, P, T, Z, R, H, Q
-        )
-
-        filtered_states[i] = a_filtered[:, 0]
-        predicted_states[i + 1] = a_hat[:, 0]
-        filtered_cov[i] = P_filtered
-        predicted_cov[i + 1] = P_hat
-        log_likelihood[i] = ll
-
-        a = a_hat
-        P = P_hat
-
-    return (
-        filtered_states,
-        predicted_states,
-        filtered_cov,
-        predicted_cov,
-        log_likelihood,
-    )
-
-
-@njit
-def univariate_kalman_step(y, a, P, T, Z, R, H, Q):
-    y = y.reshape(-1, 1)
-    nan_mask = np.isnan(y).ravel()
-    W = build_mask_matrix(nan_mask)
-
-    Z_masked = W @ Z
-    H_masked = W @ H
-    y_masked = y.copy()
-    y_masked[nan_mask] = 0.0
-
-    a_filtered, P_filtered, ll = univariate_filter_step(
-        y_masked, Z_masked, H_masked, a, P
-    )
-
-    a_hat, P_hat = predict(a=a_filtered, P=P_filtered, T=T, R=R, Q=Q)
-
-    return a_filtered, a_hat, P_filtered, P_hat, ll
-
-
-@njit
-def univariate_filter_step(y_masked, Z_masked, H_masked, a, P):
-    """
-    Univariate step that avoids inverting the F matrix by filtering one state at a time. Good for when the H matrix
-    isn't full rank (all economics problems)!
-    """
-
-    n_states = y_masked.shape[0]
-    a_filtered = a.copy()
-    P_filtered = P.copy()
-    ll_row = np.zeros(n_states)
-
-    for i in range(n_states):
-        a_filtered, P_filtered, ll = univariate_inner_step(
-            y_masked[i], Z_masked[i, :], H_masked[i, i], a_filtered, P_filtered
-        )
-        ll_row[i] = ll[0]
-
-    ll = -0.5 * ((ll_row != 0).sum() * MVN_CONST + ll_row.sum())
-    P_filtered = 0.5 * (P_filtered + P_filtered.T)
-
-    return a_filtered, P_filtered, ll
-
-
-@njit
-def univariate_inner_step(y, Z_row, sigma_H, a, P):
-    Z_row = np.atleast_2d(Z_row)
-    v = y - Z_row @ a
-
-    PZT = P @ Z_row.T
-    F = Z_row @ PZT + sigma_H
-
-    if F < EPS:
-        a_filtered = a
-        P_filtered = P
-        ll = np.zeros(v.shape[0])
-        return a_filtered, P_filtered, ll.ravel()
-
-    K = PZT / F
-    a_filtered = a + K * v
-    P_filtered = P - np.outer(K, K) * F
-    ll = np.log(F) + v**2 / F
-
-    return a_filtered, P_filtered, ll.ravel()
-
-
-@njit(
-    "Tuple((float64[:, ::1], float64[:, ::1]))(float64[:, ::1], float64[:, ::1], float64[:, ::1], "
-    "optional(float64[:, ::1]), optional(float64[:, ::1]))"
-)
-def make_initial_conditions(T, R, Q, a0, P0):
-    if a0 is None:
-        a0 = np.zeros((T.shape[0], 1))
-    if P0 is None:
-        P0 = linalg.solve_discrete_lyapunov(T, R @ Q @ R.T)
-
-    return a0, P0
-
-
-@njit
-def kalman_filter(data, T, Z, R, H, Q, a0=None, P0=None, filter_type="standard"):
-    if filter_type not in ["standard", "univariate"]:
-        raise NotImplementedError(
-            'Only "standard" and "univariate" kalman filters are implemented'
-        )
-
-    a0, P0 = make_initial_conditions(T, R, Q, a0, P0)
-
-    if filter_type == "univariate":
-        filter_results = univariate_kalman_filter(data, T, Z, R, H, Q, a0, P0)
-    else:
-        filter_results = standard_kalman_filter(data, T, Z, R, H, Q, a0, P0)
-
-    return filter_results
diff --git a/gEconpy/estimation/kalman_smoother.py b/gEconpy/estimation/kalman_smoother.py
deleted file mode 100644
index 671c778..0000000
--- a/gEconpy/estimation/kalman_smoother.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import numba as nb
-import numpy as np
-
-
-@nb.njit
-def predict(a, P, T, R, Q):
-    a_hat = T @ a
-
-    P_hat = T @ P @ T.T + R @ Q @ R.T
-    P_hat = 0.5 * (P_hat + P_hat.T)
-
-    return a_hat, P_hat
-
-
-@nb.njit
-def kalman_smoother(T, R, Q, filtered_states, filtered_covariances):
-    n_steps, k_states = filtered_states.shape
-
-    smoothed_states = np.zeros((n_steps, k_states))
-    smoothed_covariances = np.zeros((n_steps, k_states, k_states))
-
-    a_smooth = filtered_states[-1].copy()
-    P_smooth = filtered_covariances[-1].copy()
-
-    smoothed_states[-1] = a_smooth
-    smoothed_covariances[-1] = P_smooth
-
-    for t in range(n_steps - 1, -1, -1):
-        a = filtered_states[t]
-        P = filtered_covariances[t]
-        a_smooth, P_smooth = smoother_step(a, P, a_smooth, P_smooth, T, R, Q)
-
-        smoothed_states[t] = a_smooth
-        smoothed_covariances[t] = P_smooth
-
-    return smoothed_states, smoothed_covariances
-
-
-@nb.njit
-def smoother_step(a, P, a_smooth, P_smooth, T, R, Q):
-    a_hat, P_hat = predict(a, P, T, R, Q)
-
-    # Use pinv, otherwise P_hat is singular when there is missing data
-    smoother_gain = (np.linalg.pinv(P_hat) @ T @ P).T
-
-    a_smooth_next = a + smoother_gain @ (a_smooth - a_hat)
-    P_smooth_next = P + smoother_gain @ (P_smooth - P_hat) @ smoother_gain.T
-
-    return a_smooth_next, P_smooth_next
diff --git a/gEconpy/exceptions/exceptions.py b/gEconpy/exceptions.py
similarity index 78%
rename from gEconpy/exceptions/exceptions.py
rename to gEconpy/exceptions.py
index 8e4f1dd..3f501bd 100644
--- a/gEconpy/exceptions/exceptions.py
+++ b/gEconpy/exceptions.py
@@ -92,7 +92,7 @@ def __init__(self, block_name: str, missing: str) -> None:
 
 
 class MultipleObjectiveFunctionsException(ValueError):
-    def __init__(self, block_name: str, eqs: list[sp.Eq]) -> None:
+    def __init__(self, block_name: str, eqs: list[sp.Expr]) -> None:
         self.block_name = block_name
 
         n_eqs = len(eqs)
@@ -122,11 +122,13 @@ def __init__(self, block_name: str, control: TimeAwareSymbol):
         super().__init__(message)
 
 
-class SteadyStateNotSolvedError(ValueError):
-    def __init__(self):
+class ModelUnknownParameterError(ValueError):
+    def __init__(self, unknown_updates: list[str]):
+        self.unknown_updates = unknown_updates
+
         message = (
-            "The system cannot be solved before the steady-state has been found! Call the .steady_state() method"
-            "to solve for the steady state."
+            f"The following parameters were given new values, but do not exist in the model: "
+            f"{', '.join(unknown_updates)}."
         )
 
         super().__init__(message)
@@ -142,6 +144,17 @@ def __init__(self):
         super().__init__(message)
 
 
+class SteadyStateNotFoundError(ValueError):
+    def __init__(self, equations):
+        message = (
+            "The provided steady-state values did not result in zero residuals for the following equations:\n"
+            f"{', '.join(equations)}\n\nIf you used custom parameter values to compute the provided steady state, "
+            f"you must also provide these parameter values to ``solve_model``."
+        )
+
+        super().__init__(message)
+
+
 class MultipleSteadyStateBlocksException(ValueError):
     def __init__(self, ss_block_names: list[str]):
         message = (
@@ -199,7 +212,7 @@ def __init__(self, variable_name: str, d_str: str, parameter: str):
         super().__init__(message)
 
 
-class ParameterNotFoundException(ValueError):
+class DistributionParameterNotFoundException(ValueError):
     def __init__(
         self,
         variable_name: str,
@@ -300,6 +313,61 @@ def __init__(self, variable_name, d_name):
         super().__init__(message)
 
 
+class OrphanParameterError(ValueError):
+    def __init__(self, orphans):
+        orphans = set(orphans)
+        n = len(orphans)
+        verb = "was" if n == 1 else "were"
+        message = (
+            f'The following parameter{"s" if n > 1 else ""} {verb} found among model equations but did not appear in '
+            f'any calibration block: {", ".join([x.name for x in orphans])}'
+        )
+
+        super().__init__(message)
+
+
+class ExtraParameterError(ValueError):
+    def __init__(self, extras):
+        n = len(extras)
+        verb = "was" if n == 1 else "were"
+        message = (
+            f'The following parameter{"s" if n > 1 else ""} {verb} were given initial values in calibration blocks but '
+            f'were not used in model equations: {", ".join([x.name for x in extras])} \n'
+            f'Verify your model equations, or remove these parameters if they are not needed.'
+        )
+
+        super().__init__(message)
+
+
+class ExtraParameterWarning(UserWarning):
+    def __init__(self, extras):
+        n = len(extras)
+        verb = "was" if n == 1 else "were"
+        message = (
+            f'The following parameter{"s" if n > 1 else ""} {verb} were given initial values in calibration blocks but '
+            f'were not used in model equations: {", ".join([x.name for x in extras])} \n'
+            f'Verify your model equations, or remove these parameters if they are not needed.'
+        )
+
+        super().__init__(message)
+
+
+class DuplicateParameterError(ValueError):
+    def __init__(self, extras, block=None):
+        n = len(extras)
+        verb = "was" if n == 1 else "were"
+        location = "calibration blocks"
+        if block is not None:
+            location = f"in {block} calibration block"
+        message = (
+            f'The following parameter{"s" if n > 1 else ""} {verb} were given initial values in {location} more '
+            f'than once: {", ".join([x.name for x in extras])} \n'
+            f'Model parameters should be declared only once. Check your GCN file and remove one of the declarations.'
+        )
+
+        super().__init__(message)
+
+
 class IgnoredCloseMatchWarning(UserWarning):
     pass
 
diff --git a/gEconpy/estimation/__init__.py b/gEconpy/model/__init__.py
similarity index 100%
rename from gEconpy/estimation/__init__.py
rename to gEconpy/model/__init__.py
diff --git a/gEconpy/classes/block.py b/gEconpy/model/block.py
similarity index 69%
rename from gEconpy/classes/block.py
rename to gEconpy/model/block.py
index 4792fa4..57a42fe 100644
--- a/gEconpy/classes/block.py
+++ b/gEconpy/model/block.py
@@ -4,17 +4,18 @@
 
 from gEconpy.classes.containers import SymbolDictionary
 from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     ControlVariableNotFoundException,
+    DuplicateParameterError,
     DynamicCalibratingEquationException,
     MultipleObjectiveFunctionsException,
     OptimizationProblemNotDefinedException,
 )
-from gEconpy.parser import parse_equations
-from gEconpy.shared.utilities import (
+from gEconpy.utilities import (
     diff_through_time,
     expand_subs_for_all_times,
     set_equality_equals_zero,
+    substitute_repeatedly,
     unpack_keys_and_values,
 )
 
@@ -23,13 +24,12 @@ class Block:
     """
     The Block class holds equations and parameters associated with each block of the DSGE model. They hold methods
     to solve their associated optimization problem. Blocks should be created by a Model.
-
-    TODO: Refactor this into an abstract class with basic functionality, then create some child classes for specific
-    problems, e.g. IdentityBlock, OptimizationBlock, CRRABlock, etc, each with their own optimization machinery.
-
-    TODO: Split components out into their own class/protocol and let them handle their own parsing?
     """
 
+    #     TODO: Split components out into their own class/protocol and let them handle their own parsing?
+    #    TODO: Refactor this into an abstract class with basic functionality, then create some child classes for specific
+    #     problems, e.g. IdentityBlock, OptimizationBlock, CRRABlock, etc, each with their own optimization machinery.
+
     def __init__(
         self,
         name: str,
@@ -45,9 +45,9 @@ def __init__(
         ----------
         name: str
             The name of the block
-        block_dict: Dict[str, str]
-            Dictionary of component:List[equations] key-value pairs created by gEcon_parser.parsed_block_to_dict.
-        solution_hints: Dict[str, str], optional
+        block_dict: dict
+            Dictionary of component:list[equations] key-value pairs created by gEcon_parser.parsed_block_to_dict.
+        solution_hints: dict, optional
             If not None, a dictionary of flags that help the solve_optimization method combine
             the FoC into the "expected" solution. Currently unused.
         allow_incomplete_initialization: bool, optional
@@ -57,24 +57,21 @@ def __init__(
         self.name = name
         self.short_name = "".join(word[0] for word in name.split("_"))
 
-        self.definitions: dict[int, sp.Add] | None = None
+        self.definitions: dict[int, sp.Eq] | None = None
         self.controls: list[TimeAwareSymbol] | None = None
-        self.objective: dict[int, sp.Add] | None = None
-        self.constraints: dict[int, sp.Add] | None = None
-        self.identities: dict[int, sp.Add] | None = None
+        self.objective: dict[int, sp.Expr] | None = None
+        self.constraints: dict[int, sp.Eq] | None = None
+        self.identities: dict[int, sp.Eq] | None = None
         self.shocks: dict[int, TimeAwareSymbol] | None = None
-        self.calibration: dict[int, sp.Add] | None = None
+        self.calibration: dict[int, sp.Expr] | None = None
 
         self.variables: list[TimeAwareSymbol] = []
         self.param_dict: SymbolDictionary[str, float] = SymbolDictionary()
 
-        self.params_to_calibrate: list[sp.Symbol] | None = None
-        self.calibrating_equations: list[sp.Add] | None = None
-
-        self.deterministic_params: list[sp.Symbol] | None = None
-        self.deterministic_relationships: list[sp.Add] | None = None
+        self.calib_dict: SymbolDictionary[str, float] = SymbolDictionary()
+        self.deterministic_dict: SymbolDictionary[str, float] = SymbolDictionary()
 
-        self.system_equations: list[sp.Add] = []
+        self.system_equations: list[sp.Expr] = []
         self.multipliers: dict[int, TimeAwareSymbol] = {}
         self.eliminated_variables: list[sp.Symbol] = []
 
@@ -87,6 +84,8 @@ def __init__(
             assumptions = defaultdict(dict)
 
         self.initialize_from_dictionary(block_dict, assumptions)
+        self._consolidate_definitions()
+
         self._get_variable_list()
         self._get_param_dict_and_calibrating_equations()
 
@@ -96,6 +95,22 @@ def __str__(self):
             f"solved: {self.system_equations is not None}"
         )
 
+    @property
+    def deterministic_params(self) -> list[sp.Symbol]:
+        return list(self.deterministic_dict.to_sympy().keys())
+
+    @property
+    def deterministic_relationships(self) -> list[sp.Expr]:
+        return list(self.deterministic_dict.values())
+
+    @property
+    def params_to_calibrate(self) -> list[sp.Symbol]:
+        return list(self.calib_dict.to_sympy().keys())
+
+    @property
+    def calibrating_equations(self) -> list[sp.Expr]:
+        return list(self.calib_dict.values())
+
     def initialize_from_dictionary(self, block_dict: dict, assumptions: dict) -> None:
         """
         Initialize the model block with the provided definitions, objective, constraints, identities, and calibration
@@ -192,6 +207,37 @@ def _validate_initialization(self) -> bool:
                 if not control_found:
                     raise ControlVariableNotFoundException(self.name, control)
 
+        # Validate equation flags
+        # - the "is_calibrating" key can only occur in the calibration block
+        # - the "exclude" key can only occur in the constraints block
+        valid_flags = {
+            "is_calibrating": ["calibration"],
+            "exclude": ["constraints"],
+        }
+
+        for name, eq_block in zip(
+            ["definitions", "objective", "constraints", "identities"],
+            [self.definitions, self.objective, self.constraints, self.identities],
+        ):
+            if eq_block is not None:
+                for key, eq in eq_block.items():
+                    if (
+                        self.equation_flags[key].get("is_calibrating", False)
+                        and name not in valid_flags["is_calibrating"]
+                    ):
+                        raise ValueError(
+                            f"Equation {eq} in {name} block of {self.name} has an invalid decorator: is_calibrating. "
+                            f"This flag should only appear in the calibration block."
+                        )
+                    if (
+                        self.equation_flags[key].get("exclude", False)
+                        and name not in valid_flags["exclude"]
+                    ):
+                        raise ValueError(
+                            f"Equation {eq} in {name} block of {self.name} has an invalid decorator: exclude. "
+                            f"This flag should only appear in the constraints block."
+                        )
+
         return True
 
     def _validate_key(self, block_dict: dict, key: str) -> bool:
@@ -202,7 +248,7 @@ def _validate_key(self, block_dict: dict, key: str) -> bool:
         Parameters
         ----------
         block_dict : dict
-            Dictionary of component:List[equations] key-value pairs created by gEcon_parser.parsed_block_to_dict.
+            Dictionary of component:list[equations] key-value pairs created by gEcon_parser.parsed_block_to_dict.
         key : str
             A component name.
 
@@ -212,6 +258,24 @@ def _validate_key(self, block_dict: dict, key: str) -> bool:
         """
         return key in block_dict and hasattr(self, key) and block_dict[key] is not None
 
+    def _consolidate_definitions(self):
+        """
+        Combine definitions that refer to other definitions via subsitution
+        """
+        if self.definitions is None:
+            return
+
+        sub_dict = {eq.lhs: eq.rhs for eq in self.definitions.values()}
+
+        for var, eq in sub_dict.items():
+            if not hasattr(eq, "subs"):
+                continue
+            sub_dict[var] = substitute_repeatedly(eq, sub_dict)
+
+        self.definitions = {
+            k: sp.Eq(v.lhs, v.rhs.subs(sub_dict)) for k, v in self.definitions.items()
+        }
+
     def _extract_lagrange_multipliers(
         self, equations: list[list[str]], assumptions: dict
     ) -> tuple[list[list[str]], list[TimeAwareSymbol | None]]:
@@ -236,6 +300,7 @@ def _extract_lagrange_multipliers(
         list
             List of Union[TimeAwareSymbols, None].
         """
+        from gEconpy.parser import parse_equations
 
         result, multipliers = [], []
         for eq in equations:
@@ -255,9 +320,51 @@ def _extract_lagrange_multipliers(
 
         return result, multipliers
 
+    def _extract_decorators(
+        self, equations: list[list[str]], assumptions: dict
+    ) -> tuple[list[list[str]], list[dict[str, bool]]]:
+        """
+        Extract decorators from the equations in the block. Decorators are flags that indicate special properties of the
+        equation, such as whether it should be excluded from the final system of equations.
+
+        Parameters
+        ----------
+        equations : list
+            A list of lists of strings, each list representing a model equation. Created by the
+            gEcon_parser.parsed_block_to_dict function.
+
+        assumptions : dict
+            Assumptions for the model.
+
+        Returns
+        -------
+        equations: list
+            List of lists of strings. All decorator strings have been removed.
+
+        flags: dict
+            A dictionary of flags for each equation, indexed by equation number.
+        """
+
+        result, decorator_flags = [], []
+        for i, eq in enumerate(equations):
+            new_eq = []
+            flags = {}
+            for token in eq:
+                if token.startswith("@"):
+                    decorator = token.removeprefix("@")
+                    flags[decorator] = True
+                else:
+                    new_eq.append(token)
+            result.append(new_eq)
+            decorator_flags.append(flags)
+
+        return result, decorator_flags
+
     def _parse_variable_list(
-        self, block_dict: dict, key: str, assumptions: dict = None
+        self, block_dict: dict, key: str, assumptions: dict | None = None
     ) -> list[sp.Symbol] | None:
+        from gEconpy.parser import parse_equations
+
         """
         Two components -- controls and shocks -- expect a simple list of variables, which is a case the
         gEcon_parser.build_sympy_equations cannot handle.
@@ -293,7 +400,7 @@ def _get_variable_list(self) -> None:
         :return: None
         Get a list of all unique variables in the Block and store it in the class attribute "variables"
         """
-        objective, constraints, identities = [], [], []
+        objective, constraints, identities, multipliers = [], [], [], []
         sub_dict = {}
         if self.definitions is not None:
             _, definitions = unpack_keys_and_values(self.definitions)
@@ -311,14 +418,34 @@ def _get_variable_list(self) -> None:
         all_equations = [
             eq for eq_list in [objective, constraints, identities] for eq in eq_list
         ]
+
+        if self.multipliers is not None:
+            _, multipliers = unpack_keys_and_values(self.multipliers)
+            multipliers = [x for x in multipliers if x is not None]
+
+        all_equations = [
+            eq for eqs_list in [objective, constraints, identities] for eq in eqs_list
+        ]
         for eq in all_equations:
-            eq = eq.subs(sub_dict)
+            eq = substitute_repeatedly(eq, sub_dict)
             atoms = eq.atoms()
             variables = [x for x in atoms if isinstance(x, TimeAwareSymbol)]
             for variable in variables:
                 if variable.to_ss() not in self.variables:
                     self.variables.append(variable.to_ss())
 
+        if self.variables is None:
+            return
+
+        # Can't directly check if variables are not in shocks, because shocks will be None if there are none in the
+        # model
+        shocks = self.shocks or []
+        self.variables = [*self.variables, *multipliers]
+        self.variables = sorted(
+            list({x for x in self.variables if x.set_t(0) not in shocks}),
+            key=lambda x: x.name,
+        )
+
     def _get_and_record_equation_numbers(self, equations: list[sp.Eq]) -> list[int]:
         """
         Get a list of all unique variables in the Block and store it in the class attribute "variables".
@@ -356,6 +483,8 @@ def _parse_equation_list(
         dict
             A dictionary of sympy equations, indexed by their equation number, or None if the block does not exist.
         """
+        from gEconpy.parser import parse_equations
+
         if not self._validate_key(block_dict, key):
             return
 
@@ -363,20 +492,26 @@ def _parse_equation_list(
         equations, lagrange_multipliers = self._extract_lagrange_multipliers(
             equations, assumptions
         )
+        equations, decorators = self._extract_decorators(equations, assumptions)
 
         parser_output = parse_equations.build_sympy_equations(equations, assumptions)
+
         if len(parser_output) > 0:
             equations, flags = list(zip(*parser_output))
         else:
             equations, flags = [], {}
+
         equation_numbers = self._get_and_record_equation_numbers(equations)
 
         equations = dict(zip(equation_numbers, equations))
         flags = dict(zip(equation_numbers, flags))
+        decorator_flags = dict(zip(equation_numbers, decorators))
 
         lagrange_multipliers = dict(zip(equation_numbers, lagrange_multipliers))
         self.multipliers.update(lagrange_multipliers)
-        self.equation_flags.update(flags)
+        for k in equation_numbers:
+            self.equation_flags[k] = flags[k]
+            self.equation_flags[k].update(decorator_flags[k])
 
         return equations
 
@@ -402,18 +537,35 @@ def _get_param_dict_and_calibrating_equations(self) -> None:
             return
 
         eq_idxs, equations = unpack_keys_and_values(self.calibration)
+        duplicates = []
 
+        # Main parameter processing loop
         for idx, eq in zip(eq_idxs, equations):
             atoms = eq.atoms()
+            lhs, rhs = eq.lhs, eq.rhs
+            if not lhs.is_symbol:
+                raise ValueError(
+                    "Left-hand side of calibrating expressions should be the single parameter to be "
+                    f"computed. Found multiple argumnets: {eq.lhs.args}"
+                )
+
+            param = eq.lhs
+
+            # Check if the RHS is just a number (most common case). If so, convert it to a float (rather than
+            # an sp.Float, which won't play nice with lambdify later)
+            if eq.rhs.is_number:
+                value = eq.rhs.evalf()
+                if param in self.param_dict.keys():
+                    duplicates.append(param)
+                else:
+                    self.param_dict[param] = value
 
-            # Check if this equation is a normal parameter definition. If so, it will be exactly in the form x = y
-            if eq.lhs.is_symbol and eq.rhs.is_number:
-                param = eq.lhs
-                value = eq.rhs
-                self.param_dict[param] = value
+            # If the RHS was not a number, its either a calibrating equation or a deterministic relationship of other
+            # parameters.
 
+            # Calibrating equations are tagged in the equation_flags dictionary during parsing.
             elif self.equation_flags[idx]["is_calibrating"]:
-                # Check if this equation is a valid calibrating equation
+                # Calibrating equations can have variables, but they must be in the steady state
                 if not all(
                     [
                         x.time_index == "ss"
@@ -425,40 +577,28 @@ def _get_param_dict_and_calibrating_equations(self) -> None:
                         eq=eq, block_name=self.name
                     )
 
-                if self.params_to_calibrate is None:
-                    self.params_to_calibrate = [eq.lhs]
+                if param in self.calib_dict:
+                    duplicates.append(param)
                 else:
-                    self.params_to_calibrate.append(eq.lhs)
-
-                if self.calibrating_equations is None:
-                    self.calibrating_equations = [set_equality_equals_zero(eq.rhs)]
-                else:
-                    self.calibrating_equations.append(set_equality_equals_zero(eq.rhs))
+                    self.calib_dict[param] = rhs
 
             else:
                 # What is left should only be "deterministic relationships", parameters that are defined as
                 # functions of other parameters that the user wants to keep track of.
 
                 # Check that these are functions of numbers and parameters only
-
                 if any([isinstance(x, TimeAwareSymbol) for x in atoms]):
                     raise ValueError(
                         "Parameters defined as functions in the calibration sub-block cannot be functions "
                         f"of variables. Found:\n\n {eq} in {self.name}"
                     )
-                if self.deterministic_params is None:
-                    self.deterministic_params = [eq.lhs]
+                if eq.lhs in self.deterministic_dict:
+                    duplicates.append(lhs)
                 else:
-                    self.deterministic_params.append(eq.lhs)
+                    self.deterministic_dict[lhs] = rhs.doit()
 
-                if self.deterministic_relationships is None:
-                    self.deterministic_relationships = [
-                        set_equality_equals_zero(eq.rhs)
-                    ]
-                else:
-                    self.deterministic_relationships.append(
-                        set_equality_equals_zero(eq.rhs)
-                    )
+        if len(duplicates) > 0:
+            raise DuplicateParameterError(duplicates, self.name)
 
     def _build_lagrangian(self) -> sp.Add:
         """
@@ -473,7 +613,7 @@ def _build_lagrangian(self) -> sp.Add:
         -------
         None
         """
-        objective = list(self.objective.values())[0]
+        objective = next(iter(self.objective.values()))
         constraints = self.constraints
         multipliers = self.multipliers
         sub_dict = dict()
@@ -490,6 +630,7 @@ def _build_lagrangian(self) -> sp.Add:
                 lm = multipliers[key]
             else:
                 lm = TimeAwareSymbol(f"lambda__{self.short_name}_{i}", 0)
+                self.multipliers[i] = lm
                 i += 1
 
             lagrange = lagrange - lm * (
@@ -528,20 +669,29 @@ def _get_discount_factor(self) -> sp.Symbol | None:
 
         variables = [x for x in objective.atoms() if isinstance(x, TimeAwareSymbol)]
 
-        # Return 1 if there is no continuation value
+        # Return 1 if there is no continuation value -- static optimization
         if all([x.time_index in [0, -1] for x in variables]):
-            return 1.0
+            return sp.Float(1.0)
 
         else:
-            continuation_value = [x for x in variables if x.time_index == 1]
-            if len(continuation_value) > 1:
+            # We expect a bellman equation of the form X[] = a[] + E[][f(a[1]]. Step one is to identify a[], the
+            # instantaneous value function at time t. It should be a term isolated on the RHS of the equation.
+            current_value = objective.lhs
+            continuation_value = [
+                x for x in objective.rhs.args if x.has(current_value.set_t(1))
+            ]
+
+            # continuation_value = [x for x in variables if x.time_index == 1 and x.set_t(0) in variables]
+            if len(continuation_value) == 0:
                 raise ValueError(
-                    f"Block {self.name} has multiple t+1 variables in the Bellman equation, this is not "
-                    f"currently supported. Rewrite the equation in the form X[] = a[] + b * E[][X[1]], "
-                    f"where a[] is the instantaneous value function at time t, defined in the "
-                    f'"definitions" component of the block.'
+                    f"Block {self.name} did not find the continuation value of the current state value in the following"
+                    f"objective function: {objective}. Objectives should be written in the form "
+                    f"``V[t] = f(x[t]) + b[t] * E[V[t+1]]``, where V[t] is the current state value, f(x[t]) is the "
+                    f"instantaneous value function, and b[t] is the discount factor."
                 )
-            discount_factor = objective.rhs.coeff(continuation_value[0])
+
+            continuation_value = continuation_value[0]
+            discount_factor = continuation_value.subs({current_value.set_t(1): 1})
             return discount_factor
 
     def simplify_system_equations(self) -> None:
@@ -583,6 +733,10 @@ def simplify_system_equations(self) -> None:
         self.system_equations = simplified_system
         self.eliminated_variables = eliminated_variables
 
+        for key, value in self.multipliers.items():
+            if value in eliminated_variables:
+                self.multipliers[key] = None
+
     def solve_optimization(self, try_simplify: bool = True) -> None:
         r"""
         Solve the optimization problem implied by the block structure:
@@ -607,15 +761,14 @@ def solve_optimization(self, try_simplify: bool = True) -> None:
         -----
         All first order conditions, along with the constraints and objective are stored in the .system_equations method.
         No attempt is made to simplify the resulting system if try_simplify = False.
-
-        TODO: Add helper functions to simplify common setups, including CRRA/log-utility (extract Euler equation,
-            labor supply curve, etc), and common production functions (CES, CD -- extract demand curves, prices, or
-            marginal costs)
-
-        TODO: Automatically solving for un-named lagrange multipliers is currently done by the Model class, is this
-                correct?
         """
+
+        # TODO: Add helper functions to simplify common setups, including CRRA/log-utility (extract Euler equation,
+        #     labor supply curve, etc), and common production functions (CES, CD -- extract demand curves, prices, or
+        #     marginal costs)
         sub_dict = dict()
+        if self.system_equations is None:
+            self.system_equations = []
 
         if self.definitions is not None:
             _, definitions = unpack_keys_and_values(self.definitions)
@@ -629,11 +782,12 @@ def solve_optimization(self, try_simplify: bool = True) -> None:
                 )
 
         if self.constraints is not None:
-            _, constraints = unpack_keys_and_values(self.constraints)
-            for eq in constraints:
-                self.system_equations.append(
-                    set_equality_equals_zero(eq.subs(sub_dict))
-                )
+            eq_idx, constraints = unpack_keys_and_values(self.constraints)
+            for idx, eq in zip(eq_idx, constraints):
+                if not self.equation_flags[idx].get("exclude", False):
+                    self.system_equations.append(
+                        set_equality_equals_zero(eq.subs(sub_dict))
+                    )
 
         if self.controls is None and self.objective is None:
             return
@@ -668,3 +822,6 @@ def solve_optimization(self, try_simplify: bool = True) -> None:
 
         if try_simplify:
             self.simplify_system_equations()
+
+        # Update the variable list
+        self._get_variable_list()
diff --git a/gEconpy/model/build.py b/gEconpy/model/build.py
new file mode 100644
index 0000000..45680e4
--- /dev/null
+++ b/gEconpy/model/build.py
@@ -0,0 +1,332 @@
+import logging
+
+import pytensor.tensor as pt
+import sympy as sp
+
+from pymc.pytensorf import rewrite_pregrad
+from pytensor import graph_replace
+
+from gEconpy.model.compile import BACKENDS
+from gEconpy.model.model import Model
+from gEconpy.model.perturbation import compile_linearized_system
+from gEconpy.model.statespace import DSGEStateSpace
+from gEconpy.model.steady_state import (
+    ERROR_FUNCTIONS,
+    compile_model_ss_functions,
+    system_to_steady_state,
+)
+from gEconpy.parser.file_loaders import (
+    block_dict_to_model_primitives,
+    build_report,
+    gcn_to_block_dict,
+    simplify_provided_ss_equations,
+    validate_results,
+)
+from gEconpy.utilities import get_name, substitute_repeatedly
+
+_log = logging.getLogger(__name__)
+
+
+def _compile_gcn(
+    gcn_path: str,
+    simplify_blocks: bool = True,
+    simplify_tryreduce: bool = True,
+    simplify_constants: bool = True,
+    verbose: bool = True,
+    backend: BACKENDS = "numpy",
+    return_symbolic: bool = False,
+    error_function: ERROR_FUNCTIONS = "squared",
+    on_unused_parameters="raise",
+    **kwargs,
+) -> tuple[tuple, tuple, tuple, dict, tuple]:
+    outputs = gcn_to_block_dict(gcn_path, simplify_blocks=simplify_blocks)
+    block_dict, assumptions, options, try_reduce, ss_solution_dict, prior_info = outputs
+
+    (
+        equations,
+        param_dict,
+        calib_dict,
+        deterministic_dict,
+        variables,
+        shocks,
+        param_priors,
+        shock_priors,
+        hyper_priors_final,
+        reduced_vars,
+        singletons,
+    ) = block_dict_to_model_primitives(
+        block_dict,
+        assumptions,
+        try_reduce,
+        prior_info,
+        simplify_tryreduce=simplify_tryreduce,
+        simplify_constants=simplify_constants,
+    )
+
+    ss_solution_dict = simplify_provided_ss_equations(ss_solution_dict, variables)
+    steady_state_relationships = [
+        sp.Eq(var, eq) for var, eq in ss_solution_dict.to_sympy().items()
+    ]
+
+    # TODO: Move this to a separate function
+    # TODO: Add option to not eliminate deterministic parameters (the user might be interested in them)
+
+    deterministic_dict.to_sympy(inplace=True)
+    for param, expr in deterministic_dict.items():
+        deterministic_dict[param] = substitute_repeatedly(expr, deterministic_dict)
+
+    # If a deterministic parameter is only used in other parameters, it will now have been completely substituted away
+    # and can be removed
+    reduced_params = []
+    final_deterministics = deterministic_dict.copy()
+    for param in deterministic_dict.keys():
+        if not any(eq.has(param) for eq in equations + steady_state_relationships):
+            reduced_params.append(param)
+            del final_deterministics[param]
+
+    deterministic_dict = final_deterministics.to_string()
+
+    validate_results(
+        equations,
+        steady_state_relationships,
+        param_dict,
+        calib_dict,
+        deterministic_dict,
+        on_unused_parameters=on_unused_parameters,
+    )
+    steady_state_equations = system_to_steady_state(equations, shocks)
+
+    variables = sorted(variables, key=lambda x: x.base_name)
+    shocks = sorted(shocks, key=lambda x: x.base_name)
+
+    functions, cache = compile_model_ss_functions(
+        steady_state_equations,
+        ss_solution_dict,
+        variables,
+        param_dict,
+        deterministic_dict,
+        calib_dict,
+        error_func=error_function,
+        backend=backend,
+        return_symbolic=return_symbolic,
+        **kwargs,
+    )
+
+    f_params, f_ss, resid_funcs, error_funcs = functions
+    f_ss_resid, f_ss_jac = resid_funcs
+    f_ss_error, f_ss_grad, f_ss_hess, f_ss_hessp = error_funcs
+
+    f_linearize, cache = compile_linearized_system(
+        equations,
+        variables,
+        param_dict,
+        deterministic_dict,
+        calib_dict,
+        shocks,
+        backend=backend,
+        return_symbolic=return_symbolic,
+        cache=cache,
+    )
+
+    if verbose:
+        build_report(
+            equations,
+            param_dict,
+            calib_dict,
+            variables,
+            shocks,
+            param_priors,
+            shock_priors,
+            reduced_vars,
+            reduced_params,
+            singletons,
+        )
+
+    objects = (variables, shocks, equations, steady_state_relationships)
+    dictionaries = (param_dict, deterministic_dict, calib_dict)
+    functions = (
+        f_ss,
+        f_ss_jac,
+        f_params,
+        f_ss_resid,
+        f_ss_error,
+        f_ss_grad,
+        f_ss_hess,
+        f_ss_hessp,
+        f_linearize,
+    )
+    priors = (param_priors, shock_priors, hyper_priors_final)
+
+    return objects, dictionaries, functions, cache, priors
+
+
+def model_from_gcn(
+    gcn_path: str,
+    simplify_blocks: bool = True,
+    simplify_tryreduce: bool = True,
+    simplify_constants: bool = True,
+    verbose: bool = True,
+    backend: BACKENDS = "numpy",
+    error_function: ERROR_FUNCTIONS = "squared",
+    on_unused_parameters="raise",
+    **kwargs,
+) -> Model:
+    objects, dictionaries, functions, cache, priors = _compile_gcn(
+        gcn_path,
+        simplify_blocks=simplify_blocks,
+        simplify_tryreduce=simplify_tryreduce,
+        simplify_constants=simplify_constants,
+        verbose=verbose,
+        backend=backend,
+        error_function=error_function,
+        on_unused_parameters=on_unused_parameters,
+        **kwargs,
+    )
+
+    variables, shocks, equations, ss_relationships = objects
+    param_dict, deterministic_dict, calib_dict = dictionaries
+
+    (
+        f_ss,
+        f_ss_jac,
+        f_params,
+        f_ss_resid,
+        f_ss_error,
+        f_ss_grad,
+        f_ss_hess,
+        f_ss_hessp,
+        f_linearize,
+    ) = functions
+
+    return Model(
+        variables=variables,
+        shocks=shocks,
+        equations=equations,
+        steady_state_relationships=ss_relationships,
+        param_dict=param_dict,
+        deterministic_dict=deterministic_dict,
+        calib_dict=calib_dict,
+        f_ss=f_ss,
+        f_ss_jac=f_ss_jac,
+        f_params=f_params,
+        f_ss_resid=f_ss_resid,
+        f_ss_error=f_ss_error,
+        f_ss_error_grad=f_ss_grad,
+        f_ss_error_hess=f_ss_hess,
+        f_ss_error_hessp=f_ss_hessp,
+        f_linearize=f_linearize,
+        backend=backend,
+        priors=priors,
+    )
+
+
+def statespace_from_gcn(
+    gcn_path: str,
+    simplify_blocks: bool = True,
+    simplify_tryreduce: bool = True,
+    simplify_constants: bool = True,
+    verbose: bool = True,
+    error_function: ERROR_FUNCTIONS = "squared",
+    on_unused_parameters="raise",
+    log_linearize: bool = True,
+    not_loglin_variables: list[str] | None = None,
+    **kwargs,
+):
+    objects, dictionaries, functions, cache, priors = _compile_gcn(
+        gcn_path,
+        simplify_blocks=simplify_blocks,
+        simplify_tryreduce=simplify_tryreduce,
+        simplify_constants=simplify_constants,
+        verbose=verbose,
+        backend="pytensor",
+        error_function=error_function,
+        on_unused_parameters=on_unused_parameters,
+        return_symbolic=True,
+        **kwargs,
+    )
+
+    variables, shocks, equations, ss_relationships = objects
+    param_dict, deterministic_dict, calib_dict = dictionaries
+    param_priors, shock_priors, hyper_priors = priors
+
+    if len(calib_dict) > 0:
+        raise NotImplementedError("Calibration not yet implemented in StateSpace model")
+
+    (
+        steady_state_mapping,
+        ss_jac,
+        parameter_mapping,
+        ss_resid,
+        ss_error,
+        ss_grad,
+        ss_hess,
+        ss_hessp,
+        linearized_matrices,
+    ) = functions
+
+    # Check that the entire steady state has been provided
+    if steady_state_mapping is None or len(steady_state_mapping) != len(variables):
+        raise NotImplementedError(
+            "Numeric steady state not yet implemented in StateSpace model"
+        )
+
+    A, B, C, D = linearized_matrices
+
+    not_loglin_flags = next(
+        x for x in cache.values() if x.name == "not_loglin_variable"
+    )
+
+    # First replace deterministic variables with functions of input variables in the user-provided steady state
+    # expressiong
+    steady_state_mapping = {
+        k: graph_replace(v, parameter_mapping, strict=False)
+        for k, v in steady_state_mapping.items()
+    }
+
+    ss_vec = pt.stack(list(steady_state_mapping.values()))
+    if not_loglin_variables is None:
+        not_loglin_variables = []
+
+    var_names = [get_name(x, base_name=True) for x in variables]
+    unknown_not_login = set(not_loglin_variables) - set(var_names)
+
+    if len(unknown_not_login) > 0:
+        raise ValueError(
+            f"The following variables were requested not to be log-linearized, but are unknown to the model: "
+            f"{', '.join(unknown_not_login)}"
+        )
+
+    if log_linearize:
+        not_loglin_mask = pt.as_tensor([x in not_loglin_variables for x in var_names])
+        not_loglin_values = pt.le(ss_vec, 0.0).astype(float)
+        not_loglin_values = not_loglin_values[not_loglin_mask].set(1.0)
+    else:
+        not_loglin_values = pt.ones(ss_vec.shape[0])
+
+    not_loglin_replacement = {not_loglin_flags: not_loglin_values}
+
+    replacements = parameter_mapping | steady_state_mapping | not_loglin_replacement
+
+    # Replace all placeholders with functions of the input parameters
+    ss_resid, ss_jac, ss_error, ss_grad, ss_hess = graph_replace(
+        [ss_resid, ss_jac, ss_error, ss_grad, ss_hess], replacements, strict=False
+    )
+    A, B, C, D = rewrite_pregrad(
+        graph_replace([A, B, C, D], replacements, strict=False)
+    )
+
+    return DSGEStateSpace(
+        variables=variables,
+        shocks=shocks,
+        equations=equations,
+        param_dict=param_dict,
+        priors=priors,
+        parameter_mapping=parameter_mapping,
+        steady_state_mapping=steady_state_mapping,
+        ss_jac=ss_jac,
+        ss_resid=ss_resid,
+        ss_error=ss_error,
+        ss_error_grad=ss_grad,
+        ss_error_hess=ss_hess,
+        linearized_system=[A, B, C, D],
+    )
diff --git a/gEconpy/model/compile.py b/gEconpy/model/compile.py
new file mode 100644
index 0000000..2fcc2fe
--- /dev/null
+++ b/gEconpy/model/compile.py
@@ -0,0 +1,269 @@
+from collections.abc import Callable
+from functools import wraps
+from typing import Literal
+
+import numpy as np
+import pytensor
+import sympy as sp
+
+from sympytensor import as_tensor
+
+from gEconpy.classes.containers import SteadyStateResults, SymbolDictionary
+from gEconpy.numbaf.utilities import numba_lambdify
+
+BACKENDS = Literal["numpy", "numba", "pytensor"]
+
+
+def sp_to_pt_from_cache(symbol_list: list[sp.Symbol], cache: dict) -> SymbolDictionary:
+    """
+    Look up a list of symbols in a Sympy PytensorPrinter cache and return a SymbolDictionary mapping each symbol
+    to its corresponding tensor variable on the compute graph.
+
+    Parameters
+    ----------
+    symbol_list: list[sp.Symbol]
+        List of sympy symbols to look up in the cache
+
+    cache: dict
+        Dictionary created by SympyTensor during printing.
+
+    Returns
+    -------
+    sp_to_pt: SymbolDictionary
+        Mapping from sympy symbols to their pytensor Variables
+    """
+
+    sp_to_pt = {}
+    cached_names = [x[0] for x in cache.keys()]
+    cached_tensors = list(cache.values())
+    for symbol in symbol_list:
+        if symbol.name in cached_names:
+            idx = cached_names.index(symbol.name)
+            sp_to_pt[symbol] = cached_tensors[idx]
+        else:
+            raise ValueError(f"{symbol} not found in the provided cache")
+
+    return SymbolDictionary(sp_to_pt)
+
+
+def output_to_tensor(x, cache):
+    if isinstance(x, int | float | sp.Float | sp.Integer):
+        return pytensor.tensor.constant(x, dtype=pytensor.config.floatX)
+
+    return as_tensor(x, cache)
+
+
+def dictionary_return_wrapper(f: Callable, outputs: list[sp.Symbol]) -> Callable:
+    @wraps(f)
+    def inner(*args, **kwargs):
+        values = f(*args, **kwargs)
+        return SteadyStateResults(zip(outputs, values)).to_string()
+
+    return inner
+
+
+def stack_return_wrapper(f: Callable) -> Callable:
+    @wraps(f)
+    def inner(*args, **kwargs):
+        values = f(*args, **kwargs)
+        if not isinstance(values, list):
+            # Special case for single output functions, for example a partially declared steady state
+            # with only one equation
+            values = [values]
+        return np.stack(values)
+
+    return inner
+
+
+def pop_return_wrapper(f: Callable) -> Callable:
+    @wraps(f)
+    def inner(*args, **kwargs):
+        values = np.array(f(*args, **kwargs))
+        if values.ndim == 0:
+            return values.item(0)
+        else:
+            return values[0]
+
+    return inner
+
+
+def array_return_wrapper(f: Callable) -> Callable:
+    @wraps(f)
+    def inner(*args, **kwargs):
+        return np.array(f(*args, **kwargs))
+
+    return inner
+
+
+def _configue_pytensor_kwargs(kwargs: dict) -> dict:
+    if "on_unused_input" not in kwargs:
+        kwargs["on_unused_input"] = "ignore"
+    return kwargs
+
+
+def compile_function(
+    inputs: list[sp.Symbol],
+    outputs: list[sp.Symbol | sp.Expr] | sp.MutableDenseMatrix,
+    backend: BACKENDS,
+    cache: dict | None = None,
+    stack_return: bool = False,
+    pop_return: bool = False,
+    return_symbolic: bool = False,
+    **kwargs,
+) -> tuple[Callable, dict]:
+    """
+    Dispatch compilation of a sympy function to one of three possible backends: numpy, numba, or pytensor.
+
+    Parameters
+    ----------
+    inputs: list[sp.Symbol]
+        The inputs to the function.
+
+    outputs: list[Union[sp.Symbol, sp.Expr]]
+        The outputs of the function.
+
+    backend: str, one of "numpy", "numba", "pytensor"
+        The backend to use for the compiled function.
+
+    cache: dict, optional
+        A dictionary mapping from pytensor symbols to sympy expressions. Used to prevent duplicate mappings from
+        sympy symbol to pytensor symbol from being created. Default is a empty dictionary, implying no other functions
+        have been compiled yet.
+
+        Ignored if backend is not "pytensor".
+
+    stack_return: bool, optional
+        If True, the function will return a single numpy array with all outputs. Otherwise it will return a tuple of
+        numpy arrays. Default is False.
+
+    pop_return: bool, optional
+        If True, the function will return only the 0th element of the output. Used to remove the list wrapper around
+        scalar outputs. Default is False.
+
+    return_symbolic: bool, default True
+        If True, when mode="pytensor", the will return a symbolic pytensor computation graph instead of a compiled
+        function. Ignored when mode is not "pytensor".
+
+    Returns
+    -------
+    f: Callable
+        A python function that computes the outputs from the inputs.
+
+    cache: dict
+        A dictionary mapping from sympy symbols to pytensor symbols.
+    """
+    if backend == "numpy":
+        f, cache = compile_to_numpy(
+            inputs, outputs, cache, stack_return, pop_return, **kwargs
+        )
+    elif backend == "numba":
+        f, cache = compile_to_numba(
+            inputs, outputs, cache, stack_return, pop_return, **kwargs
+        )
+    elif backend == "pytensor":
+        f, cache = compile_to_pytensor_function(
+            inputs, outputs, cache, stack_return, pop_return, return_symbolic, **kwargs
+        )
+    else:
+        raise NotImplementedError(
+            f"backend {backend} not implemented. Must be one of {BACKENDS}."
+        )
+
+    return f, cache
+
+
+def compile_to_numpy(
+    inputs: list[sp.Symbol],
+    outputs: list[sp.Symbol | sp.Expr] | sp.MutableDenseMatrix,
+    cache: dict,
+    stack_return: bool,
+    pop_return: bool,
+    **kwargs,
+):
+    f = sp.lambdify(inputs, outputs)
+    if stack_return:
+        f = stack_return_wrapper(f)
+    if pop_return:
+        f = pop_return_wrapper(f)
+    return f, cache
+
+
+def compile_to_numba(
+    inputs: list[sp.Symbol],
+    outputs: list[sp.Symbol | sp.Expr],
+    cache: dict,
+    stack_return: bool,
+    pop_return: bool,
+    **kwargs,
+):
+    f = numba_lambdify(inputs, outputs, stack_outputs=stack_return)
+    if pop_return:
+        f = pop_return_wrapper(f)
+    return f, cache
+
+
+def compile_to_pytensor_function(
+    inputs: list[sp.Symbol],
+    outputs: list[sp.Symbol | sp.Expr],
+    cache: dict,
+    stack_return: bool,
+    pop_return: bool,
+    return_symbolic: bool,
+    **kwargs,
+):
+    kwargs = _configue_pytensor_kwargs(kwargs)
+    cache = {} if cache is None else cache
+
+    outputs = [outputs] if not isinstance(outputs, list) else outputs
+    input_pt = [as_tensor(x, cache) for x in inputs]
+    output_pt = [output_to_tensor(x, cache) for x in outputs]
+
+    original_shape = [x.type.shape for x in output_pt]
+
+    if stack_return:
+        output_pt = pytensor.tensor.stack(output_pt)
+    if pop_return:
+        output_pt = (
+            output_pt[0]
+            if (isinstance(output_pt, list) and len(output_pt) == 1)
+            else output_pt
+        )
+
+    if return_symbolic:
+        return output_pt, cache
+
+    f = pytensor.function(input_pt, output_pt, **kwargs)
+
+    # If pytensor is in JAX mode, compiled functions will JAX array objects rather than numpy arrays
+    # Add a wrapper to convert the JAX array to a numpy array
+    if kwargs.get("mode", None) == "JAX":
+        f = array_return_wrapper(f)
+
+    # Pytensor never returns a scalar float (it will return a 0d array in this case), so we need to wrap the function
+    # in this case
+    if len(original_shape) == 1 and original_shape[0] == () and pop_return:
+        f = pop_return_wrapper(f)
+
+    return f, cache
+
+
+def make_cache_key(name, cls):
+    return (name, cls, (), "floatX", ())
+
+
+def make_return_dict_and_update_cache(input_symbols, output_tensors, cache, cls=None):
+    if cls is None:
+        cls = sp.Symbol
+    out_dict = {}
+    for symbol, value in zip(input_symbols, output_tensors):
+        cache_key = make_cache_key(symbol.name, cls)
+
+        if cache_key in cache:
+            pt_symbol = cache[cache_key]
+        else:
+            pt_symbol = pytensor.tensor.scalar(name=symbol.name, dtype="floatX")
+            cache[cache_key] = pt_symbol
+
+        out_dict[pt_symbol] = value
+
+    return out_dict, cache
diff --git a/gEconpy/model/model.py b/gEconpy/model/model.py
new file mode 100644
index 0000000..a2db6aa
--- /dev/null
+++ b/gEconpy/model/model.py
@@ -0,0 +1,1891 @@
+import difflib
+import functools as ft
+import logging
+
+from collections.abc import Callable, Sequence
+from copy import deepcopy
+from typing import Literal, cast
+
+import numba as nb
+import numpy as np
+import pandas as pd
+import sympy as sp
+import xarray as xr
+
+from better_optimize import minimize, root
+from scipy import linalg
+
+from gEconpy.classes.containers import SteadyStateResults, SymbolDictionary
+from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
+from gEconpy.exceptions import (
+    GensysFailedException,
+    ModelUnknownParameterError,
+    PerturbationSolutionNotFoundException,
+    SteadyStateNotFoundError,
+)
+from gEconpy.model.compile import BACKENDS
+from gEconpy.model.perturbation import check_bk_condition as _check_bk_condition
+from gEconpy.model.perturbation import (
+    check_perturbation_solution,
+    make_not_loglin_flags,
+    override_dummy_wrapper,
+    residual_norms,
+    statespace_to_gEcon_representation,
+)
+from gEconpy.model.steady_state import system_to_steady_state
+from gEconpy.solvers.cycle_reduction import solve_policy_function_with_cycle_reduction
+from gEconpy.solvers.gensys import (
+    interpret_gensys_output,
+    solve_policy_function_with_gensys,
+)
+from gEconpy.utilities import get_name, postprocess_optimizer_res, safe_to_ss
+
+VariableType = sp.Symbol | TimeAwareSymbol
+_log = logging.getLogger(__name__)
+
+
+def scipy_wrapper(
+    f: Callable,
+    variables: list[str],
+    unknown_var_idxs: np.ndarray[int | bool],
+    unknown_eq_idxs: np.ndarray[int | bool],
+    f_ss: Callable | None = None,
+    include_p=False,
+) -> Callable:
+    if f_ss is not None:
+        if not include_p:
+
+            @ft.wraps(f)
+            def inner(ss_values, param_dict):
+                given_ss = f_ss(**param_dict)
+                ss_dict = SymbolDictionary(zip(variables, ss_values)).to_string()
+                ss_dict.update(given_ss)
+                res = f(**ss_dict, **param_dict)
+
+                if isinstance(res, float | int):
+                    return res
+                elif res.ndim == 1:
+                    res = res[unknown_eq_idxs]
+                elif res.ndim == 2:
+                    res = res[unknown_eq_idxs, :][:, unknown_var_idxs]
+                return res
+        else:
+
+            @ft.wraps(f)
+            def inner(ss_values, p, param_dict):
+                given_ss = f_ss(**param_dict)
+                ss_dict = SymbolDictionary(zip(variables, ss_values)).to_string()
+                ss_dict.update(given_ss)
+
+                p_full = np.zeros(unknown_eq_idxs.shape[0])
+                p_full[unknown_var_idxs] = p
+
+                res = f(p_full, **ss_dict, **param_dict)
+
+                if isinstance(res, float | int):
+                    return res
+                elif res.ndim == 1:
+                    res = res[unknown_eq_idxs]
+                elif res.ndim == 2:
+                    res = res[unknown_eq_idxs, :][:, unknown_var_idxs]
+                return res
+
+    else:
+        if not include_p:
+
+            @ft.wraps(f)
+            def inner(ss_values, param_dict):
+                ss_dict = SymbolDictionary(zip(variables, ss_values)).to_string()
+                return f(**ss_dict, **param_dict)
+        else:
+
+            @ft.wraps(f)
+            def inner(ss_values, p, param_dict):
+                ss_dict = SymbolDictionary(zip(variables, ss_values)).to_string()
+                return f(p, **ss_dict, **param_dict)
+
+    return inner
+
+
+def add_more_ss_values_wrapper(
+    f_ss: Callable | None, known_variables: SymbolDictionary
+) -> Callable:
+    """
+    Inject user-provided constant steady state values to the return of the steady state function.
+
+    Parameters
+    ----------
+    f_ss: Callable, Optional
+        Compiled function that maps models parameters to numerical steady state values for variables.
+
+    known_variables: SymbolDictionary
+        Numerical values for model variables in the steady state provided by the user. Keys are expected to be string
+        variable names, and values floats.
+
+    Returns
+    -------
+    Callable
+        A new version of f_ss whose returns always includes the contents of known_variables.
+    """
+
+    @ft.wraps(f_ss)
+    def inner(**parameters):
+        if f_ss is None:
+            return known_variables
+
+        ss_dict = f_ss(**parameters)
+        ss_dict.update(known_variables)
+        return ss_dict
+
+    return inner
+
+
+def infer_variable_bounds(variable):
+    assumptions = variable.assumptions0
+    is_positive = assumptions.get("positive", False)
+    is_negative = assumptions.get("negative", False)
+    lhs = 1e-8 if is_positive else None
+    rhs = -1e-8 if is_negative else None
+
+    return lhs, rhs
+
+
+def _initialize_x0(optimizer_kwargs, variables, jitter_x0):
+    n_variables = len(variables)
+
+    use_default_x0 = "x0" not in optimizer_kwargs
+    x0 = optimizer_kwargs.pop("x0", np.full(n_variables, 0.8))
+
+    if use_default_x0:
+        negative_idx = [x.assumptions0.get("negative", False) for x in variables]
+        x0[negative_idx] = -x0[negative_idx]
+
+    if jitter_x0:
+        x0 += np.random.normal(scale=1e-4, size=n_variables)
+
+    return x0
+
+
+def validate_policy_function(
+    A, B, C, D, T, R, tol: float = 1e-8, verbose: bool = True
+) -> None:
+    gEcon_matrices = statespace_to_gEcon_representation(A, T, R, tol)
+
+    P, Q, _, _, A_prime, R_prime, S_prime = gEcon_matrices
+
+    resid_norms = residual_norms(B, C, D, Q, P, A_prime, R_prime, S_prime)
+    norm_deterministic, norm_stochastic = resid_norms
+
+    if verbose:
+        _log.info(f"Norm of deterministic part: {norm_deterministic:0.9f}")
+        _log.info(f"Norm of stochastic part:    {norm_deterministic:0.9f}")
+
+
+def get_known_equation_mask(
+    steady_state_system: list[sp.Expr],
+    ss_dict: SymbolDictionary[sp.Symbol, float],
+    param_dict: SymbolDictionary[sp.Symbol, float],
+    tol: float = 1e-8,
+) -> np.ndarray:
+    sub_dict = ss_dict.copy() | param_dict.copy()
+    subbed_system = [eq.subs(sub_dict.to_sympy()) for eq in steady_state_system]
+
+    eq_is_zero_mask = [
+        (sp.Abs(subbed_eq) < tol) == True  # noqa: E712
+        for eq, subbed_eq in zip(steady_state_system, subbed_system)
+    ]
+
+    return np.array(eq_is_zero_mask)
+
+
+def validate_user_steady_state_simple(
+    steady_state_system: list[sp.Expr],
+    ss_dict: SymbolDictionary[sp.Symbol, float],
+    param_dict: SymbolDictionary[sp.Symbol, float],
+    tol: float = 1e-8,
+) -> None:
+    r"""
+    Perform a "shallow" validation of user-provided steady-state values.
+
+    Insert provided numeric values into the systesm of steady state equations and check for non-zero residuals. This
+    is a "shallow" check in the sense that no effort is made to check dependencies between equations (that is,
+    sp.solve is not called). Partial steady states are allowed -- the function simply looks for numeric, non-zero values
+    after the provided values are substituted. Therefore, passing an incorrect value that would later cause a numeric
+    solver to fail is also not detected.
+
+    For example, the following system would be detected as having an incorrect steady-state: for :math:`x_1 = 0.5` :
+
+    .. math::
+
+        \begin{align}
+            x_1 - 1 &= 0 \\
+            x_2^ - 3 = 0
+        \end{align}
+
+    Because the first equation will reduce to :math:`-0.5` after simple substitution. On the other hand, this system
+    would not be marked at :math:`x_1 = 0.5`:
+
+    ..math::
+
+        \begin{align}
+            x_1 - x_2 &= 0 \\
+            x_2 - x_3 &= 0 \\
+            x_3 - 1 &= 0
+        \end{align}
+
+    Clearly this can be reduced to :math:`x_1 = 1$`, but no effort is made to perform these substitutions, so the error
+    will not be flagged. In general, these substitutions are non-trivial, and attempting to solve results in significant
+    time cost.
+
+    Parameters
+    ----------
+    steady_state_system: list of sp.Expr
+        System of model equations with all time indices set to the steady state
+    ss_dict: SymbolDictionary
+        Dictionary of user-provided steady state values. Expected to have TimeAwareSymbol variables as keys and numeric
+        values as values.
+    param_dict: SymbolDictionary
+        Dictionary of parameter values at which to solve for the steady state. Expected to have Symbol variables as
+         keys and numeric values as values.
+    tol: float
+        Radius around zero within which to consider values as zero. Default is 1e-8.
+    """
+    sub_dict = ss_dict.copy() | param_dict.copy()
+    subbed_system = [eq.subs(sub_dict.to_sympy()) for eq in steady_state_system]
+
+    # This has to use equality to check True -- sympy doesn't know the truth value of e.g. |x - 3| < 1e-8. But it does
+    # know that this is NOT the same as True.
+    invalid_equation_strings = [
+        str(eq)
+        for eq, subbed_eq in zip(steady_state_system, subbed_system)
+        if (sp.Abs(subbed_eq) < tol) == False  #  noqa
+    ]
+
+    if len(invalid_equation_strings) > 0:
+        msg = (
+            "User-provide steady state is not valid. The following equations had non-zero residuals "
+            "after subsitution:\n"
+        )
+        msg += "\n".join(invalid_equation_strings)
+        raise ValueError(msg)
+
+
+class Model:
+    def __init__(
+        self,
+        variables: list[TimeAwareSymbol],
+        shocks: list[TimeAwareSymbol],
+        equations: list[sp.Expr],
+        steady_state_relationships: list[sp.Eq],
+        param_dict: SymbolDictionary,
+        deterministic_dict: SymbolDictionary,
+        calib_dict: SymbolDictionary,
+        priors: tuple | None,
+        f_params: Callable[[np.ndarray, ...], SymbolDictionary],
+        f_ss_resid: Callable[[np.ndarray, ...], float],
+        f_ss: Callable[[np.ndarray, ...], SymbolDictionary],
+        f_ss_error: Callable[[np.ndarray, ...], np.ndarray],
+        f_ss_jac: Callable[[np.ndarray, ...], np.ndarray],
+        f_ss_error_grad: Callable[[np.ndarray, ...], np.ndarray],
+        f_ss_error_hess: Callable[[np.ndarray, ...], np.ndarray],
+        f_ss_error_hessp: Callable[[np.ndarray, ...], np.ndarray],
+        f_linearize: Callable,
+        backend: BACKENDS = "numpy",
+    ) -> None:
+        """
+        A Dynamic Stochastic General Equlibrium (DSGE) Model
+
+        Parameters
+        ----------
+        variables: list[TimeAwareSymbol]
+            List of variables in the model
+        shocks: list[TimeAwareSymbol]
+            List of shocks in the model
+        equations: list[sp.Expr]
+            List of equations in the model
+        param_dict: SymbolDictionary
+            Dictionary of parameters in the model
+        f_params: Callable
+            Function that returns a dictionary of parameter values given a dictionary of parameter values
+        f_ss_resid: Callable
+            Function that takes a dictionary of parameter values theta and steady-state variable values x_ss and
+            evaluates the system of model equations f(x_ss, theta) = 0.
+        f_ss: Callable
+            Function that takes current parameter values and returns a dictionary of steady-state values.
+        f_ss_error: Callable, optional
+            Function that takes a dictionary of parameter values theta and steady-state variable values x_ss and returns
+            a scalar error measure of x_ss given theta.
+            If None, the sum of squared residuals returned by f_ss_resid is used.
+        f_ss_error_grad: Callable, optional
+            Function that takes a dictionary of parameter values theta and steady-state variable values x_ss and returns
+            the gradients of the error function f_ss_error with respect to the steady-state variable values x_ss
+
+            If f_ss_error is not provided, an error will be raised if a gradient function is passed.
+        f_ss_error_hess: Callable, optional
+            Function that takes a dictionary of parameter values theta and steady-state variable values x_ss and returns
+            the Hessian of the error function f_ss_error with respect to the steady-state variable values x_ss
+
+            If f_ss_error is not provided, an error will be raised if a gradient function is passed.
+
+        f_ss_error_hessp: Callable, optional
+            Function that takes a dictionary of parameter values theta and steady-state variable values x_ss and returns
+            the Hessian-vector product of the error function f_ss_error with respect to the steady-state variable values x_ss
+
+
+        f_ss_jac: Callable, optional
+
+        f_linearize: Callable, optional
+
+        """
+
+        self.variables = variables
+        self.shocks = shocks
+        self.equations = equations
+        self.params = list(param_dict.to_sympy().keys())
+
+        self.deterministic_params = list(deterministic_dict.to_sympy().keys())
+        self.calibrated_params = list(calib_dict.to_sympy().keys())
+
+        self.steady_state_relationships = steady_state_relationships
+
+        self._all_names_to_symbols = {
+            get_name(x, base_name=True): x
+            for x in (
+                self.variables
+                + self.params
+                + self.calibrated_params
+                + self.deterministic_params
+                + self.shocks
+            )
+        }
+
+        self.priors = priors
+
+        self._default_params = param_dict.copy()
+        self.f_params = f_params
+        self.f_ss_resid = f_ss_resid
+
+        self.f_ss_error = f_ss_error
+        self.f_ss_error_grad = f_ss_error_grad
+        self.f_ss_error_hess = f_ss_error_hess
+        self.f_ss_error_hessp = f_ss_error_hessp
+
+        self.f_ss = f_ss
+        self.f_ss_jac = f_ss_jac
+
+        if backend == "numpy":
+            f_linearize = override_dummy_wrapper(f_linearize, "not_loglin_variable")
+        self.f_linearize = f_linearize
+
+    def parameters(self, **updates: float):
+        # Remove deterministic parameters for updates. These can appear **self.parameters() into a fitting function
+        deterministic_names = [x.name for x in self.deterministic_params]
+        updates = {k: v for k, v in updates.items() if k not in deterministic_names}
+
+        # Check for unknown updates (typos, etc)
+        param_dict = self._default_params.copy()
+        unknown_updates = set(updates.keys()) - set(param_dict.keys())
+        if unknown_updates:
+            raise ModelUnknownParameterError(list(unknown_updates))
+        param_dict.update(updates)
+
+        return self.f_params(**param_dict).to_string()
+
+    def get(self, name: str) -> sp.Symbol:
+        """
+        Get a variable or parameter by name
+        """
+        ss_requested = name.endswith("_ss")
+        name = name.removesuffix("_ss")
+
+        result = self._all_names_to_symbols.get(name)
+        if result is None:
+            close_match = difflib.get_close_matches(
+                name, [get_name(x) for x in self._all_names_to_symbols.keys()], n=1
+            )[0]
+            raise IndexError(
+                f"Did not find {name} among model objects. Did you mean {close_match}?"
+            )
+        if ss_requested:
+            return result.to_ss()
+        return result
+
+    def _validate_provided_steady_state_variables(
+        self, user_fixed_variables: Sequence[str]
+    ):
+        # User is allowed to pass the variable name either with or without the _ss suffix. Begin by normalizing the
+        # inputs
+        fixed_variables_normed = [x.removesuffix("_ss") for x in user_fixed_variables]
+
+        # Check for duplicated values. This should only be possible if the user passed both `x` and `x_ss`.
+        counts = [fixed_variables_normed.count(x) for x in fixed_variables_normed]
+        duplicates = [x for x, c in zip(fixed_variables_normed, counts) if c > 1]
+        if len(duplicates) > 0:
+            raise ValueError(
+                'The following variables were provided twice (once with a _ss prefix and once without):\n'
+                f'{", ".join(duplicates)}'
+            )
+
+        # Check that all variables are in the model
+        model_variable_names = [x.base_name for x in self.variables]
+        unknown_fixed = set(fixed_variables_normed) - set(model_variable_names)
+
+        if len(unknown_fixed) > 0:
+            raise ValueError(
+                f"The following variables or calibrated parameters were given fixed steady state values but are "
+                f"unknown to the model: {', '.join(unknown_fixed)}"
+            )
+
+    def steady_state(
+        self,
+        how: Literal["analytic", "root", "minimize"] = "analytic",
+        use_jac=True,
+        use_hess=True,
+        use_hessp=False,
+        progressbar=True,
+        optimizer_kwargs: dict | None = None,
+        verbose=True,
+        bounds: dict[str, tuple[float, float]] | None = None,
+        fixed_values: dict[str, float] | None = None,
+        jitter_x0: bool = False,
+        **updates: float,
+    ) -> SteadyStateResults:
+        """
+        Solve for the deterministic steady state of the DSGE model
+
+
+        Parameters
+        ----------
+        how: str, one of ['analytic', 'root', 'minimize'], default: 'analytic'
+            Method to use to solve for the steady state. If ``'analytic'``, the model is solved analytically using
+            user-provided steady-state equations. This is only possible if the steady-state equations are fully
+            defined. If ``'root'``, the steady state is solved using a root-finding algorithm. If ``'minimize'``, the
+            steady state is solved by minimizing a squared error loss function.
+
+        use_jac: bool, default: True
+            Flag indicating whether to use the Jacobian of the error function when solving for the steady state. Ignored
+            if ``how`` is 'analytic'.
+
+        use_hess: bool, default: False
+            Flag indicating whether to use the Hessian of the error function when solving for the steady state. Ignored
+            if ``how`` is not 'minimize'
+
+        use_hessp: bool, default: True
+            Flag indicating whether to use the Hessian-vector product of the error function when solving for the
+            steady state. This should be preferred over ``use_hess`` if your chosen method supports it. For larger
+            problems it is substantially more performant.
+            Ignored if ``how`` not "minimize".
+
+        progressbar: bool, default: True
+            Flag indicating whether to display a progress bar when solving for the steady state.
+
+        optimizer_kwargs: dict, optional
+            Keyword arguments passed to either scipy.optimize.root or scipy.optimize.minimize, depending on the value of
+            ``how``. Common argments include:
+
+            - 'method': str,
+                The optimization method to use. Default is ``'hybr'`` for ``how = 'root'`` and ``trust-krylov`` for
+                ``how = 'minimize'``
+            - 'maxiter': int,
+                The maximum number of iterations to use. Default is 5000. This argument will be automatically renamed
+                to match the argument expected by different optimizers (for example, the ``'hybr'`` method uses
+                ``maxfev``).
+
+        verbose: bool, default True
+            If true, print a message about convergence (or not) to the console .
+
+        bounds: dict, optional
+            Dictionary of bounds for the steady-state variables. The keys are the variable names and the values are
+            tuples of the form (lower_bound, upper_bound). These are passed to the scipy.optimize.minimize function,
+            see that docstring for more information.
+
+        fixed_values: dict, optional
+            Dictionary of fixed values for the steady-state variables. The keys are the variable names and the values
+            are the fixed values. These are not check for validity, and passing an inaccurate value may result in the
+            system becoming unsolvable.
+
+        jitter_x0: bool
+            Whether to apply some small N(0, 1e-4) jitter to the initial point
+
+        **updates: float, optional
+            Parameter values at which to solve the steady state. Passed to self.parameters. If not provided, the default
+            parameter values (those originally defined during model construction) are used.
+
+        Returns
+        -------
+        steady_state: SteadyStateResults
+            Dictionary of steady-state values
+
+        """
+        if optimizer_kwargs is None:
+            optimizer_kwargs = {}
+
+        if fixed_values is None:
+            f_ss = self.f_ss
+
+        else:
+            self._validate_provided_steady_state_variables(list(fixed_values.keys()))
+            fixed_symbols = [safe_to_ss(self.get(x)) for x in fixed_values.keys()]
+
+            fixed_dict = SymbolDictionary(
+                {
+                    symbol: value
+                    for symbol, value in zip(fixed_symbols, fixed_values.values())
+                },
+            ).to_string()
+
+            f_ss = add_more_ss_values_wrapper(self.f_ss, fixed_dict)
+
+        # This logic could be made a lot of complex by looking into solver-specific arguments passed via
+        # "options"
+        tol = optimizer_kwargs.get("tol", 1e-8)
+
+        param_dict = self.parameters(**updates)
+        ss_dict = SteadyStateResults()
+        ss_system = system_to_steady_state(self.equations, self.shocks)
+        unknown_eq_idx = np.full(len(ss_system), True)
+
+        # The default value is analytic, because that's best if the user gave everything we need to proceed. If he gave
+        # nothing though, use minimize as a fallback default.
+        if how == "analytic" and f_ss is None:
+            how = "minimize"
+        else:
+            # If we have at least some user information, check if its is complete. If it's not, we will minimize
+            # with the user-provided values fixed.
+            ss_dict = f_ss(**param_dict) if f_ss is not None else ss_dict
+            if len(ss_dict) != 0 and len(ss_dict) != len(self.variables):
+                if how == "root":
+                    zero_eq_mask = get_known_equation_mask(
+                        steady_state_system=ss_system,
+                        ss_dict=ss_dict,
+                        param_dict=param_dict,
+                        tol=tol,
+                    )
+                    if sum(zero_eq_mask) != len(ss_dict):
+                        n_eliminated = sum(zero_eq_mask)
+                        raise ValueError(
+                            'Solving a partially provided steady state with how = "root" is only allowed if applying '
+                            f'the given values results in a new square system.\n'
+                            f'Found: {len(ss_dict)} provided steady state value{"s" if len(ss_dict) != 1 else ""}\n'
+                            f'Eliminated: {n_eliminated} equation{"s" if n_eliminated != 1 else ""}.'
+                        )
+                    unknown_eq_idx = ~zero_eq_mask
+                else:
+                    how = "minimize"
+
+            # Or, if we have everything, we're done.
+            elif len(ss_dict) == len(self.variables):
+                resid = self.f_ss_resid(**param_dict, **ss_dict)
+                success = np.allclose(resid, 0.0, atol=1e-8)
+                ss_dict.success = success
+                return ss_dict
+
+        # Quick and dirty check of user-provided steady-state validity. This is NOT robust at all.
+        validate_user_steady_state_simple(
+            steady_state_system=ss_system,
+            ss_dict=ss_dict,
+            param_dict=param_dict,
+            tol=tol,
+        )
+
+        ss_variables = [x.to_ss() for x in self.variables] + list(
+            self.calibrated_params
+        )
+
+        known_variables = (
+            [] if f_ss is None else list(f_ss(**self.parameters()).to_sympy().keys())
+        )
+
+        vars_to_solve = [var for var in ss_variables if var not in known_variables]
+        unknown_var_idx = np.array(
+            [x in vars_to_solve for x in ss_variables], dtype="bool"
+        )
+
+        if how == "root":
+            res = self._solve_steady_state_with_root(
+                f_ss=f_ss,
+                use_jac=use_jac,
+                vars_to_solve=vars_to_solve,
+                unknown_var_idx=unknown_var_idx,
+                unknown_eq_idx=unknown_eq_idx,
+                progressbar=progressbar,
+                optimizer_kwargs=optimizer_kwargs,
+                jitter_x0=jitter_x0,
+                **updates,
+            )
+
+        elif how == "minimize":
+            res = self._solve_steady_state_with_minimize(
+                f_ss=f_ss,
+                use_jac=use_jac,
+                use_hess=use_hess,
+                use_hessp=use_hessp,
+                vars_to_solve=vars_to_solve,
+                unknown_var_idx=unknown_var_idx,
+                unknown_eq_idx=unknown_var_idx,
+                progressbar=progressbar,
+                bounds=bounds,
+                optimizer_kwargs=optimizer_kwargs,
+                jitter_x0=jitter_x0,
+                **updates,
+            )
+        else:
+            raise NotImplementedError()
+
+        provided_ss_values = f_ss(**param_dict).to_sympy() if f_ss is not None else {}
+        optimizer_results = SymbolDictionary(
+            {var: res.x[i] for i, var in enumerate(vars_to_solve)}
+        )
+        res_dict = optimizer_results | provided_ss_values
+        res_dict = SteadyStateResults(
+            {x: res_dict[x] for x in ss_variables}
+        ).to_string()
+
+        return postprocess_optimizer_res(
+            res=res,
+            res_dict=res_dict,
+            f_resid=ft.partial(self.f_ss_resid, **param_dict),
+            f_jac=ft.partial(self.f_ss_error_grad, **param_dict),
+            tol=tol,
+            verbose=verbose,
+        )
+
+    def _evaluate_steady_state(self, **updates: float):
+        param_dict = self.parameters(**updates)
+        ss_dict = self.f_ss(**param_dict)
+
+        return self.f_ss_resid(**param_dict, **ss_dict)
+
+    def _solve_steady_state_with_root(
+        self,
+        f_ss,
+        use_jac: bool = True,
+        vars_to_solve: list[TimeAwareSymbol] | None = None,
+        unknown_var_idx: np.ndarray | None = None,
+        unknown_eq_idx: np.ndarray | None = None,
+        progressbar: bool = True,
+        optimizer_kwargs: dict | None = None,
+        jitter_x0: bool = False,
+        **param_updates,
+    ):
+        if optimizer_kwargs is None:
+            optimizer_kwargs = {}
+        optimizer_kwargs = deepcopy(optimizer_kwargs)
+
+        maxiter = optimizer_kwargs.pop("maxiter", 5000)
+        method = optimizer_kwargs.pop("method", "hybr")
+
+        if "options" not in optimizer_kwargs:
+            optimizer_kwargs["options"] = {}
+
+        if method in ["hybr", "df-sane"]:
+            optimizer_kwargs["options"].update({"maxfev": maxiter})
+        else:
+            optimizer_kwargs["options"].update({"maxiter": maxiter})
+
+        x0 = _initialize_x0(optimizer_kwargs, vars_to_solve, jitter_x0)
+
+        param_dict = self.parameters(**param_updates)
+        wrapper = ft.partial(
+            scipy_wrapper,
+            variables=vars_to_solve,
+            unknown_var_idxs=unknown_var_idx,
+            unknown_eq_idxs=unknown_eq_idx,
+            f_ss=f_ss,
+        )
+
+        f = wrapper(self.f_ss_resid)
+        f_jac = wrapper(self.f_ss_jac) if use_jac else None
+
+        with np.errstate(all="ignore"):
+            res = root(
+                f=f,
+                x0=x0,
+                args=(param_dict,),
+                jac=f_jac,
+                method=method,
+                progressbar=progressbar,
+                **optimizer_kwargs,
+            )
+
+        return res
+
+    def _solve_steady_state_with_minimize(
+        self,
+        f_ss,
+        use_jac: bool = True,
+        use_hess: bool = False,
+        use_hessp: bool = True,
+        vars_to_solve: list[str] | None = None,
+        unknown_var_idx: np.ndarray | None = None,
+        unknown_eq_idx: np.ndarray | None = None,
+        progressbar: bool = True,
+        optimizer_kwargs: dict | None = None,
+        jitter_x0: bool = False,
+        bounds: dict[str, tuple[float, float]] | None = None,
+        **param_updates,
+    ):
+        if optimizer_kwargs is None:
+            optimizer_kwargs = {}
+        optimizer_kwargs = deepcopy(optimizer_kwargs)
+
+        x0 = _initialize_x0(optimizer_kwargs, vars_to_solve, jitter_x0)
+        tol = optimizer_kwargs.pop("tol", 1e-30)
+
+        user_bounds = {} if bounds is None else bounds
+        bound_dict = {x.name: infer_variable_bounds(x) for x in vars_to_solve}
+        bound_dict.update(user_bounds)
+
+        bounds = [bound_dict[x.name] for x in vars_to_solve]
+        has_bounds = any([x != (None, None) for x in bounds])
+
+        method = optimizer_kwargs.pop(
+            "method", "trust-ncg" if not has_bounds else "trust-constr"
+        )
+        if method not in ["trust-constr", "L-BFGS-B", "powell"]:
+            has_bounds = False
+
+        maxiter = optimizer_kwargs.pop("maxiter", 5000)
+        if "options" not in optimizer_kwargs:
+            optimizer_kwargs["options"] = {}
+        optimizer_kwargs["options"].update({"maxiter": maxiter})
+        if method == "L-BFGS-B":
+            optimizer_kwargs["options"].update({"maxfun": maxiter})
+
+        param_dict = self.parameters(**param_updates)
+
+        wrapper = ft.partial(
+            scipy_wrapper,
+            variables=vars_to_solve,
+            unknown_var_idxs=unknown_var_idx,
+            unknown_eq_idxs=unknown_eq_idx,
+            f_ss=f_ss,
+        )
+
+        if use_hess and use_hessp:
+            _log.warning(
+                "Both use_hess and use_hessp are set to True. use_hessp will be used."
+            )
+            use_hess = False
+
+        f = wrapper(self.f_ss_error)
+        f_jac = wrapper(self.f_ss_error_grad) if use_jac else None
+        f_hess = wrapper(self.f_ss_error_hess) if use_hess else None
+        f_hessp = wrapper(self.f_ss_error_hessp, include_p=True) if use_hessp else None
+
+        res = minimize(
+            f=f,
+            x0=x0,
+            args=(param_dict,),
+            jac=f_jac,
+            hess=f_hess,
+            hessp=f_hessp,
+            method=method,
+            bounds=bounds if has_bounds else None,
+            tol=tol,
+            progressbar=progressbar,
+            **optimizer_kwargs,
+        )
+
+        return res
+
+    def linearize_model(
+        self,
+        order: Literal[1] = 1,
+        log_linearize: bool = True,
+        not_loglin_variables: list[str] | None = None,
+        steady_state: dict | None = None,
+        loglin_negative_ss: bool = False,
+        steady_state_kwargs: dict | None = None,
+        verbose: bool = True,
+        **parameter_updates,
+    ):
+        if order != 1:
+            raise NotImplementedError(
+                "Only first order linearization is currently supported."
+            )
+        if steady_state_kwargs is None:
+            steady_state_kwargs = {}
+
+        param_dict = self.parameters(**parameter_updates)
+
+        if steady_state is None:
+            steady_state = self.steady_state(
+                **self.parameters(**param_dict), **steady_state_kwargs
+            )
+
+        not_loglin_flags = make_not_loglin_flags(
+            variables=self.variables,
+            calibrated_params=self.calibrated_params,
+            steady_state=steady_state,
+            log_linearize=log_linearize,
+            not_loglin_variables=not_loglin_variables,
+            loglin_negative_ss=loglin_negative_ss,
+            verbose=verbose,
+        )
+
+        A, B, C, D = self.f_linearize(
+            **param_dict, **steady_state, not_loglin_variable=not_loglin_flags
+        )
+
+        return A, B, C, D
+
+    def solve_model(
+        self,
+        solver="cycle_reduction",
+        log_linearize: bool = True,
+        not_loglin_variables: list[str] | None = None,
+        order: Literal[1] = 1,
+        loglin_negative_ss: bool = False,
+        steady_state: dict | None = None,
+        steady_state_kwargs: dict | None = None,
+        tol: float = 1e-8,
+        max_iter: int = 1000,
+        verbose: bool = True,
+        on_failure="error",
+        **parameter_updates,
+    ) -> tuple[np.ndarray | None, np.ndarray | None]:
+        """
+        Solve for the linear approximation to the policy function via perturbation. Adapted from R code in the gEcon
+        package by Grzegorz Klima, Karol Podemski, and Kaja Retkiewicz-Wijtiwiak., http://gecon.r-forge.r-project.org/.
+
+        Parameters
+        ----------
+        solver: str, default: 'cycle_reduction'
+            Name of the algorithm to solve the linear solution. Currently "cycle_reduction" and "gensys" are supported.
+            Following Dynare, cycle_reduction is the default, but note that gEcon uses gensys.
+        log_linearize: bool, default: True
+            Whether to log-linearize the model. If False, the model will be solved in levels.
+        not_loglin_variables: list of strings, optional
+            Variables to not log linearize when solving the model. Variables with steady state values close to zero
+            (or negative) will be automatically selected to not log linearize. Ignored if log_linearize is False.
+        order: int, default: 1
+            Order of taylor expansion to use to solve the model. Currently only 1st order approximation is supported.
+        steady_state: dict, optional
+            Dictionary of steady-state solutions. If not provided, the steady state will be solved for using the
+            ``steady_state`` method.
+        steady_state_kwargs: dict, optional
+            Keyword arguments passed to the `steady_state` method. Ignored if a steady-state solution is provided
+            via the steady_state argument, Default is None.
+        loglin_negative_ss: bool, default is False
+            Whether to force log-linearization of variable with negative steady-state. This is impossible in principle
+            (how can :math:`exp(x_ss)` be negative?), but can still be done; see the docstring for
+            :fun:`perturbation.linearize_model` for details. Use with caution, as results will not correct. Ignored if
+            log_linearize is False.
+        tol: float, default 1e-8
+            Desired level of floating point accuracy in the solution
+        max_iter: int, default: 1000
+            Maximum number of cycle_reduction iterations. Not used if solver is 'gensys'.
+        verbose: bool, default: True
+            Flag indicating whether to print solver results to the terminal
+        on_failure: str, one of ['error', 'ignore'], default: 'error'
+            Instructions on what to do if the algorithm to find a linearized policy matrix. "Error" will raise an error,
+            while "ignore" will return None. "ignore" is useful when repeatedly solving the model, e.g. when sampling.
+        parameter_updates: dict
+            New parameter values at which to solve the model. Unspecified values will be taken from the initial values
+            set in the GCN file.
+
+        Returns
+        -------
+        T: np.ndarray, optional
+            Transition matrix, approximated to the requested order. Represents the policy function, governing agent's
+            optimal state-conditional actions. If the solver fails, None is returned instead.
+
+        R: np.ndarray, optional
+            Selection matrix, approximated to the requested order. Represents the state- and agent-conditional
+            transmission of stochastic shocks through the economy. If the solver fails, None is returned instead.
+        """
+        if on_failure not in ["error", "ignore"]:
+            raise ValueError(
+                f'Parameter on_failure must be one of "error" or "ignore", found {on_failure}'
+            )
+        if steady_state_kwargs is None:
+            steady_state_kwargs = {}
+
+        ss_dict = _maybe_solve_steady_state(
+            self, steady_state, steady_state_kwargs, parameter_updates
+        )
+        n_variables = len(self.variables)
+
+        A, B, C, D = self.linearize_model(
+            order=order,
+            log_linearize=log_linearize,
+            not_loglin_variables=not_loglin_variables,
+            steady_state=ss_dict.to_string(),
+            loglin_negative_ss=loglin_negative_ss,
+            verbose=verbose,
+            **parameter_updates,
+        )
+
+        if solver == "gensys":
+            gensys_results = solve_policy_function_with_gensys(A, B, C, D, tol)
+            G_1, constant, impact, f_mat, f_wt, y_wt, gev, eu, loose = gensys_results
+
+            success = all([x == 1 for x in eu[:2]])
+
+            if not success:
+                if on_failure == "error":
+                    raise GensysFailedException(eu)
+                elif on_failure == "ignore":
+                    if verbose:
+                        message = interpret_gensys_output(eu)
+                        _log.info(message)
+
+                    return None, None
+
+            if verbose:
+                message = interpret_gensys_output(eu)
+                _log.info(message)
+                _log.info(
+                    "Policy matrices have been stored in attributes model.P, model.Q, model.R, and model.S"
+                )
+
+            T = G_1[:n_variables, :][:, :n_variables]
+            R = impact[:n_variables, :]
+
+        elif solver == "cycle_reduction":
+            (
+                T,
+                R,
+                result,
+                log_norm,
+            ) = solve_policy_function_with_cycle_reduction(
+                A, B, C, D, max_iter, tol, verbose
+            )
+            if T is None:
+                if on_failure == "error":
+                    raise GensysFailedException(result)
+                elif on_failure == "ignore":
+                    if verbose:
+                        _log.info(result)
+                    return None, None
+        else:
+            raise NotImplementedError(
+                'Only "cycle_reduction" and "gensys" are valid values for solver'
+            )
+
+        if verbose:
+            check_perturbation_solution(A, B, C, D, T, R, tol=tol)
+
+        return np.ascontiguousarray(T), np.ascontiguousarray(R)
+
+
+def _maybe_solve_steady_state(
+    model: Model,
+    steady_state: dict | None,
+    steady_state_kwargs: dict | None,
+    parameter_updates: dict | None,
+):
+    if steady_state is None:
+        return model.steady_state(
+            **model.parameters(**parameter_updates), **steady_state_kwargs
+        )
+
+    ss_resid = model.f_ss_resid(**steady_state, **model.parameters(**parameter_updates))
+    unsatisfied_flags = np.abs(ss_resid) > 1e-8
+    unsatisfied_eqs = [
+        f"Equation {i}" for i, flag in enumerate(unsatisfied_flags) if flag
+    ]
+
+    if np.any(unsatisfied_flags):
+        raise SteadyStateNotFoundError(unsatisfied_eqs)
+    steady_state.success = True
+
+    return steady_state
+
+
+def _maybe_linearize_model(
+    model: Model,
+    A: np.ndarray | None,
+    B: np.ndarray | None,
+    C: np.ndarray | None,
+    D: np.ndarray | None,
+    verbose: bool = True,
+    **linearize_model_kwargs,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Linearize a model if required, or return the provided matrices
+
+    Parameters
+    ----------
+    model: Model
+        DSGE model
+    A: np.ndarray, optional
+        Matrix of partial derivatives of model equations with respect to variables at time t-1, evaluated at the
+        steady-state
+    B: np.ndarray, optional
+        Matrix of partial derivatives of model equations with respect to variables at time t, evaluated at the
+        steady-state
+    C: np.ndarray, optional
+        Matrix of partial derivatives of model equations with respect to variables at time t+1, evaluated at the
+        steady-state
+    D: np.ndarray, optional
+        Matrix of partial derivatives of model equations with respect to stochastic innovations, evaluated at the
+        steady-state
+    verbose: bool, default: True
+        Flag indicating whether to print details about the linearization process to the console
+    linearize_model_kwargs
+        Arguments forwarded to the ``model.linearize_model`` method. Ignored if all of A, B, C, and D are provided.
+
+    Returns
+    -------
+    linear_system: np.ndarray, np.ndarray, np.ndarray, np.ndarray
+    """
+
+    n_matrices = sum(x is not None for x in [A, B, C, D])
+    if n_matrices < 4 and n_matrices > 0 and verbose:
+        _log.warning(
+            f"Passing an incomplete subset of A, B, C, and D (you passed {n_matrices}) will still trigger "
+            f"``model.linearize_model`` (which might be expensive). Pass all to avoid this, or None to silence "
+            f"this warning."
+        )
+        A = None
+        B = None
+        C = None
+        D = None
+
+    if all(x is None for x in [A, B, C, D]):
+        A, B, C, D = model.linearize_model(verbose=verbose, **linearize_model_kwargs)
+
+    return A, B, C, D
+
+
+def _maybe_solve_model(
+    model: Model, T: np.ndarray | None, R: np.ndarray | None, **solve_model_kwargs
+):
+    """
+    Solve for the linearized policy matrix of a model if required, or return the provided T and R
+
+    Parameters
+    ----------
+    model: Model
+        DSGE Model assoicated with T and R
+    T: np.ndarray, optional
+        Transition matrix of the solved system. If None, this will be computed using the model's ``solve_model``
+        method.
+    R: np.ndarray
+        Selection matrix of the solved system. If None, this will be computed using the model's ``solve_model`` method.
+    **solve_model_kwargs
+        Arguments forwarded to the ``solve_model`` method. Ignored if T and R are provided.
+
+    Returns
+    -------
+    T: np.ndarray, optional
+        Transition matrix, approximated to the requested order. Represents the policy function, governing agent's
+        optimal state-conditional actions. If the solver fails, None is returned instead.
+
+    R: np.ndarray, optional
+        Selection matrix, approximated to the requested order. Represents the state- and agent-conditional
+        transmission of stochastic shocks through the economy. If the solver fails, None is returned instead.
+    """
+    n_matrices = sum(x is not None for x in [T, R])
+    if n_matrices == 1:
+        _log.warning(
+            "Passing only one of T or R will still trigger ``model.solve_model`` (which might be expensive). "
+            "Pass both to avoid this, or None to silence this warning."
+        )
+        T = None
+        R = None
+
+    if T is None and R is None:
+        T, R = model.solve_model(**solve_model_kwargs)
+
+    return T, R
+
+
+def _validate_shock_options(
+    shock_std_dict: dict[str, float] | None,
+    shock_cov_matrix: np.ndarray | None,
+    shock_std: float | np.ndarray | list | None,
+    shocks: list[TimeAwareSymbol],
+):
+    n_shocks = len(shocks)
+    n_provided = sum(
+        x is not None for x in [shock_std_dict, shock_cov_matrix, shock_std]
+    )
+    if n_provided > 1 or n_provided == 0:
+        raise ValueError(
+            "Exactly one of shock_std_dict, shock_cov_matrix, or shock_std should be provided. You passed "
+            f"{n_provided}."
+        )
+
+    if shock_cov_matrix is not None:
+        if any(s != n_shocks for s in shock_cov_matrix.shape):
+            raise ValueError(
+                f"Incorrect covariance matrix shape. Expected ({n_shocks}, {n_shocks}), "
+                f"found {shock_cov_matrix.shape}"
+            )
+
+    if shock_std_dict is not None:
+        shock_names = [x.base_name for x in shocks]
+        missing = [x for x in shock_std_dict.keys() if x not in shock_names]
+        extra = [x for x in shock_names if x not in shock_std_dict.keys()]
+        if len(missing) > 0:
+            raise ValueError(
+                f"If shock_std_dict is specified, it must give values for all shocks. The following shocks were not "
+                f"found among the provided keys: {', '.join(missing)}"
+            )
+        if len(extra) > 0:
+            raise ValueError(
+                f"Unexpected shocks in shock_std_dict. The following names were not found among the model shocks: "
+                f"{', '.join(extra)}"
+            )
+
+    if shock_std is not None:
+        if isinstance(shock_std, np.ndarray | list):
+            shock_std = cast(np.ndarray | list, shock_std)
+            if len(shock_std) != n_shocks:
+                raise ValueError(
+                    f"Length of shock_std ({len(shock_std)}) does not match the number of shocks ({n_shocks})"
+                )
+            if not np.all(shock_std > 0):
+                raise ValueError("Shock standard deviations must be positive")
+        elif isinstance(shock_std, int | float):
+            if shock_std < 0:
+                raise ValueError("Shock standard deviation must be positive")
+
+
+def _validate_simulation_options(shock_size, shock_cov, shock_trajectory) -> None:
+    options = [shock_size, shock_cov, shock_trajectory]
+    n_options = sum(x is not None for x in options)
+
+    if n_options != 1:
+        raise ValueError(
+            "Specify exactly 1 of shock_size, shock_cov, or shock_trajectory"
+        )
+
+
+def build_Q_matrix(
+    model_shocks: list[TimeAwareSymbol],
+    shock_std_dict: dict[str, float] | None = None,
+    shock_cov_matrix: np.ndarray | None = None,
+    shock_std: np.ndarray | list | float | None = None,
+) -> np.array:
+    """
+    Take different options for user input and reconcile them into a covariance matrix. Exactly one or zero of shock_dict
+    or shock_cov_matrix should be provided. Then, proceed according to the following logic:
+
+    - If `shock_cov_matrix` is provided, it is Q. Return it.
+    - If `shock_dict` is provided, insert these into a diagonal matrix at locations according to `model_shocks`.
+
+    For values missing from `shock_dict`, or if neither `shock_dict` nor `shock_cov_matrix` are provided:
+
+    - Fill missing values using the mean of the prior defined in `shock_priors`
+    - If no prior is set, fill the value with `default_value`.
+
+    Note that the only way to get off-diagonal elements is to explicitly pass the entire covariance matrix.
+
+    Parameters
+    ----------
+    model_shocks: list of str
+        List of model shock names, used to infer positions in the covariance matrix
+    shock_std_dict: dict, optional
+        Dictionary of shock names and standard deviations to be used to build Q
+    shock_cov_matrix: array, optional
+        An (n_shocks, n_shocks) covariance matrix describing the exogenous shocks
+    shock_std: float or sequence of float, optional
+        Standard deviation of all model shocks. If float, the same value will be used for all shocks. If sequence, the
+        length must match the number of shocks.
+
+    Raises
+    ------
+    LinalgError
+        If the provided Q is not positive semi-definite
+    ValueError
+        If both model_shocks and shock_dict are provided
+
+    Returns
+    -------
+    Q: ndarray
+        Shock variance-covariance matrix
+    """
+
+    _validate_shock_options(
+        shock_std_dict=shock_std_dict,
+        shock_cov_matrix=shock_cov_matrix,
+        shock_std=shock_std,
+        shocks=model_shocks,
+    )
+
+    if shock_cov_matrix is not None:
+        return shock_cov_matrix
+
+    elif shock_std_dict is not None:
+        shock_names = [x.base_name for x in model_shocks]
+        indices = [shock_names.index(x) for x in shock_std_dict.keys()]
+        Q = np.zeros((len(model_shocks), len(model_shocks)))
+        for i, (key, value) in enumerate(shock_std_dict.items()):
+            Q[indices[i], indices[i]] = value**2
+        return Q
+
+    else:
+        return np.eye(len(model_shocks)) * shock_std**2
+
+
+def stationary_covariance_matrix(
+    model: Model,
+    T: np.ndarray | None = None,
+    R: np.ndarray | None = None,
+    shock_std_dict: dict[str, float] | None = None,
+    shock_cov_matrix: np.ndarray | None = None,
+    shock_std: np.ndarray | list | float | None = None,
+    return_df: bool = True,
+    **solve_model_kwargs,
+) -> np.ndarray | pd.DataFrame:
+    """
+    Compute the stationary covariance matrix of the solved system by solving the associated discrete lyapunov
+    equation.
+
+    In order to construct the shock covariance matrix, exactly one of shock_dict, shock_cov_matrix, or shock_std should
+    be provided.
+
+    Parameters
+    ----------
+    model: Model
+        DSGE Model assoicated with T and R
+    T: np.ndarray, optional
+        Transition matrix of the solved system. If None, this will be computed using the model's ``solve_model``
+        method.
+    R: np.ndarray
+        Selection matrix of the solved system. If None, this will be computed using the model's ``solve_model`` method.
+    shock_std_dict: dict, optional
+        A dictionary of shock sizes to be used to compute the stationary covariance matrix.
+    shock_cov_matrix: array, optional
+        An (n_shocks, n_shocks) covariance matrix describing the exogenous shocks
+    shock_std: float, optional
+        Standard deviation of all model shocks.
+    return_df: bool
+        If True, return the covariance matrix as a DataFrame
+    **solve_model_kwargs
+        Arguments forwarded to the ``solve_model`` method. Ignored if T and R are provided.
+
+    Returns
+    -------
+    Sigma: np.ndarray | pd.DataFrame
+        Stationary covariance matrix of the linearized model. Datatype depends on the variable of the ``return_df``
+        argument.
+    """
+    shocks = model.shocks
+    _validate_shock_options(
+        shock_std_dict=shock_std_dict,
+        shock_cov_matrix=shock_cov_matrix,
+        shock_std=shock_std,
+        shocks=shocks,
+    )
+
+    T, R = _maybe_solve_model(model, T, R, **solve_model_kwargs)
+
+    Q = build_Q_matrix(
+        model_shocks=shocks,
+        shock_std_dict=shock_std_dict,
+        shock_cov_matrix=shock_cov_matrix,
+        shock_std=shock_std,
+    )
+
+    RQRT = np.linalg.multi_dot([R, Q, R.T])
+    Sigma = linalg.solve_discrete_lyapunov(T, RQRT)
+
+    if return_df:
+        variables = [x.base_name for x in model.variables]
+        Sigma = pd.DataFrame(Sigma, index=variables, columns=variables)
+
+    return Sigma
+
+
+def check_bk_condition(
+    model: Model,
+    *,
+    A: np.ndarray | None = None,
+    B: np.ndarray | None = None,
+    C: np.ndarray | None = None,
+    D: np.ndarray | None = None,
+    tol=1e-8,
+    verbose=True,
+    on_failure: Literal["raise", "ignore"] = "ignore",
+    return_value: Literal["dataframe", "bool", None] = "dataframe",
+    **linearize_model_kwargs,
+) -> bool | pd.DataFrame | None:
+    """
+    Compute the generalized eigenvalues of system in the form presented in [1]. Per [2], the number of
+    unstable eigenvalues (|v| > 1) should not be greater than the number of forward-looking variables. Failing
+    this test suggests timing problems in the definition of the model.
+
+    Parameters
+    ----------
+    model: Model
+        DSGE model
+    A: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to past variables
+        values that are known when decision-making: those with t-1 subscripts.
+    B: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to variables that
+        are observed when decision-making: those with t subscripts.
+    C: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to variables that
+        enter in expectation when decision-making: those with t+1 subscripts.
+    D: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to exogenous shocks.
+    verbose: bool, default: True
+        Flag to print the results of the test, otherwise the eigenvalues are returned without comment.
+    on_failure: str, default: 'ignore'
+        Action to take if the Blanchard-Kahn condition is not satisfied. Valid values are 'ignore' and 'raise'.
+    return_value: string, default: 'dataframe'
+        Controls what is returned by the function. Valid values are 'dataframe', 'bool', and 'none'.
+        If df, a dataframe containing eigenvalues is returned. If 'bool', a boolean indicating whether the BK
+        condition is satisfied. If None, nothing is returned.
+    tol: float, 1e-8
+        Tolerance below which numerical values are considered zero
+
+    Returns
+    -------
+    bk_result, bool or pd.DataFrame, optional.
+        Return value requested. Datatype corresponds to what was requested in the ``return_value`` argument:
+        - None, If return_value is 'none'
+        - condition_satisfied, bool, if return_value is 'bool', returns True if the Blanchard-Kahn condition is
+          satisfied, False otherwise.
+        - Eigenvalues, pd.DataFrame, if return_value is 'df', returns a dataframe containing the real and imaginary
+          components of the system's, eigenvalues, along with their modulus.
+    """
+
+    A, B, C, D = _maybe_linearize_model(
+        model, A, B, C, D, verbose=verbose, **linearize_model_kwargs
+    )
+    bk_result = _check_bk_condition(
+        A,
+        B,
+        C,
+        D,
+        tol=tol,
+        verbose=verbose,
+        on_failure=on_failure,
+        return_value=return_value,
+    )
+    return bk_result
+
+
+@nb.njit(cache=True)
+def _compute_autocovariance_matrix(T, Sigma, n_lags=5, correlation=True):
+    """Compute the autocorrelation matrix for the given state-space model.
+
+    Parameters
+    ----------
+    T: np.ndarray, optional
+        Transition matrix of the solved system.
+    Sigma: np.ndarray
+        Stationary covariance matrix of the linearized model
+    n_lags : int, optional
+        The number of lags for which to compute the autocorrelation matrices.
+    correlation: bool
+        If True, return the autocorrelation matrices instead of the autocovariance matrices.
+
+    Returns
+    -------
+    acov : ndarray
+        An array of shape (n_lags, n_variables, n_variables) whose (i, j, k)-th entry gives the autocorrelation
+        (or autocovaraince) between variables j and k at lag i.
+    """
+
+    n_vars = T.shape[0]
+    auto_coors = np.empty((n_lags, n_vars, n_vars))
+    std_vec = np.sqrt(np.diag(Sigma))
+
+    if correlation:
+        normalization_factor = np.outer(std_vec, std_vec)
+    else:
+        normalization_factor = np.ones_like(Sigma)
+
+    for i in range(n_lags):
+        auto_coors[i] = np.linalg.matrix_power(T, i) @ Sigma / normalization_factor
+
+    return auto_coors
+
+
+def autocovariance_matrix(
+    model: Model,
+    T: np.ndarray | None = None,
+    R: np.ndarray | None = None,
+    shock_std_dict: dict[str, float] | None = None,
+    shock_cov_matrix: np.ndarray | None = None,
+    shock_std: np.ndarray | list | float | None = None,
+    n_lags: int = 10,
+    correlation=False,
+    return_xr=True,
+    **solve_model_kwargs,
+):
+    """
+    Computes the model's autocovariance matrix using the stationary covariance matrix. Alteratively, the autocorrelation
+    matrix can be returned by specifying ``correlation = True``.
+
+    In order to construct the shock covariance matrix, exactly one of shock_dict, shock_cov_matrix, or shock_std should
+    be provided.
+
+    Parameters
+    ----------
+    model: Model
+        DSGE Model assoicated with T and R
+    T: np.ndarray, optional
+        Transition matrix of the solved system. If None, this will be computed using the model's ``solve_model``
+        method.
+    R: np.ndarray
+        Selection matrix of the solved system. If None, this will be computed using the model's ``solve_model`` method.
+    shock_std_dict: dict, optional
+        A dictionary of shock sizes to be used to compute the stationary covariance matrix.
+    shock_cov_matrix: array, optional
+        An (n_shocks, n_shocks) covariance matrix describing the exogenous shocks
+    shock_std: float, optional
+        Standard deviation of all model shocks.
+    n_lags: int
+        Number of lags of auto-covariance and cross-covariance to compute. Default is 10.
+    correlation: bool
+        If True, return the autocorrelation matrices instead of the autocovariance matrices.
+    return_xr: bool
+        If True, return the covariance matrices as a DataArray with dimensions ["variable", "variable_aux", and "lag"].
+        Otherwise returns a 3d numpy array with shape (lag, variable, variable).
+    **solve_model_kwargs
+        Arguments forwarded to the ``solve_model`` method. Ignored if T and R are provided.
+
+    Returns
+    -------
+    acorr_mat: DataFrame
+    """
+    T, R = _maybe_solve_model(model, T, R, **solve_model_kwargs)
+
+    Sigma = stationary_covariance_matrix(
+        model,
+        T=T,
+        R=R,
+        shock_dict=shock_std_dict,
+        shock_cov_matrix=shock_cov_matrix,
+        shock_std=shock_std,
+        return_df=False,
+    )
+    result = _compute_autocovariance_matrix(
+        T, Sigma, n_lags=n_lags, correlation=correlation
+    )
+
+    if return_xr:
+        variables = [x.base_name for x in model.variables]
+        result = xr.DataArray(
+            result,
+            dims=["lag", "variable", "variable_aux"],
+            coords={
+                "lag": range(n_lags),
+                "variable": variables,
+                "variable_aux": variables,
+            },
+        )
+
+    return result
+
+
+def summarize_perturbation_solution(
+    linear_system: Sequence[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
+    perturbation_solution: Sequence[np.ndarray | None, np.ndarray | None],
+    model: Model,
+):
+    A, B, C, D = linear_system
+    T, R = perturbation_solution
+    if T is None or R is None:
+        raise PerturbationSolutionNotFoundException()
+
+    coords = {
+        "equation": np.arange(A.shape[0]).astype(int),
+        "variable": [x.base_name for x in model.variables],
+        "shock": [x.base_name for x in model.shocks],
+    }
+
+    return xr.Dataset(
+        data_vars={
+            "A": (("equation", "variable"), A),
+            "B": (("equation", "variable"), B),
+            "C": (("equation", "variable"), C),
+            "D": (("equation", "shock"), D),
+            "T": (("equation", "variable"), T),
+            "R": (("equation", "shock"), R),
+        },
+        coords=coords,
+    )
+
+
+autocorrelation_matrix = ft.partial(autocovariance_matrix, correlation=True)
+autocorrelation_matrix.__doc__ = autocovariance_matrix.__doc__
+
+
+def impulse_response_function(
+    model: Model,
+    T: np.ndarray | None = None,
+    R: np.ndarray | None = None,
+    simulation_length: int = 40,
+    shock_size: float | np.ndarray | dict[str, float] | None = None,
+    shock_cov: np.ndarray | None = None,
+    shock_trajectory: np.ndarray | None = None,
+    return_individual_shocks: bool | None = None,
+    orthogonalize_shocks: bool = False,
+    random_seed: int | np.random.RandomState | None = None,
+    **solve_model_kwargs,
+) -> xr.DataArray:
+    """
+    Generate impulse response functions (IRF) from state space model dynamics.
+
+    An impulse response function represents the dynamic response of the state space model
+    to an instantaneous shock applied to the system. This function calculates the IRF
+    based on either provided shock specifications or the posterior state covariance matrix.
+
+    Parameters
+    ----------
+    model: Model
+        DSGE Model object
+    T: np.ndarray, optional
+        Transition matrix of the solved system. If None, this will be computed using the model's ``solve_model``
+        method.
+    R: np.ndarray, optional
+        Selection matrix of the solved system. If None, this will be computed using the model's ``solve_model`` method.
+    simulation_length : int, optional
+        The number of periods to compute the IRFs over. The default is 40.
+    shock_size : float, array, or dict; default=None
+        The size of the shock applied to the system. If specified, it will create a covariance
+        matrix for the shock with diagonal elements equal to `shock_size`:
+            -  If float, the covariance matrix will be the identity matrix, scaled by `shock_size`.
+            -  If array, the covariance matrix will be ``diag(shock_size)``. In this case, the length of the provided array
+            must match the number of shocks in the state space model.
+            -  If dictionary, a diagonal matrix will be created with entries corresponding to the keys in the dictionary.
+               Shocks which are not specified will be set to zero.
+
+        Only one of `use_stationary_cov`, `shock_cov`, `shock_size`, or `shock_trajectory` can be specified.
+    shock_cov : Optional[np.ndarray], default=None
+        A user-specified covariance matrix for the shocks. It should be a 2D numpy array with
+        dimensions (n_shocks, n_shocks), where n_shocks is the number of shocks in the state space model.
+
+        Only one of `use_stationary_cov`, `shock_cov`, `shock_size`, or `shock_trajectory` can be specified.
+    shock_trajectory : Optional[np.ndarray], default=None
+        A pre-defined trajectory of shocks applied to the system. It should be a 2D numpy array
+        with dimensions (n, n_shocks), where n is the number of time steps and k_posdef is the
+        number of shocks in the state space model.
+
+        Only one of `use_stationary_cov`, `shock_cov`, `shock_size`, or `shock_trajectory` can be specified.
+    return_individual_shocks: bool, optional
+        If True, an IRF will be computed separately for each shock in the model. An additional dimension will be added
+        to the output DataArray to show each shock. This is only valid if `shock_size` is a scalar, dictionary, or if
+        the covariance matrix is diagonal.
+
+        If not specified, this will be set to True if ``shock_size`` if the above conditions are met.
+    orthogonalize_shocks : bool, default=False
+        If True, orthogonalize the shocks using Cholesky decomposition when generating the impulse
+        response. This option is ignored if `shock_trajectory` or `shock_size` are used, or if the covariance matrix is
+        diagonal.
+    random_seed : int, RandomState or Generator, optional
+        Seed for the random number generator.
+    **solve_model_kwargs
+        Arguments forwarded to the ``solve_model`` method. Ignored if T and R are provided.
+
+    Returns
+    -------
+    xr.DataArray
+        The IRFs for each variable in the model.
+    """
+    variable_names = [x.base_name for x in model.variables]
+    model_shock_names = [x.base_name for x in model.shocks]
+
+    n_variables = len(model.variables)
+    n_model_shocks = len(model.shocks)
+
+    rng = np.random.default_rng(random_seed)
+    Q = None  # No covariance matrix needed if a trajectory is provided. Will be overwritten later if needed.
+
+    _validate_simulation_options(shock_size, shock_cov, shock_trajectory)
+
+    return_individual_shocks = (
+        True if return_individual_shocks is None else return_individual_shocks
+    )
+
+    if shock_trajectory is not None:
+        n, k = shock_trajectory.shape
+
+        # Validate the shock trajectory
+        if k != n_model_shocks:
+            raise ValueError(
+                "If shock_trajectory is provided, there must be a trajectory provided for each shock. "
+                f"Model has {n_model_shocks} shocks, but shock_trajectory has only {k} columns"
+            )
+        if simulation_length is not None and simulation_length != n:
+            _log.warning(
+                "Both steps and shock_trajectory were provided but do not agree. Length of "
+                "shock_trajectory will take priority, and steps will be ignored."
+            )
+        simulation_length = n  # Overwrite steps with the length of the shock trajectory
+        shock_trajectory = np.array(shock_trajectory)
+
+    if shock_cov is not None:
+        Q = np.array(shock_cov)
+        is_diag = np.all(Q == np.diag(np.diagonal(Q)))
+        return_individual_shocks = is_diag
+
+        if orthogonalize_shocks:
+            Q = linalg.cholesky(Q) / np.diag(Q)[:, None]
+
+    T, R = _maybe_solve_model(model, T, R, **solve_model_kwargs)
+
+    def _simulate(shock_trajectory):
+        data = np.zeros((simulation_length, n_variables))
+
+        for t in range(1, simulation_length):
+            stochastic = R @ shock_trajectory[t - 1]
+            deterministic = T @ data[t - 1]
+            data[t] = deterministic + stochastic
+
+        return data
+
+    def _create_shock_trajectory(
+        n_shocks, shock_names, Q=None, shock_size=None, shock_trajectory=None
+    ):
+        if shock_trajectory is not None:
+            return np.array(shock_trajectory)
+
+        shock_trajectory = np.zeros((simulation_length, n_shocks))
+
+        if Q is not None:
+            shock_size = rng.multivariate_normal(
+                mean=np.zeros(n_shocks), cov=Q, size=simulation_length
+            )
+
+        else:
+            if isinstance(shock_size, int | float):
+                shock_size = np.ones(n_shocks) * shock_size
+            if isinstance(shock_size, dict):
+                shock_dict = shock_size.copy()
+                shock_size = np.zeros(n_shocks)
+                for i, name in enumerate(shock_names):
+                    if name in shock_dict:
+                        shock_size[i] = shock_dict[name]
+
+        shock_trajectory[0] = shock_size
+
+        return shock_trajectory
+
+    def _make_shock_dict(shocks, shock_size=None, Q=None):
+        if Q is not None:
+            return {x.base_name: np.sqrt(Q[i, i]) for i, x in enumerate(shocks)}
+        if isinstance(shock_size, dict):
+            return shock_size
+        if isinstance(shock_size, int | float):
+            return {x.base_name: shock_size for x in shocks}
+        if isinstance(shock_size, np.ndarray | list):
+            return {x.base_name: shock_size[i] for i, x in enumerate(shocks)}
+
+    shock_dict = _make_shock_dict(model.shocks, shock_size, Q)
+    shock_names = (
+        list(shock_dict.keys()) if shock_dict is not None else model_shock_names
+    )
+
+    # Sort the shock names to match the order of the model shocks
+    shock_names = [x for x in model_shock_names if x in shock_names]
+    n_shocks = len(shock_names)
+
+    data_shape = (simulation_length, n_variables)
+
+    coords = {"time": np.arange(simulation_length), "variable": variable_names}
+    dims = ["time", "variable"]
+
+    if return_individual_shocks:
+        data_shape = (n_shocks, *data_shape)
+        coords.update({"shock": shock_names})
+        dims = ["shock", "time", "variable"]
+
+    data = np.zeros(data_shape)
+
+    if return_individual_shocks and shock_dict is not None:
+        for i, (shock_name, init_shock) in enumerate(shock_dict.items()):
+            step_dict = {
+                k: shock_dict[k] if k == shock_name else 0.0 for k in shock_dict
+            }
+            traj = _create_shock_trajectory(
+                shock_names=model_shock_names,
+                n_shocks=n_model_shocks,
+                shock_size=step_dict,
+            )
+
+            data[i] = _simulate(traj)
+
+    elif return_individual_shocks and shock_trajectory is not None:
+        for i, shock_name in enumerate(shock_names):
+            traj = np.zeros_like(shock_trajectory)
+            traj[i] = shock_trajectory[i]
+            data[i] = _simulate(traj)
+
+    else:
+        traj = _create_shock_trajectory(
+            shock_names=model_shock_names,
+            n_shocks=n_model_shocks,
+            Q=Q,
+            shock_trajectory=shock_trajectory,
+            shock_size=shock_size,
+        )
+
+        data = _simulate(traj)
+
+    irf = xr.DataArray(
+        data,
+        dims=dims,
+        coords=coords,
+    )
+
+    return irf
+
+
+def simulate(
+    model: Model,
+    T: np.ndarray | None = None,
+    R: np.ndarray | None = None,
+    n_simulations: int = 1,
+    simulation_length: int = 40,
+    shock_std_dict: dict[str, float] | None = None,
+    shock_cov_matrix: np.ndarray | None = None,
+    shock_std: np.ndarray | list | float | np.ndarray = None,
+    random_seed: int | np.random.RandomState | None = None,
+    **solve_model_kwargs,
+) -> xr.DataArray:
+    """
+    Simulate the model over a certain number of time periods.
+
+    Parameters
+    ----------
+    model: Model
+        DSGE Model object
+    T: np.ndarray, optional
+        Transition matrix of the solved system. If None, this will be computed using the model's ``solve_model``
+        method.
+    R: np.ndarray, optional
+        Selection matrix of the solved system. If None, this will be computed using the model's ``solve_model`` method.
+    n_simulations : int, optional
+        Number of trajectories to simulate. Default is 1.
+    simulation_length : int, optional
+        Length of each simulated trajectory. Default is 40.
+    shock_std_dict: dict, optional
+        Dictionary of shock names and standard deviations to be used to build Q
+    shock_cov_matrix: array, optional
+        An (n_shocks, n_shocks) covariance matrix describing the exogenous shocks
+    shock_std: float or sequence, optional
+        Standard deviation of all model shocks.
+    random_seed : int, RandomState or Generator, optional
+        Seed for the random number generator.
+    **solve_model_kwargs
+        Arguments forwarded to the ``solve_model`` method. Ignored if T and R are provided.
+
+    Returns
+    -------
+    xr.DataArray
+        Simulated trajectories.
+    """
+    rng = np.random.default_rng(random_seed)
+    shocks = model.shocks
+    T, R = _maybe_solve_model(model, T, R, **solve_model_kwargs)
+
+    n_variables, n_shocks = R.shape
+
+    _validate_shock_options(
+        shock_std_dict=shock_std_dict,
+        shock_cov_matrix=shock_cov_matrix,
+        shock_std=shock_std,
+        shocks=shocks,
+    )
+
+    Q = build_Q_matrix(
+        model_shocks=shocks,
+        shock_std_dict=shock_std_dict,
+        shock_cov_matrix=shock_cov_matrix,
+        shock_std=shock_std,
+    )
+
+    epsilons = rng.multivariate_normal(
+        mean=np.zeros(n_shocks),
+        cov=Q,
+        size=(n_simulations, simulation_length),
+        method="svd",
+    )
+
+    data = np.zeros((n_simulations, simulation_length, n_variables))
+
+    for t in range(1, simulation_length):
+        stochastic = np.einsum("nk,sk->sn", R, epsilons[:, t - 1, :])
+        deterministic = np.einsum("nm,sm->sn", T, data[:, t - 1, :])
+        data[:, t, :] = deterministic + stochastic
+
+    data = xr.DataArray(
+        data,
+        dims=["simulation", "time", "variable"],
+        coords={
+            "variable": [x.base_name for x in model.variables],
+            "simulation": np.arange(n_simulations),
+            "time": np.arange(simulation_length),
+        },
+    )
+
+    return data
+
+
+def matrix_to_dataframe(
+    matrix,
+    model,
+    dim1: str | None = None,
+    dim2: str | None = None,
+    round: None | int = None,
+) -> pd.DataFrame:
+    """
+    Convert a matrix to a DataFrame with variable names as columns and rows.
+
+
+    Parameters
+    ----------
+    matrix: np.ndarray
+        DSGE matrix to convert to a DataFrame. Each dimension should have shape n_variables or n_shocks.
+    model: Model
+        DSGE model object
+    dim1: str, Optional
+        Name of the first dimension of the matrix. Must be one of "variable", "equation",  or "shock". If None, the
+        function will guess based on the shape of the matrix. In the event that the model has exactly as many
+        variables as shocks, it will guess "variable", so be careful!
+    dim2: str, Optional
+        Name of the second dimension of the matrix. Must be one of "variable", "equation", or "shock". If None, the
+        function will guess based on the shape of the matrix.
+    round: int, Optional
+        Number of decimal places to round the values in the DataFrame. If None, values will not be rounded.
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with variable names as columns and rows.
+    """
+    var_names = [x.base_name for x in model.variables]
+    shock_names = [x.base_name for x in model.shocks]
+    equation_names = [f"Equation {i}" for i in range(len(model.equations))]
+
+    coords = {"variable": var_names, "shock": shock_names, "equation": equation_names}
+
+    n_variables = len(var_names)
+    n_shocks = len(shock_names)
+
+    if matrix.ndim != 2:
+        raise ValueError("Matrix must be 2-dimensional")
+
+    for i, ordinal in enumerate(["First", "Secoond"]):
+        if matrix.shape[i] not in [n_variables, n_shocks]:
+            raise ValueError(
+                f"{ordinal} dimension of the matrix must match the number of variables or shocks "
+                f"in the model"
+            )
+
+    if dim1 is None:
+        dim1 = "variable" if matrix.shape[0] == n_variables else "shock"
+    if dim2 is None:
+        dim2 = "variable" if matrix.shape[1] == n_variables else "shock"
+
+    df = pd.DataFrame(
+        matrix,
+        index=coords[dim1],
+        columns=coords[dim2],
+    )
+
+    if round is not None:
+        return df.round(round)
+
+    return df
diff --git a/gEconpy/model/parameters.py b/gEconpy/model/parameters.py
new file mode 100644
index 0000000..be15efc
--- /dev/null
+++ b/gEconpy/model/parameters.py
@@ -0,0 +1,69 @@
+from collections.abc import Callable
+
+from gEconpy.classes.containers import SymbolDictionary
+from gEconpy.model.compile import (
+    BACKENDS,
+    compile_function,
+    dictionary_return_wrapper,
+    make_return_dict_and_update_cache,
+)
+
+
+def compile_param_dict_func(
+    param_dict: SymbolDictionary,
+    deterministic_dict: SymbolDictionary,
+    backend: BACKENDS = "numpy",
+    cache: dict | None = None,
+    return_symbolic: bool = False,
+) -> tuple[Callable, dict]:
+    """
+    Compile a function to compute model parameters from given "free" parameters.
+
+    Most model parameters are provided by the user as fixed values. We denote these are "free" parameters. Others are
+    functions of the free parameters, and need to be dynamically recomputed each time the free parameters change.
+
+    Parameters
+    ----------
+    param_dict: SymbolDictionary
+        A dictionary of free parameters.
+    deterministic_dict: SymbolDictionary
+        A dictionary of deterministic parameters, with the keys being the parameters and the values being the
+        expressions to compute them.
+    backend: str, one of "numpy", "numba", "pytensor"
+        The backend to use for the compiled function.
+    cache: dict, optional
+        A dictionary mapping from pytensor symbols to sympy expressions. Used to prevent duplicate mappings from
+        sympy symbol to pytensor symbol from being created. Default is a empty dictionary, implying no other functions
+        have been compiled yet.
+    return_symbolic: bool, default False
+        When true, if backend is "pytensor", return a symbolic graph representing the computation of parameter values
+        rather than a compiled pytensor function. Ignored if backend is not "pytensor"
+
+    Returns
+    -------
+    f: Callable
+        A function that takes the free parameters as keyword arguments and returns a dictionary of the computed
+        parameters.
+    cache: dict
+        A dictionary mapping from sympy symbols to pytensor symbols.
+    """
+    cache = {} if cache is None else cache
+
+    inputs = list(param_dict.to_sympy().keys())
+    output_params = inputs + list(deterministic_dict.to_sympy().keys())
+    output_exprs = inputs + list(deterministic_dict.values_to_float().values())
+
+    f, cache = compile_function(
+        inputs,
+        output_exprs,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        pop_return=False,
+        stack_return=not return_symbolic,
+    )
+
+    if return_symbolic and backend == "pytensor":
+        return make_return_dict_and_update_cache(output_params, f, cache)
+
+    return dictionary_return_wrapper(f, output_params), cache
diff --git a/gEconpy/model/perturbation.py b/gEconpy/model/perturbation.py
new file mode 100644
index 0000000..cab288b
--- /dev/null
+++ b/gEconpy/model/perturbation.py
@@ -0,0 +1,515 @@
+import logging
+
+from functools import wraps
+from inspect import signature
+from typing import Literal
+
+import numba as nb
+import numpy as np
+import pandas as pd
+import pytensor.tensor as pt
+import sympy as sp
+
+from pytensor.graph.basic import Apply
+from pytensor.graph.op import Op
+from scipy import linalg
+
+from gEconpy.classes.containers import SymbolDictionary
+from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
+from gEconpy.model.compile import BACKENDS, compile_function
+from gEconpy.numbaf.overloads import nb_ordqz
+from gEconpy.solvers.gensys import _gensys_setup
+from gEconpy.utilities import eq_to_ss, get_name, simplify_matrix
+
+_log = logging.getLogger(__name__)
+
+
+def override_dummy_wrapper(f, param_name="not_loglin_variable"):
+    """
+    Wrap a function to map a parameter name to a _Dummy argument in a sympy lambdify generated function
+
+    To have a 1d array input to a sympy lambdify function, it is necessary to use an IndexBase. IndexBase,
+    unfortunately, always ends up as a Dummy value when lambdified. This wrapper finds a single dummy value
+    in a function signature, and automatically maps the parameter name to it.
+
+    Parameters
+    ----------
+    f: Callable
+        Function generated by sympy.lambidfy, with exactly one dummy variable
+    param_name: str
+        Named arugment that will be mapped to the dummy in the wrapped function
+
+    Returns
+    -------
+    inner: Callable
+        Same as f, with a keyword argument "param_name" that maps to the Dummy input
+
+    """
+    sig = signature(f)
+    f_inputs = list(sig.parameters.keys())
+
+    # If the parameter is already in the function signature, we're copacetic and don't need to wrap the function
+    if param_name in f_inputs:
+        return f
+
+    dummies = [x for x in f_inputs if x.startswith("_Dummy")]
+    assert len(dummies) == 1
+
+    @wraps(f)
+    def inner(*args, **kwargs):
+        loglin = kwargs.pop(param_name)
+        kwargs[dummies[0]] = loglin
+
+        return f(*args, **kwargs)
+
+    return inner
+
+
+def make_all_variable_time_combinations(
+    variables,
+) -> tuple[list[TimeAwareSymbol], list[TimeAwareSymbol], list[TimeAwareSymbol]]:
+    """
+    Given a list of TimeAwareSymbols, all at time t, shift them to create all possible lags, current, and lead variables.
+
+    Parameters
+    ----------
+    variables: List[TimeAwareSymbol]
+        List of variables to shift.
+
+    Returns
+    -------
+    lags: List[TimeAwareSymbol]
+        List of variables shifted to t-1.
+    now: List[TimeAwareSymbol]
+        List of variables at time t.
+    leads: List[TimeAwareSymbol]
+        List of variables shifted to t+1.
+    """
+    # Set all variables to time t, remove duplicates, and sort by base name.
+    now = list({x.set_t(0) for x in variables})
+    now = sorted(now, key=lambda x: x.base_name)
+
+    # Create lags and leads by shifting the time of the variables.
+    lags = [x.step_backward() for x in now]
+    leads = [x.step_forward() for x in now]
+
+    return lags, now, leads
+
+
+def linearize_model(
+    variables: list[TimeAwareSymbol],
+    equations: list[sp.Expr],
+    shocks: list[sp.Symbol],
+    order=1,
+) -> tuple[list[sp.Matrix, ...], sp.Symbol]:
+    r"""
+    Log-linearize a model around its steady state.
+
+    Parameters
+    ----------
+    variables: List[TimeAwareSymbol]
+        List of all variables in the model, expressed at time t
+
+    equations: List[sp.Expr]
+        List of equations that define the model.
+
+    shocks: List[sp.Symbol]
+        List of exogenous shocks in the model.
+
+    order: int, default 1
+        Order of the linear approximation of the model. Currently only order = 1 is supported.
+
+    Returns
+    -------
+    Fs: List[sp.Matrix]
+        List of matrices representing the log-linearized model.
+
+    not_loglin_variables: sp.Symbol
+        A special symbol created by the function that allows transformation between the log-linear and non-log-linear
+        representations of the model. See the Notes for details.
+
+    Notes
+    -----
+    Convert the non-linear model to its log-linear approximation using a first-order Taylor expansion around the
+    deterministic steady state. The specific method of log-linearization is taken from ..[1]
+
+    .. math::
+        F_1 T y_{t-1} + F_2 @ T @ y_t + F_3 @ T @ y_{t+1} + F4 \varepsilon_t = 0
+
+    Each of F1, F2, F3, and F4 are the Jacobian matrices of the model equations with respect to the variables at time
+    t-1, t, t+1, and the exogenous shocks, respectively.
+
+    The T matrix requires special note. It is a diagonal matrix with either the steady state value of the variable or
+    1, depending on whether the variable is log-linearized or not. Specifically:
+
+    .. math::
+        T = \text{Diagonal}(y_{ss}^{1 - \text{not_loglin_variable})
+
+    Where :math:`\text{not_loglin_variable}` is a vector whose :math:`i`-th value is zero if the :math:`i`-th variable
+    is log-linearized, and one otherwise. The :math:`T` matrix arises from application of the chain rule. When a
+    variable is assumed to be represented in logs, it is entered into all model equations as :math:`\exp(y)` (indeed,
+    Dynare requires the research to do exactly this). Having made this substitution, the partial derivative of a model
+    equation :math:`f(exp(x))` with respect to :math:`x` is:
+
+    .. math::
+       \frac{\partial f}{\partial x_ss} f(exp(x_ss)) = f'(exp(x_ss)) \cdot exp(x_ss)
+
+    Since we interpret the variable :math:`y_{ss}` as (implicitly) being in logs, this simplifies to
+    :math:`f'(y_ss) \cdot y_{ss}`. On the other hand, if we are not log-linearizing the variable, the partial derivative
+    is simply :math:`f'(y_ss)`. By setting the value of the exponent to 1 or 0, we can obtain the correct value of the
+    derivative for each equation, with respect to each variable.
+
+    Evaluating the matrix multiplications between each :math:`F` matrix and the :math:`T` matrix, we obtain the
+    following simplified expression:
+
+    .. math::
+        A y_{t-1} + B y_t + C y_{t+1} + D \varepsilon = 0
+
+    Matrices A, B, C, and D are returned by this function.
+
+    References
+    ----------
+    [1] gEcon User's Guide, page 54, equation 9.9.
+    """
+    if order != 1:
+        raise NotImplementedError(
+            "Only order = 1 linearization is currently implemented."
+        )
+
+    ss_variables = [x.to_ss() for x in variables]
+    lags, now, leads = make_all_variable_time_combinations(variables)
+
+    eq_vec = sp.Matrix(equations)
+    A, B, C = (eq_to_ss(eq_vec.jacobian(var_group)) for var_group in [lags, now, leads])
+    A, B, C = (simplify_matrix(x) for x in [A, B, C])
+    D = eq_to_ss(eq_vec.jacobian(shocks)) if shocks else sp.ZeroMatrix(*eq_vec.shape)
+
+    not_loglin_var = sp.IndexedBase("not_loglin_variable", shape=(len(variables),))
+    T = sp.diag(
+        *[ss_var ** (1 - not_loglin_var[i]) for i, ss_var in enumerate(ss_variables)]
+    )
+
+    Fs = [A @ T, B @ T, C @ T, D]
+
+    return Fs, not_loglin_var
+
+
+def make_not_loglin_flags(
+    variables: list[TimeAwareSymbol],
+    calibrated_params: list[sp.Symbol],
+    steady_state: SymbolDictionary[str, float],
+    log_linearize: bool = True,
+    not_loglin_variables: list[str] | None = None,
+    loglin_negative_ss: bool = False,
+    verbose: bool = True,
+):
+    if not_loglin_variables is None:
+        not_loglin_variables = []
+    if not log_linearize:
+        return np.ones(len(variables))
+
+    vars_and_calibrated = variables + calibrated_params
+    var_names = [get_name(x, base_name=True) for x in vars_and_calibrated]
+    unknown_not_login = set(not_loglin_variables) - set(var_names)
+
+    if len(unknown_not_login) > 0:
+        raise ValueError(
+            f"The following variables were requested not to be log-linearized, but are unknown to the model: "
+            f"{', '.join(unknown_not_login)}"
+        )
+
+    if verbose and len(not_loglin_variables) > 0:
+        _log.warning(
+            "The following variables will not be log-linearized at the user's request: "
+            f"{not_loglin_variables}"
+        )
+
+    n_variables = len(vars_and_calibrated)
+    not_loglin_flags = np.zeros(n_variables)
+
+    for i, name in enumerate(var_names):
+        not_loglin_flags[i] = name in not_loglin_variables
+
+    ss_values = np.array(list(steady_state.values()))
+    ss_zeros = np.abs(ss_values) < 1e-8
+    ss_negative = ss_values < 0.0
+
+    if np.any(ss_zeros):
+        zero_idxs = np.flatnonzero(ss_zeros)
+        zero_vars = [vars_and_calibrated[i] for i in zero_idxs]
+        if verbose:
+            _log.warning(
+                f"The following variables had steady-state values close to zero and will not be log-linearized:"
+                f"{[get_name(x) for x in zero_vars]}"
+            )
+
+        not_loglin_flags[ss_zeros] = 1
+
+    if np.any(ss_negative) and not loglin_negative_ss:
+        neg_idxs = np.flatnonzero(ss_negative)
+        neg_vars = [vars_and_calibrated[i] for i in neg_idxs]
+        if verbose:
+            _log.warning(
+                f"The following variables had negative steady-state values and will not be log-linearized:"
+                f"{[get_name(x) for x in neg_vars]}"
+            )
+
+        not_loglin_flags[neg_idxs] = 1
+
+    return not_loglin_flags
+
+
+def compile_linearized_system(
+    equations: list[sp.Expr],
+    variables: list[sp.Symbol | TimeAwareSymbol],
+    param_dict: SymbolDictionary[sp.Symbol, float | sp.Expr],
+    deterministic_dict: SymbolDictionary[sp.Symbol, sp.Expr],
+    calib_dict: SymbolDictionary[sp.Symbol, float | sp.Expr],
+    shocks: list[TimeAwareSymbol],
+    model_is_linear: bool = False,
+    backend: BACKENDS = "numpy",
+    return_symbolic: bool = False,
+    cache: dict | None = None,
+    **kwargs,
+):
+    """
+    Compile a function that evaluates the linearized system of equations.
+
+    Parameters
+    ----------
+    equations
+    variables
+    param_dict
+    deterministic_dict
+    calib_dict
+    shocks
+    backend
+    return_symbolic
+    cache
+    kwargs
+
+    Returns
+    -------
+    f_linearze: Callable
+        Function that evaluates the linearized system of equations.
+    cache: dict
+        Dictionary mapping sympy symbols to pytensor tensors. Empty if backend is not pytensor
+    """
+    cache = {} if cache is None else cache
+
+    ss_variables = [x.to_ss() for x in variables]
+
+    parameters = list((param_dict | deterministic_dict).to_sympy().keys())
+    parameters = [x for x in parameters if x not in calib_dict.to_sympy()]
+    calib_params = list(calib_dict.to_sympy().keys())
+
+    outputs, not_loglin_var = linearize_model(variables, equations, shocks)
+    inputs = ss_variables + calib_params + parameters + [not_loglin_var]
+
+    f_linearize, cache = compile_function(
+        inputs,
+        outputs,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        **kwargs,
+    )
+
+    return f_linearize, cache
+
+
+def residual_norms(B, C, D, Q, P, A_prime, R_prime, S_prime):
+    norm_deterministic = linalg.norm(A_prime + B @ R_prime + C @ R_prime @ P)
+
+    norm_stochastic = linalg.norm(B @ S_prime + C @ R_prime @ Q + D)
+
+    return norm_deterministic, norm_stochastic
+
+
+def statespace_to_gEcon_representation(A, T, R, tol):
+    n_vars = T.shape[1]
+    n_shocks = R.shape[1]
+
+    state_var_idx = np.where(
+        np.abs(T[np.argmax(np.abs(T), axis=0), np.arange(n_vars)]) >= tol
+    )[0]
+    state_var_mask = np.isin(np.arange(n_vars), state_var_idx)
+
+    shock_idx = np.arange(n_shocks)
+
+    PP = T.copy()
+    PP[np.where(np.abs(PP) < tol)] = 0
+    QQ = R.copy()
+    QQ = QQ[:n_vars, :]
+    QQ[np.where(np.abs(QQ) < tol)] = 0
+
+    P = PP[state_var_mask, :][:, state_var_mask]
+    Q = QQ[state_var_mask, :][:, shock_idx]
+    R = PP[~state_var_mask, :][:, state_var_idx]
+    S = QQ[~state_var_mask, :][:, shock_idx]
+
+    A_prime = A[:, state_var_mask]
+    R_prime = PP[:, state_var_mask]
+    S_prime = QQ[:, shock_idx]
+
+    return P, Q, R, S, A_prime, R_prime, S_prime
+
+
+def check_perturbation_solution(A, B, C, D, T, R, tol=1e-8):
+    P, Q, R, S, A_prime, R_prime, S_prime = statespace_to_gEcon_representation(
+        A, T, R, tol
+    )
+    norm_deterministic, norm_stochastic = residual_norms(
+        B, C, D, Q, P, A_prime, R_prime, S_prime
+    )
+
+    _log.info(f"Norm of deterministic part: {norm_deterministic:0.9f}")
+    _log.info(f"Norm of stochastic part:    {norm_stochastic:0.9f}")
+
+
+# TODO: Cannot cache this I think because of the call to ordqz -- test if this is true
+@nb.njit(cache=False)
+def _compute_solution_eigenvalues(A, B, C, D, tol=1e-8) -> np.array:
+    Gamma_0, Gamma_1, _, _, _ = _gensys_setup(A, B, C, D, tol)
+
+    # Using scipy instead of qzdiv appears to offer a huge speedup for nearly the same answer; some eigenvalues
+    # have sign flip relative to qzdiv -- does it matter?
+    A, B, alpha, beta, Q, Z = nb_ordqz(-Gamma_0, Gamma_1, sort="ouc", output="complex")
+
+    eigenval = np.diag(B) / (np.diag(A) + tol)
+    n_eigs = len(eigenval)
+
+    eig = np.empty((n_eigs, 3))
+    eig[:, 0] = np.abs(eigenval)
+    eig[:, 1] = np.real(eigenval)
+    eig[:, 2] = np.imag(eigenval)
+
+    sorted_idx = np.argsort(eig[:, 0])
+    eig = eig[sorted_idx, :]
+
+    return eig
+
+
+def check_bk_condition(
+    A,
+    B,
+    C,
+    D,
+    tol=1e-8,
+    verbose=True,
+    on_failure: Literal["raise", "ignore"] = "ignore",
+    return_value: Literal["dataframe", "bool", None] = "dataframe",
+) -> bool | pd.DataFrame | None:
+    """
+    Compute the generalized eigenvalues of system in the form presented in [1]. Per [2], the number of
+    unstable eigenvalues (|v| > 1) should not be greater than the number of forward-looking variables. Failing
+    this test suggests timing problems in the definition of the model.
+
+    Parameters
+    ----------
+    A: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to past variables
+        values that are known when decision-making: those with t-1 subscripts.
+    B: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to variables that
+        are observed when decision-making: those with t subscripts.
+    C: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to variables that
+        enter in expectation when decision-making: those with t+1 subscripts.
+    D: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to exogenous shocks.
+    verbose: bool, default: True
+        Flag to print the results of the test, otherwise the eigenvalues are returned without comment.
+    on_failure: str, default: 'ignore'
+        Action to take if the Blanchard-Kahn condition is not satisfied. Valid values are 'ignore' and 'raise'.
+    return_value: string, default: 'dataframe'
+        Controls what is returned by the function. Valid values are 'dataframe', 'bool', and 'none'.
+        If df, a dataframe containing eigenvalues is returned. If 'bool', a boolean indicating whether the BK
+        condition is satisfied. If None, nothing is returned.
+    tol: float, 1e-8
+        Tolerance below which numerical values are considered zero
+
+    Returns
+    -------
+    bk_result, bool or pd.DataFrame, optional.
+        Return value requested. Datatype corresponds to what was requested in the ``return_value`` argument:
+        - None, If return_value is 'none'
+        - condition_satisfied, bool, if return_value is 'bool', returns True if the Blanchard-Kahn condition is
+          satisfied, False otherwise.
+        - Eigenvalues, pd.DataFrame, if return_value is 'df', returns a dataframe containing the real and imaginary
+          components of the system's, eigenvalues, along with their modulus.
+    """
+    if return_value not in ["dataframe", "bool", None]:
+        raise ValueError(f'Unknown return type "{return_value}"')
+
+    n_forward = (np.abs(C).sum(axis=0) > tol).sum().astype(int)
+    eig = pd.DataFrame(
+        _compute_solution_eigenvalues(A, B, C, D, tol),
+        columns=["Modulus", "Real", "Imaginary"],
+    )
+    n_greater_than_one = (eig["Modulus"] > 1).sum()
+    condition_not_satisfied = n_forward != n_greater_than_one
+
+    message = (
+        f'Model solution has {n_greater_than_one} eigenvalues greater than one in modulus and {n_forward} '
+        f'forward-looking variables. '
+        f'\nBlanchard-Kahn condition is{" NOT" if condition_not_satisfied else ""} satisfied.'
+    )
+
+    if condition_not_satisfied:
+        if n_greater_than_one > n_forward:
+            reason = "No stable solution (More unstable eigenvalues than forward-looking variables)"
+        else:
+            reason = "No unique solution (More forward-looking variables than unstable eigenvalues)"
+
+        message += " " + reason
+
+    if condition_not_satisfied and on_failure == "raise":
+        raise ValueError(message)
+
+    if verbose:
+        _log.info(message)
+
+    if return_value is None:
+        return
+    if return_value == "dataframe":
+        return eig
+    else:
+        return ~condition_not_satisfied
+
+
+class BlanchardKahnCondition(Op):
+    def __init__(self, tol=1e-8):
+        self.tol = tol
+        super().__init__()
+
+    def make_node(self, A, B, C, D) -> Apply:
+        inputs = list(map(pt.as_tensor, [A, B, C, D]))
+        outputs = [
+            pt.scalar("bk_flag", dtype=bool),
+            pt.scalar("n_forward", dtype=int),
+            pt.scalar("n_greater_than_one", dtype=int),
+        ]
+
+        return Apply(self, inputs, outputs)
+
+    def perform(
+        self, node: Apply, inputs: list[np.ndarray], outputs: list[list[None]]
+    ) -> None:
+        A, B, C, D = inputs
+        eig = check_bk_condition(
+            A, B, C, D, tol=self.tol, verbose=False, return_value="dataframe"
+        )
+
+        n_forward = (np.abs(C).sum(axis=0) > 1e-8).sum().astype(int)
+        n_greater_than_one = (eig["Modulus"] > 1).sum()
+
+        condition_not_satisfied = n_forward != n_greater_than_one
+
+        outputs[0][0] = np.array(condition_not_satisfied)
+        outputs[1][0] = np.array(n_forward)
+        outputs[2][0] = np.array(n_greater_than_one)
+
+
+def check_bk_condition_pt(A, B, C, D, tol=1e-8):
+    return BlanchardKahnCondition(tol=tol)(A, B, C, D)
diff --git a/gEconpy/model/simplification.py b/gEconpy/model/simplification.py
new file mode 100644
index 0000000..edd73ba
--- /dev/null
+++ b/gEconpy/model/simplification.py
@@ -0,0 +1,152 @@
+from warnings import warn
+
+import numpy as np
+import sympy as sp
+
+from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
+from gEconpy.utilities import (
+    expand_subs_for_all_times,
+    is_variable,
+    make_all_var_time_combos,
+    substitute_all_equations,
+)
+
+
+def _check_system_is_square(msg: str, n_equations: int, n_variables: int) -> bool:
+    if n_equations != n_variables:
+        warn(
+            f'{msg} was requested but not possible because the system is not well defined. '
+            f'Found {n_equations} equation{"s" if n_equations > 1 else ""} but {n_variables} variable'
+            f'{"s" if n_variables > 1 else ""}'
+        )
+        return False
+    return True
+
+
+def reduce_variable_list(equations, variables):
+    reduced_variables = {
+        atom.set_t(0)
+        for eq in equations
+        for atom in eq.atoms()
+        if is_variable(atom) and atom.set_t(0) in variables
+    }
+
+    reduced_variables = sorted(list(reduced_variables), key=lambda x: x.name)
+    eliminated_vars = sorted(
+        list(set(variables) - set(reduced_variables)), key=lambda x: x.name
+    )
+
+    return reduced_variables, eliminated_vars
+
+
+def simplify_tryreduce(
+    try_reduce_vars: list[TimeAwareSymbol],
+    equations: list[sp.Expr],
+    variables: list[TimeAwareSymbol],
+    tryreduce_sub_dict: dict[TimeAwareSymbol, sp.Expr] | None = None,
+) -> tuple[list[sp.Expr], list[TimeAwareSymbol], list[TimeAwareSymbol]]:
+    """
+    Attempt to reduce the number of equations in the system by removing equations requested in the `tryreduce`
+    block of the GCN file. Equations are considered safe to remove if they are "self-contained" that is, if
+    no other variables depend on their values.
+
+    Returns
+    -------
+    list
+        The names of the variables that were removed. If reduction was not possible, None is returned.
+    """
+    n_equations = len(equations)
+    n_variables = len(variables)
+    if not _check_system_is_square(
+        "Simplification via a tryreduce block", n_equations, n_variables
+    ):
+        return equations, variables, []
+    if tryreduce_sub_dict is None:
+        tryreduce_sub_dict = {}
+
+    occurrence_matrix = np.zeros((n_variables, n_variables))
+    reduced_equations = []
+
+    for i, eq in enumerate(equations):
+        for j, var in enumerate(variables):
+            if any([x in eq.atoms() for x in make_all_var_time_combos([var])]):
+                occurrence_matrix[i, j] += 1
+
+    # Columns with a sum of 1 are variables that appear only in a single equations; these equations can be deleted
+    # without consequence w.r.t solving the system, with no further checking required.
+    isolated_variables = np.array(variables)[occurrence_matrix.sum(axis=0) == 1]
+    to_remove = set(isolated_variables).intersection(set(try_reduce_vars))
+
+    for eq in equations:
+        if not any([var in eq.atoms() for var in to_remove]):
+            reduced_equations.append(eq)
+
+    # Next use the user-supplied equations to reduce the system further, seeking to eliminate variables via direct
+    # substitution.
+    for reduction_variable in try_reduce_vars:
+        if reduction_variable not in tryreduce_sub_dict:
+            continue
+        sub_dict = {reduction_variable: tryreduce_sub_dict[reduction_variable]}
+        reduction_candidate = substitute_all_equations(reduced_equations, sub_dict)
+        reduction_candidate = [eq.simplify() for eq in reduction_candidate]
+
+        # To be a valid reduction, there should be exactly one zero in reduction_candidates, and the reduced variable
+        # should no longer appear in the system.
+        if reduction_candidate.count(0) == 1 and not any(
+            [
+                x in eq.atoms()
+                for eq in reduction_candidate
+                for x in make_all_var_time_combos([reduction_variable])
+            ]
+        ):
+            reduced_equations = [eq for eq in reduction_candidate if eq != 0]
+
+    reduced_variables, eliminated_vars = reduce_variable_list(
+        reduced_equations, variables
+    )
+    return reduced_equations, reduced_variables, eliminated_vars
+
+
+def simplify_constants(
+    equations: list[sp.Expr], variables: list[TimeAwareSymbol]
+) -> tuple[list[sp.Expr], list[TimeAwareSymbol], list[TimeAwareSymbol]]:
+    """
+    Simplify the system by removing variables that are deterministically defined as a known value. Common examples
+    include P[] = 1, setting the price level of the economy as the numeraire, or B[] = 0, putting the bond market
+    in net-zero supply.
+
+    In these cases, the variable can be replaced by the deterministic value after all FoC
+    have been computed.
+
+    Returns
+    -------
+    eliminated_vars : List[str]
+        The names of the variables that were removed.
+    """
+    n_equations = len(equations)
+    n_variables = len(variables)
+
+    if not _check_system_is_square(
+        "Removal of constant variables", n_equations, n_variables
+    ):
+        return equations, variables, []
+
+    reduce_dict = {}
+
+    for eq in equations:
+        if len(eq.atoms()) < 4:
+            var = [x for x in eq.atoms() if is_variable(x)]
+            if len(var) != 1:
+                continue
+            var = var[0]
+            sub_dict = expand_subs_for_all_times(sp.solve(eq, var, dict=True)[0])
+            reduce_dict.update(sub_dict)
+
+    reduced_equations = substitute_all_equations(equations, reduce_dict)
+    reduced_equations = [eq for eq in reduced_equations if eq != 0]
+
+    reduced_variables, eliminated_vars = reduce_variable_list(
+        reduced_equations, variables
+    )
+
+    return reduced_equations, reduced_variables, eliminated_vars
diff --git a/gEconpy/model/statespace.py b/gEconpy/model/statespace.py
new file mode 100644
index 0000000..ad800fa
--- /dev/null
+++ b/gEconpy/model/statespace.py
@@ -0,0 +1,492 @@
+import logging
+
+import numpy as np
+import pandas as pd
+import preliz as pz
+import pymc as pm
+import pytensor
+import pytensor.tensor as pt
+import sympy as sp
+
+from pymc.pytensorf import rewrite_pregrad
+from pymc_experimental.statespace.core.statespace import PyMCStateSpace
+from pymc_experimental.statespace.models.utilities import make_default_coords
+from pymc_experimental.statespace.utils.constants import (
+    JITTER_DEFAULT,
+    SHOCK_AUX_DIM,
+    SHOCK_DIM,
+)
+from pytensor import graph_replace
+from scipy.stats._continuous_distns import (
+    beta_gen,
+    expon_gen,
+    gamma_gen,
+    halfnorm_gen,
+    invgamma_gen,
+    norm_gen,
+    truncnorm_gen,
+    uniform_gen,
+)
+from scipy.stats.distributions import rv_frozen
+
+from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
+from gEconpy.model.perturbation import check_bk_condition_pt
+from gEconpy.solvers.cycle_reduction import cycle_reduction_pt, scan_cycle_reduction
+from gEconpy.solvers.gensys import gensys_pt
+
+_log = logging.getLogger(__name__)
+floatX = pytensor.config.floatX
+
+
+SCIPY_TO_PRELIZ = {
+    norm_gen: pz.Normal,
+    halfnorm_gen: pz.HalfNormal,
+    truncnorm_gen: pz.TruncatedNormal,
+    uniform_gen: pz.Uniform,
+    beta_gen: pz.Beta,
+    gamma_gen: pz.Gamma,
+    invgamma_gen: pz.InverseGamma,
+    expon_gen: pz.Exponential,
+}
+
+
+class DSGEStateSpace(PyMCStateSpace):
+    def __init__(
+        self,
+        variables: list[TimeAwareSymbol],
+        shocks: list[TimeAwareSymbol],
+        equations: list[sp.Expr],
+        param_dict: dict[str, float],
+        priors: list[dict[str, rv_frozen]],
+        parameter_mapping: dict[pt.TensorVariable, pt.TensorVariable],
+        steady_state_mapping: dict[pt.TensorVariable, pt.TensorVariable],
+        ss_jac: pt.TensorVariable,
+        ss_resid: pt.TensorVariable,
+        ss_error: pt.TensorVariable,
+        ss_error_grad: pt.TensorVariable,
+        ss_error_hess: pt.TensorVariable,
+        linearized_system: list[pt.TensorVariable],
+    ):
+        self.variables = variables
+        self.equations = equations
+        self.shocks = shocks
+        self.priors = priors
+        self.param_dict = param_dict
+
+        self.parameter_mapping = parameter_mapping
+        self.steady_state_mapping = steady_state_mapping
+        self.input_parameters = [
+            x for x in parameter_mapping.keys() if x.name in param_dict
+        ]
+
+        self.ss_jac = ss_jac
+        self.ss_resid = ss_resid
+        self.ss_error = ss_error
+        self.ss_error_grad = ss_error_grad
+        self.ss_error_hess = ss_error_hess
+
+        self.linearized_system = linearized_system
+
+        self.full_covariance = False
+        self.constant_parameters = []
+        self._configured = False
+        self._obs_state_names = None
+        self.error_states = []
+        self._solver = "gensys"
+        self._solver_kwargs: dict | None = None
+        self._mode = None
+        self._linearized_system_subbed: list | None = None
+        self._policy_graph: list | None = None
+        self._ss_resid: pt.TensorVariable | None = None
+
+        self._bk_output = None
+        self._policy_resid = None
+
+        k_endog = 1  # to be updated later
+        k_states = len(variables)
+        k_posdef = len(shocks)
+
+        super().__init__(
+            k_endog,
+            k_states,
+            k_posdef,
+            filter_type="standard",
+            verbose=False,
+            measurement_error=False,
+        )
+
+    def make_symbolic_graph(self):
+        if not self._configured:
+            _log.info(
+                "Statespace model construction complete, but call the .configure method to finalize."
+            )
+            return
+
+        # Register the existing placeholders with the statespace model
+        constant_replacements = {}
+        for parameter in self.input_parameters:
+            if parameter.name in self.constant_parameters:
+                constant_replacements[parameter] = pt.constant(
+                    np.array(self.param_dict[parameter.name]).astype(floatX),
+                    name=parameter.name,
+                )
+            else:
+                self._name_to_variable[parameter.name] = parameter
+
+        self._linearized_system_subbed = [A, B, C, D] = pytensor.graph_replace(
+            self.linearized_system, constant_replacements, strict=False
+        )
+
+        self._bk_output = check_bk_condition_pt(A, B, C, D)
+        n_steps = None
+
+        if self._solver == "gensys":
+            T, R, success = gensys_pt(A, B, C, D, **self._solver_kwargs)
+        elif self._solver == "cycle_reduction":
+            T, R = cycle_reduction_pt(A, B, C, D, **self._solver_kwargs)
+        else:
+            T, R, n_steps = scan_cycle_reduction(
+                A, B, C, D, mode=self._mode, **self._solver_kwargs
+            )
+
+        resid = pt.square(A + B @ T + C @ T @ T).sum()
+
+        ss_resid = pytensor.graph_replace(
+            self.ss_resid, constant_replacements, strict=False
+        )
+        ss_resid = pt.square(ss_resid).sum()
+
+        T = rewrite_pregrad(T)
+        R = rewrite_pregrad(R)
+        resid = rewrite_pregrad(resid)
+        ss_resid = rewrite_pregrad(ss_resid)
+
+        self._policy_graph = [T, R]
+        self._n_steps = n_steps
+        self._policy_resid = resid
+        self._ss_resid = ss_resid
+
+        self.ssm["transition", :, :] = T
+        self.ssm["selection", :, :] = R
+        self.ssm["design", :, :] = self._make_design_matrix()
+
+        if not self.full_covariance:
+            for i, shock in enumerate(self.shocks):
+                sigma = self.make_and_register_variable(
+                    f"sigma_{shock.base_name}", shape=()
+                )
+                self.ssm["state_cov", i, i] = sigma**2
+        else:
+            state_cov = self.make_and_register_variable(
+                "state_cov", shape=(self.k_posdef, self.k_posdef)
+            )
+            self.ssm["state_cov", :, :] = state_cov
+
+        if self.measurement_error:
+            for i, state in enumerate(self.error_states):
+                sigma = self.make_and_register_variable(f"sigma_{state}", shape=())
+                self.ssm["obs_cov", i, i] = sigma**2
+
+        self.ssm["initial_state", :] = pt.zeros(self.k_states)
+
+        Q = self.ssm["state_cov"]
+        self.ssm["initial_state_cov", :, :] = pt.linalg.solve_discrete_lyapunov(
+            T, R @ Q @ R.T
+        )
+
+    def configure(
+        self,
+        observed_states: list[str],
+        measurement_error: list[str] | None = None,
+        constant_params: list[str] | None = None,
+        full_shock_covaraince: bool = False,
+        solver: str = "gensys",
+        mode: str | None = None,
+        **solver_kwargs,
+    ):
+        # Set up observed states
+        unknown_states = [x for x in observed_states if x not in self.state_names]
+        if len(unknown_states) > 0:
+            raise ValueError(
+                f'The following states are unknown to the model and cannot be set as observed: '
+                f'{", ".join(unknown_states)}'
+            )
+
+        # Set up measurement errors
+        if measurement_error is None:
+            measurement_error = []
+        else:
+            unknown_states = [x for x in measurement_error if x not in observed_states]
+            if len(unknown_states) > 0:
+                raise ValueError(
+                    f'The following states are not observed, and cannot have measurement error: '
+                    f'{", ".join(unknown_states)}'
+                )
+
+        # Validate constant params
+        if constant_params is None:
+            constant_params = []
+        else:
+            input_param_names = [x.name for x in self.input_parameters]
+            unknown_params = [x for x in constant_params if x not in input_param_names]
+            if len(unknown_params) > 0:
+                raise ValueError(
+                    f'The following parameters are unknown to the model and cannot be set as constant: '
+                    f'{", ".join(unknown_params)}'
+                )
+
+        # Validate solver argument
+        if solver not in ["gensys", "cycle_reduction", "scan_cycle_reduction"]:
+            raise ValueError(
+                f'Unknown solver {solver}, expected one of "gensys", "cycle_reduction", '
+                f'or "scan_cycle_reduction"'
+            )
+
+        # Check model is identified
+        k_endog = len(observed_states)
+        model_df = len(measurement_error) + len(self.shock_names)
+        verb = "are" if model_df != 1 else "is"
+        suffix = "s" if model_df != 1 else ""
+        if k_endog > model_df:
+            raise ValueError(
+                f"Stochastic singularity! You requested {k_endog} observed timeseries, but there {verb} "
+                f"only {model_df} source{suffix} of stochastic variation. "
+                f"\n\nReduce the number of observed timeseries, or add more sources of stochastic "
+                f"variation (by adding measurement error or structural shocks)"
+            )
+
+        self._obs_state_names = observed_states
+        self.error_states = measurement_error
+        self.constant_parameters = constant_params
+
+        self.full_covariance = full_shock_covaraince
+        self._configured = True
+        self._solver = solver
+        self._solver_kwargs = solver_kwargs
+        self._mode = mode
+
+        # Rebuild the internal statespace representation and kalman filters with the newly resized matrices
+        super().__init__(
+            k_endog,
+            self.k_states,
+            self.k_posdef,
+            measurement_error=len(measurement_error) > 0,
+            verbose=True,
+        )
+
+    def _make_design_matrix(self):
+        Z = np.zeros((self.k_endog, self.k_states))
+
+        for i, name in enumerate(self.observed_states):
+            Z[i, self.state_names.index(name)] = 1.0
+
+        return Z
+
+    @property
+    def param_names(self):
+        param_names = [x.name for x in self.input_parameters]
+        if self.constant_parameters is not None:
+            param_names = [x for x in param_names if x not in self.constant_parameters]
+
+        if self.full_covariance:
+            param_names += ["state_cov"]
+        else:
+            param_names += [f"sigma_{shock.base_name}" for shock in self.shocks]
+
+        if self.measurement_error:
+            param_names += [f"sigma_{state}" for state in self.error_states]
+
+        return param_names
+
+    @property
+    def state_names(self):
+        return [x.base_name for x in self.variables]
+
+    @property
+    def shock_names(self):
+        return [x.base_name for x in self.shocks]
+
+    @property
+    def observed_states(self):
+        return self._obs_state_names
+
+    @property
+    def param_dims(self):
+        if not self._configured:
+            return {}
+
+        return {
+            param: None if param != "state_cov" else (SHOCK_DIM, SHOCK_AUX_DIM)
+            for param in self.param_names
+        }
+
+    @property
+    def coords(self):
+        coords = make_default_coords(self)
+        return coords
+
+    @property
+    def param_info(self):
+        info = {}
+        if not self._configured:
+            return info
+
+        for var in self.param_names:
+            placeholder = self._name_to_variable[var]
+
+            info[var] = {
+                "shape": placeholder.type.shape,
+                "initval": self.param_dict.get(var, None),
+            }
+            if var.startswith("sigma"):
+                info[var]["constraints"] = "Positive"
+            elif var == "state_cov":
+                info[var]["constraints"] = "Positive Semi-Definite"
+            else:
+                info[var]["constraints"] = None
+
+        # Lazy way to add the dims without making any typos
+        for name in self.param_names:
+            info[name]["dims"] = self.param_dims[name]
+
+        return info
+
+    def build_statespace_graph(
+        self,
+        data: np.ndarray | pd.DataFrame | pt.TensorVariable,
+        register_data: bool = True,
+        missing_fill_value: float | None = None,
+        cov_jitter: float | None = JITTER_DEFAULT,
+        save_kalman_filter_outputs_in_idata: bool = False,
+        add_norm_check: bool = True,
+        add_bk_check: bool = False,
+        add_solver_success_check: bool = False,
+        add_steady_state_penalty: bool = True,
+        resid_penalty: float = 1.0,
+    ) -> None:
+        super().build_statespace_graph(
+            data=data,
+            register_data=register_data,
+            missing_fill_value=missing_fill_value,
+            cov_jitter=cov_jitter,
+            save_kalman_filter_outputs_in_idata=save_kalman_filter_outputs_in_idata,
+            mode=self._mode,
+        )
+
+        pymc_model = pm.modelcontext(None)
+
+        replacement_dict = {
+            var: pymc_model[name] for name, var in self._name_to_variable.items()
+        }
+
+        A, B, C, D, T, R = graph_replace(
+            self._linearized_system_subbed + self._policy_graph,
+            replace=replacement_dict,
+            strict=False,
+        )
+
+        if self._n_steps is not None:
+            n_steps = graph_replace(
+                self._n_steps, replace=replacement_dict, strict=False
+            )
+            pm.Deterministic("n_cycle_steps", n_steps.astype(int))
+
+        policy_resid, *bk_output, ss_resid = graph_replace(
+            [self._policy_resid, *self._bk_output, self._ss_resid],
+            replace=replacement_dict,
+            strict=False,
+        )
+
+        bk_flag, n_forward, n_gt_one = bk_output
+
+        if add_norm_check:
+            n_vars, n_shocks = R.shape
+            tm1_grid = np.array(
+                [
+                    [eq.has(var.set_t(-1)) for var in self.variables]
+                    for eq in self.equations
+                ]
+            )
+            t_grid = np.array(
+                [
+                    [eq.has(var.set_t(0)) for var in self.variables]
+                    for eq in self.equations
+                ]
+            )
+
+            tm1_idx = np.any(tm1_grid, axis=0)
+            t_idx = np.any(t_grid, axis=0)
+
+            shock_idx = pt.arange(n_shocks)
+            state_var_mask = pt.bitwise_and(tm1_idx, t_idx)
+
+            QQ = R[:n_vars, :]
+            P = T[state_var_mask, :][:, state_var_mask]
+            Q = QQ[state_var_mask, :][:, shock_idx]
+
+            A_prime = A[:, state_var_mask]
+            R_prime = T[:, state_var_mask]
+            S_prime = QQ[:, shock_idx]
+
+            norm_deterministic = pm.Deterministic(
+                "deterministic_norm",
+                pt.linalg.norm(A_prime + B @ R_prime + C @ R_prime @ P),
+            )
+            norm_stochastic = pm.Deterministic(
+                "stochastic_norm", pt.linalg.norm(B @ S_prime + C @ R_prime @ Q + D)
+            )
+
+            # Add penalty terms to the likelihood to rule out invalid solutions
+            pm.Potential(
+                "solution_norm_penalty",
+                -resid_penalty * (norm_deterministic + norm_stochastic),
+            )
+
+        if add_bk_check:
+            pm.Deterministic("bk_flag", bk_flag)
+            pm.Potential(
+                "bk_condition_satisfied", pt.switch(pt.eq(bk_flag, 1.0), 0.0, -np.inf)
+            )
+
+        if add_solver_success_check:
+            policy_resid = pm.Deterministic("policy_resid", policy_resid)
+            pm.Potential("policy_resid_penalty", -resid_penalty * policy_resid)
+
+        if add_steady_state_penalty:
+            ss_resid = pm.Deterministic("ss_resid", ss_resid)
+            pm.Potential("steady_state_resid_penalty", -resid_penalty * ss_resid)
+
+    def priors_to_preliz(self):
+        priors = self.priors[0]
+        pz_priors = {}
+
+        for name, rv in priors.items():
+            dist_type = type(rv.dist)
+            pz_dist = SCIPY_TO_PRELIZ[dist_type]
+
+            match rv.dist:
+                case norm_gen():
+                    pz_priors[name] = pz_dist(mu=rv.kwds["loc"], sigma=rv.kwds["scale"])
+                case truncnorm_gen():
+                    loc, scale, a, b = (rv.kwds[x] for x in ["loc", "scale", "a", "b"])
+                    lower = loc + scale * a
+                    upper = loc + scale * b
+                    pz_priors[name] = pz_dist(
+                        mu=loc, sigma=scale, lower=lower, upper=upper
+                    )
+                case halfnorm_gen():
+                    pz_priors[name] = pz_dist(sigma=rv.kwds["scale"])
+                case gamma_gen():
+                    pz_priors[name] = pz_dist(
+                        alpha=rv.kwds["a"], beta=1 / rv.kwds["scale"]
+                    )
+                case beta_gen():
+                    pz_priors[name] = pz_dist(alpha=rv.kwds["a"], beta=rv.kwds["b"])
+                case uniform_gen():
+                    pz_priors[name] = pz_dist(lower=rv.kwds["a"], upper=rv.kwds["b"])
+                case invgamma_gen():
+                    pz_priors[name] = pz_dist(alpha=rv.kwds["a"], beta=rv.kwds["scale"])
+                case expon_gen():
+                    pz_priors[name] = pz_dist(lam=1 / rv.kwds["scale"])
+
+        return pz_priors
diff --git a/gEconpy/model/steady_state.py b/gEconpy/model/steady_state.py
new file mode 100644
index 0000000..cd03c07
--- /dev/null
+++ b/gEconpy/model/steady_state.py
@@ -0,0 +1,366 @@
+import logging
+
+from typing import Literal, cast
+
+import sympy as sp
+
+from gEconpy.classes.containers import SteadyStateResults, SymbolDictionary
+from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
+from gEconpy.model.compile import (
+    BACKENDS,
+    compile_function,
+    dictionary_return_wrapper,
+    make_return_dict_and_update_cache,
+)
+from gEconpy.model.parameters import compile_param_dict_func
+from gEconpy.utilities import eq_to_ss
+
+_log = logging.getLogger(__name__)
+
+ERROR_FUNCTIONS = Literal["squared", "mean_squared", "abs", "l2-norm"]
+
+
+def _validate_optimizer_kwargs(
+    optimizer_kwargs: dict,
+    n_eq: int,
+    method: str,
+    use_jac: bool,
+    use_hess: bool,
+) -> dict:
+    """
+    Validate user-provided keyword arguments to either scipy.optimize.root or scipy.optimize.minimize, and insert
+    defaults where not provided.
+
+    Note: This function never overwrites user arguments.
+
+    Parameters
+    ----------
+    optimizer_kwargs: dict
+        User-provided arguments for the optimizer
+    n_eq: int
+        Number of remaining steady-state equations after reduction
+    method: str
+        Which family of solution algorithms, minimization or root-finding, to be used.
+    use_jac: bool
+        Whether computation of the jacobian has been requested
+    use_hess: bool
+        Whether computation of the hessian has been requested
+
+    Returns
+    -------
+    optimizer_kwargs: dict
+        Keyword arguments for the scipy function, with "reasonable" defaults inserted where not provided
+    """
+
+    optimizer_kwargs = {} if optimizer_kwargs is None else optimizer_kwargs
+    method_given = "method" in optimizer_kwargs.keys()
+
+    if method == "root" and not method_given:
+        if use_jac:
+            optimizer_kwargs["method"] = "hybr"
+        else:
+            optimizer_kwargs["method"] = "broyden1"
+
+        if n_eq == 1:
+            optimizer_kwargs["method"] = "lm"
+
+    elif method == "minimize" and not method_given:
+        # Set optimizer_kwargs for minimization
+        if use_hess and use_jac:
+            optimizer_kwargs["method"] = "trust-exact"
+        elif use_jac:
+            optimizer_kwargs["method"] = "BFGS"
+        else:
+            optimizer_kwargs["method"] = "Nelder-Mead"
+
+    if "tol" not in optimizer_kwargs.keys():
+        optimizer_kwargs["tol"] = 1e-9
+
+    return optimizer_kwargs
+
+
+def make_steady_state_shock_dict(shocks):
+    return SymbolDictionary.fromkeys(shocks, 0.0).to_ss()
+
+
+def make_steady_state_variables(variables):
+    return list(map(lambda x: x.to_ss(), variables))
+
+
+def system_to_steady_state(system, shocks):
+    shock_dict = make_steady_state_shock_dict(shocks)
+    steady_state_system = [eq_to_ss(eq).subs(shock_dict).simplify() for eq in system]
+
+    return steady_state_system
+
+
+def faster_simplify(x: sp.Expr, var_list: list[TimeAwareSymbol]):
+    # return sp.powsimp(sp.powdenest(x, force=True), force=True)
+    return x
+
+
+def steady_state_error_function(
+    steady_state, variables: list[sp.Symbol], func: ERROR_FUNCTIONS = "squared"
+) -> sp.Expr:
+    ss_vars = [x.to_ss() if isinstance(x, TimeAwareSymbol) else x for x in variables]
+
+    if func == "squared":
+        error = sum([faster_simplify(eq**2, ss_vars) for eq in steady_state])
+    elif func == "mean_squared":
+        error = sum([faster_simplify(eq**2, ss_vars) for eq in steady_state]) / len(
+            steady_state
+        )
+    elif func == "abs":
+        error = sum([faster_simplify(sp.Abs(eq), ss_vars) for eq in steady_state])
+    elif func == "l2-norm":
+        error = sp.sqrt(sum([faster_simplify(eq**2, ss_vars) for eq in steady_state]))
+    else:
+        raise NotImplementedError(
+            f"Error function {func} not implemented, must be one of {ERROR_FUNCTIONS}"
+        )
+
+    return error
+
+
+def compile_ss_resid_and_sq_err(
+    steady_state: list[sp.Expr],
+    variables: list[TimeAwareSymbol],
+    parameters: list[sp.Symbol],
+    ss_error: sp.Expr,
+    backend: BACKENDS,
+    cache: dict,
+    return_symbolic: bool,
+    **kwargs,
+):
+    cache = {} if cache is None else cache
+    ss_variables = [x.to_ss() if hasattr(x, "to_ss") else x for x in variables]
+    resid_jac = sp.Matrix(
+        [
+            [faster_simplify(eq.diff(x), ss_variables) for x in ss_variables]
+            for eq in steady_state
+        ]
+    )
+
+    f_ss_resid, cache = compile_function(
+        ss_variables + parameters,
+        steady_state,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        stack_return=True,
+        pop_return=False,
+        **kwargs,
+    )
+
+    f_ss_jac, cache = compile_function(
+        ss_variables + parameters,
+        resid_jac,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        # for pytensor/numba, the return is a single object; don't stack into a (1,n,n) array
+        stack_return=backend == "numpy",
+        # Numba directly returns the jacobian as an array, don't pop
+        # pytensor and lambdify return a list of one item, so we have to extract it.
+        pop_return=backend != "numba",
+        **kwargs,
+    )
+
+    error_grad = [faster_simplify(ss_error.diff(x), ss_variables) for x in ss_variables]
+    error_hess = sp.Matrix(
+        [
+            [faster_simplify(eq.diff(x), ss_variables) for eq in error_grad]
+            for x in ss_variables
+        ]
+    )
+
+    n = len(ss_variables)
+    p = sp.IndexedBase("hess_eval_point", shape=n)
+    hessp_loss = cast(sp.Expr, sum([error_grad[i] * p[i] for i in range(n)]))
+    hessp = [faster_simplify(hessp_loss.diff(x), ss_variables) for x in ss_variables]
+
+    f_ss_error, cache = compile_function(
+        ss_variables + parameters,
+        [ss_error],
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        pop_return=True,
+        stack_return=False,
+        **kwargs,
+    )
+
+    f_ss_grad, cache = compile_function(
+        ss_variables + parameters,
+        error_grad,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        stack_return=True,
+        pop_return=False,
+        **kwargs,
+    )
+
+    f_ss_hess, cache = compile_function(
+        ss_variables + parameters,
+        error_hess,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        # error_hess is a list of one element; don't stack into a (1,n,n) array
+        stack_return=backend != "pytensor",
+        # Numba directly returns the hessian as an array, don't pop
+        pop_return=backend != "numba",
+        **kwargs,
+    )
+
+    f_ss_hessp, cache = compile_function(
+        [p, *ss_variables, *parameters],
+        hessp,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        stack_return=True,
+        pop_return=False,
+        **kwargs,
+    )
+
+    return (f_ss_resid, f_ss_jac), (f_ss_error, f_ss_grad, f_ss_hess, f_ss_hessp), cache
+
+
+def compile_known_ss(
+    ss_solution_dict: SymbolDictionary,
+    variables: list[TimeAwareSymbol | sp.Symbol],
+    parameters: list[sp.Symbol],
+    backend: BACKENDS,
+    cache: dict,
+    return_symbolic: bool = False,
+    stack_return: bool | None = None,
+    **kwargs,
+):
+    def to_ss(x):
+        if isinstance(x, TimeAwareSymbol):
+            return x.to_ss()
+        return x
+
+    cache = {} if cache is None else cache
+    if not ss_solution_dict:
+        return None, cache
+
+    ss_solution_dict = ss_solution_dict.to_sympy()
+    ss_variables = [to_ss(x) for x in variables]
+
+    sorted_solution_dict = {
+        to_ss(k): ss_solution_dict[to_ss(k)]
+        for k in ss_variables
+        if k in ss_solution_dict.keys()
+    }
+
+    output_vars, output_exprs = (
+        list(sorted_solution_dict.keys()),
+        list(sorted_solution_dict.values()),
+    )
+    if stack_return is None:
+        stack_return = True if not return_symbolic else False
+
+    f_ss, cache = compile_function(
+        parameters,
+        output_exprs,
+        backend=backend,
+        cache=cache,
+        stack_return=stack_return,
+        return_symbolic=return_symbolic,
+        **kwargs,
+    )
+    if return_symbolic and backend == "pytensor":
+        return make_return_dict_and_update_cache(
+            ss_variables, f_ss, cache, TimeAwareSymbol
+        )
+
+    return dictionary_return_wrapper(f_ss, output_vars), cache
+
+
+def compile_model_ss_functions(
+    steady_state_equations,
+    ss_solution_dict,
+    variables,
+    param_dict,
+    deterministic_dict,
+    calib_dict,
+    error_func: ERROR_FUNCTIONS = "squared",
+    backend: BACKENDS = "numpy",
+    return_symbolic: bool = False,
+    **kwargs,
+):
+    cache = {}
+    f_params, cache = compile_param_dict_func(
+        param_dict,
+        deterministic_dict,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+    )
+
+    calib_eqs = list(calib_dict.to_sympy().values())
+    steady_state_equations = steady_state_equations + calib_eqs
+
+    parameters = list((param_dict | deterministic_dict).to_sympy().keys())
+    parameters = [x for x in parameters if x not in calib_dict.to_sympy()]
+
+    variables = variables + list(calib_dict.to_sympy().keys())
+    ss_error = steady_state_error_function(
+        steady_state_equations, variables, error_func
+    )
+
+    f_ss, cache = compile_known_ss(
+        ss_solution_dict,
+        variables,
+        parameters,
+        backend=backend,
+        cache=cache,
+        return_symbolic=return_symbolic,
+        **kwargs,
+    )
+
+    (f_ss_resid, f_ss_jac), (f_ss_error, f_ss_grad, f_ss_hess, f_ss_hessp), cache = (
+        compile_ss_resid_and_sq_err(
+            steady_state_equations,
+            variables,
+            parameters,
+            ss_error,
+            backend=backend,
+            cache=cache,
+            return_symbolic=return_symbolic,
+            **kwargs,
+        )
+    )
+
+    return (
+        f_params,
+        f_ss,
+        (f_ss_resid, f_ss_jac),
+        (f_ss_error, f_ss_grad, f_ss_hess, f_ss_hessp),
+    ), cache
+
+
+def print_steady_state(ss_dict: SteadyStateResults):
+    output = []
+    if not ss_dict.success:
+        output.append(
+            "Values come from the latest solver iteration but are NOT a valid steady state."
+        )
+
+    max_var_name = max(len(x) for x in list(ss_dict.keys())) + 5
+
+    calibrated_outputs = []
+    for key, value in ss_dict.to_sympy().items():
+        if isinstance(key, TimeAwareSymbol):
+            output.append(f"{key.name:{max_var_name}}{value:>10.3f}")
+        else:
+            calibrated_outputs.append(f"{key.name:{max_var_name}}{value:>10.3f}")
+
+    if len(calibrated_outputs) > 0:
+        output.append("\n")
+        output.extend(calibrated_outputs)
+
+    _log.info("\n".join(output))
diff --git a/gEconpy/numba_tools/LAPACK.py b/gEconpy/numbaf/LAPACK.py
similarity index 89%
rename from gEconpy/numba_tools/LAPACK.py
rename to gEconpy/numbaf/LAPACK.py
index dd47b44..147727e 100644
--- a/gEconpy/numba_tools/LAPACK.py
+++ b/gEconpy/numbaf/LAPACK.py
@@ -274,3 +274,33 @@ def numba_xtrtrs(cls, dtype):
         )  # INFO
 
         return functype(addr)
+
+    @classmethod
+    def numba_xsysv(cls, dtype):
+        """
+        From LAPACK docs:
+
+        *SYSV computes the solution to a real system of linear equations
+        A * X = B,
+        where A is an N-by-N symmetric matrix and X and B are N-by-NRHS
+        matrices.
+        """
+        d = _blas_kinds[dtype]
+        func_name = f"{d}sysv"
+        float_pointer = _get_float_pointer_for_dtype(d)
+        addr = get_cython_function_address("scipy.linalg.cython_lapack", func_name)
+        functype = ctypes.CFUNCTYPE(
+            None,
+            _ptr_int,  # UPLO
+            _ptr_int,  # N
+            _ptr_int,  # NRHS
+            float_pointer,  # A
+            _ptr_int,  # LDA
+            _ptr_int,  # IPIV
+            float_pointer,  # B
+            _ptr_int,  # LDB
+            float_pointer,  # WORK
+            _ptr_int,  # LWORK
+            _ptr_int,  # INFO
+        )
+        return functype(addr)
diff --git a/gEconpy/numbaf/__init__.py b/gEconpy/numbaf/__init__.py
new file mode 100644
index 0000000..242ed7b
--- /dev/null
+++ b/gEconpy/numbaf/__init__.py
@@ -0,0 +1,17 @@
+from gEconpy.numbaf.overloads import (
+    nb_ordqz,
+    nb_qz,
+    nb_schur,
+    nb_solve_continuous_lyapunov,
+    nb_solve_discrete_lyapunov,
+    nb_solve_triangular,
+)
+
+__all__ = [
+    "nb_solve_triangular",
+    "nb_schur",
+    "nb_qz",
+    "nb_ordqz",
+    "nb_solve_continuous_lyapunov",
+    "nb_solve_discrete_lyapunov",
+]
diff --git a/gEconpy/numba_tools/intrinsics.py b/gEconpy/numbaf/intrinsics.py
similarity index 100%
rename from gEconpy/numba_tools/intrinsics.py
rename to gEconpy/numbaf/intrinsics.py
diff --git a/gEconpy/numba_tools/overloads.py b/gEconpy/numbaf/overloads.py
similarity index 86%
rename from gEconpy/numba_tools/overloads.py
rename to gEconpy/numbaf/overloads.py
index fd0abe7..a5a571b 100644
--- a/gEconpy/numba_tools/overloads.py
+++ b/gEconpy/numbaf/overloads.py
@@ -1,5 +1,7 @@
+from collections.abc import Callable
+
 import numpy as np
-import scipy
+
 from numba.core import types
 from numba.extending import overload
 from numba.np.linalg import (
@@ -10,9 +12,9 @@
 )
 from scipy import linalg
 
-from gEconpy.numba_tools.intrinsics import int_ptr_to_val, val_to_int_ptr
-from gEconpy.numba_tools.LAPACK import _LAPACK
-from gEconpy.numba_tools.utilities import (
+from gEconpy.numbaf.intrinsics import int_ptr_to_val, val_to_int_ptr
+from gEconpy.numbaf.LAPACK import _LAPACK
+from gEconpy.numbaf.utilities import (
     _check_scipy_linalg_matrix,
     _get_underlying_float,
     _iuc,
@@ -23,7 +25,27 @@
 )
 
 
-@overload(scipy.linalg.solve_triangular)
+def nb_solve_triangular(
+    a: np.ndarray,
+    b: np.ndarray,
+    trans: int | str | None = 0,
+    lower: bool | None = False,
+    unit_diagonal: bool | None = False,
+    overwrite_b: bool | None = False,
+    check_finite: bool | None = True,
+) -> np.ndarray:
+    return linalg.solve_triangular(
+        a,
+        b,
+        trans=trans,
+        lower=lower,
+        unit_diagonal=unit_diagonal,
+        overwrite_b=overwrite_b,
+        check_finite=check_finite,
+    )
+
+
+@overload(nb_solve_triangular)
 def solve_triangular_impl(A, B, trans=0, lower=False, unit_diagonal=False):
     ensure_lapack()
 
@@ -88,7 +110,25 @@ def impl(A, B, trans=0, lower=False, unit_diagonal=False):
     return impl
 
 
-@overload(scipy.linalg.schur)
+def nb_schur(
+    a: np.ndarray,
+    output: str | None = "real",
+    lwork: int | None = None,
+    overwrite_a: bool | None = False,
+    sort: None | Callable | str = None,
+    check_finite: bool | None = True,
+) -> tuple[np.ndarray, np.ndarray] | tuple[np.ndarray, np.ndarray, int]:
+    return linalg.schur(
+        a=a,
+        output=output,
+        lwork=lwork,
+        overwrite_a=overwrite_a,
+        sort=sort,
+        check_finite=check_finite,
+    )
+
+
+@overload(nb_schur)
 def schur_impl(A, output):
     ensure_lapack()
 
@@ -258,7 +298,35 @@ def complex_schur_impl(A, output):
         return real_schur_impl
 
 
-def full_return_qz(A, B, output):
+def nb_qz(
+    A: np.ndarray,
+    B: np.ndarray,
+    output: str | None = "real",
+    lwork: int | None = None,
+    sort: None | Callable | str = None,
+    overwrite_a: bool | None = False,
+    overwrite_b: bool | None = False,
+    check_finite: bool | None = True,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    return linalg.qz(
+        A=A,
+        B=B,
+        output=output,
+        lwork=lwork,
+        sort=sort,
+        overwrite_a=overwrite_a,
+        overwrite_b=overwrite_b,
+        check_finite=check_finite,
+    )
+
+
+def full_return_qz(
+    A, B, output
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Dummy function to be overloaded below. It's purpose is to match the signature of the underlying LAPACK function,
+    rather than the scipy function.
+    """
     pass
 
 
@@ -482,7 +550,7 @@ def complex_full_return_qz_impl(A, B, output):
         return real_full_return_qz_impl
 
 
-@overload(scipy.linalg.qz)
+@overload(nb_qz)
 def qz_impl(A, B, output):
     """
     scipy.linalg.qz overload. Wraps full_return_qz and returns only A, B, Q ,Z to match the scipy signature.
@@ -507,7 +575,27 @@ def complex_qz_impl(A, B, output):
         return real_qz_impl
 
 
-@overload(scipy.linalg.ordqz)
+def nb_ordqz(
+    A: np.ndarray,
+    B: np.ndarray,
+    sort: Callable | str | None = "lhp",
+    output: str | None = "real",
+    overwrite_a: bool | None = False,
+    overwrite_b: bool | None = False,
+    check_finite: bool | None = True,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    return linalg.ordqz(
+        A=A,
+        B=B,
+        sort=sort,
+        output=output,
+        overwrite_a=overwrite_a,
+        overwrite_b=overwrite_b,
+        check_finite=check_finite,
+    )
+
+
+@overload(nb_ordqz)
 def ordqz_impl(A, B, sort, output):
     ensure_lapack()
 
@@ -761,7 +849,11 @@ def complex_ordqz_impl(A, B, sort, output):
         return real_ordqz_impl
 
 
-@overload(scipy.linalg.solve_continuous_lyapunov)
+def nb_solve_continuous_lyapunov(a: np.ndarray, q: np.ndarray) -> np.ndarray:
+    return linalg.solve_continuous_lyapunov(a=a, q=q)
+
+
+@overload(nb_solve_continuous_lyapunov)
 def solve_continuous_lyapunov_impl(A, Q):
     ensure_lapack()
 
@@ -843,7 +935,11 @@ def _solve_cont_lyapunov_impl(A, Q):
     return _solve_cont_lyapunov_impl
 
 
-@overload(scipy.linalg.solve_discrete_lyapunov)
+def nb_solve_discrete_lyapunov(a, q, method="auto"):
+    return linalg.solve_discrete_lyapunov(a=a, q=q, method=method)
+
+
+@overload(nb_solve_discrete_lyapunov)
 def solve_discrete_lyapunov_impl(A, Q, method="auto"):
     ensure_lapack()
 
@@ -876,3 +972,29 @@ def impl(A, Q, method="auto"):
         return X
 
     return impl
+
+
+# def solve_assume_a_sym_impl(A, B,
+#                             lower: bool = False,
+#                             check_finite: bool = True,
+#                             transposed: bool = False):
+#     ensure_lapack()
+#
+#     _check_scipy_linalg_matrix(A, "solve(assume_a='sym')")
+#     _check_scipy_linalg_matrix(B, "solve(assume_a='sym')")
+#
+#     dtype = A.dtype
+#     w_type = _get_underlying_float(dtype)
+#     numba_xsysv = _LAPACK().numba_xsysv(dtype)
+#
+#     def impl(A, B, lower, check_finite, transposed):
+
+
+__all__ = [
+    "nb_solve_triangular",
+    "nb_schur",
+    "nb_qz",
+    "nb_ordqz",
+    "nb_solve_discrete_lyapunov",
+    "nb_solve_continuous_lyapunov",
+]
diff --git a/gEconpy/numba_tools/utilities.py b/gEconpy/numbaf/utilities.py
similarity index 61%
rename from gEconpy/numba_tools/utilities.py
rename to gEconpy/numbaf/utilities.py
index 20ac63f..03bf32e 100644
--- a/gEconpy/numba_tools/utilities.py
+++ b/gEconpy/numbaf/utilities.py
@@ -1,11 +1,16 @@
 import re
+
 from collections.abc import Callable
 
 import numba as nb
 import numpy as np
 import sympy as sp
+
 from numba.core import types
 from numba.core.errors import TypingError
+from sympy.printing.numpy import NumPyPrinter, _known_functions_numpy
+
+_known_functions_numpy.update({"DiracDelta": lambda x: 0.0, "log": "log"})
 
 # Pattern needs to hit "0," and "0]". but not "x0" or "6.0", and return the
 # close-bracket (if any).
@@ -25,18 +30,18 @@ def _get_underlying_float(dtype):
 
 def _check_scipy_linalg_matrix(a, func_name):
     prefix = "scipy.linalg"
-    interp = (prefix, func_name)
+
     # Unpack optional type
     if isinstance(a, types.Optional):
         a = a.type
     if not isinstance(a, types.Array):
-        msg = "%s.%s() only supported for array types" % interp
+        msg = f"{prefix}.{func_name} only supported for array types"
         raise TypingError(msg, highlighting=False)
     if not a.ndim == 2:
-        msg = "%s.%s() only supported on 2-D arrays." % interp
+        msg = f"{prefix}.{func_name} only supported on 2-D arrays."
         raise TypingError(msg, highlighting=False)
-    if not isinstance(a.dtype, (types.Float, types.Complex)):
-        msg = "%s.%s() only supported on " "float and complex arrays." % interp
+    if not isinstance(a.dtype, types.Float | types.Complex):
+        msg = f"{prefix}.{func_name} only supported on " "float and complex arrays."
         raise TypingError(msg, highlighting=False)
 
 
@@ -92,6 +97,7 @@ def _ouc(alpha, beta):
         alpha vector, as returned by zgges
     beta: Array, complex
         beta vector, as return by zgges
+
     Returns
     -------
     out: Array, bool
@@ -109,11 +115,65 @@ def _ouc(alpha, beta):
     return out
 
 
+class NumbaFriendlyNumPyPrinter(NumPyPrinter):
+    _kf = _known_functions_numpy
+
+    def _print_Max(self, expr):
+        # Use maximum instead of amax, because 1) we only expect scalars, and 2) numba doesn't accept amax
+        return "{}({})".format(
+            self._module_format(self._module + ".maximum"),
+            ",".join(self._print(i) for i in expr.args),
+        )
+
+    def _print_Piecewise(self, expr):
+        # Use the default python Piecewise instead of the numpy one -- looping with if conditions is faster in numba
+        # anyway.
+        result = []
+        i = 0
+        for arg in expr.args:
+            e = arg.expr
+            c = arg.cond
+            if i == 0:
+                result.append("(")
+            result.append("(")
+            result.append(self._print(sp.Float(e)))
+            result.append(")")
+            result.append(" if ")
+            result.append(self._print(c))
+            result.append(" else ")
+            i += 1
+        result = result[:-1]
+        if result[-1] == "True":
+            result = result[:-2]
+            result.append(")")
+        else:
+            result.append(" else None)")
+        return "".join(result)
+
+    def _print_DiracDelta(self, expr):
+        # The proper function should return infinity at one point, but the measure of that point is zero so this should
+        # be fine. Pytensor defines grad(grad(max(0, x), x), x) to be zero everywhere.
+        return "0.0"
+
+    def _print_log(self, expr):
+        return "{}({})".format(
+            self._module_format(self._module + ".log"),
+            ",".join(self._print(i) for i in expr.args),
+        )
+
+    def _print_exp(self, expr):
+        return "{}({})".format(
+            self._module_format(self._module + ".exp"),
+            ",".join(self._print(i) for i in expr.args),
+        )
+
+
 def numba_lambdify(
-    exog_vars: list[sp.Symbol],
+    inputs: list[sp.Symbol],
     expr: list[sp.Expr] | sp.Matrix | list[sp.Matrix],
-    endog_vars: list[sp.Symbol] | None = None,
     func_signature: str | None = None,
+    ravel_outputs=False,
+    stack_outputs=False,
 ) -> Callable:
     """
     Convert a sympy expression into a Numba-compiled function.  Unlike sp.lambdify, the resulting function can be
@@ -124,7 +184,7 @@ def numba_lambdify(
 
     Parameters
     ----------
-    exog_vars: list of sympy.Symbol
+    inputs: list of sympy.Symbol
         A list of "exogenous" variables. The distinction between "exogenous" and "enodgenous" is
         useful when passing the resulting function to a scipy.optimize optimizer. In this context, exogenous
         variables should be the choice varibles used to minimize the function.
@@ -132,10 +192,13 @@ def numba_lambdify(
         The sympy expression(s) to be converted. Expects a list of expressions (in the case that we're compiling a
         system to be stacked into a single output vector), a single matrix (which is returned as a single nd-array)
         or a list of matrices (which are returned as a list of nd-arrays)
-    endog_vars : Optional, list of sympy.Symbol
-        A list of "exogenous" variables, passed as a second argument to the function.
     func_signature: str
         A numba function signature, passed to the numba.njit decorator on the generated function.
+    ravel_outputs: bool, default False
+        If true, all outputs of the jitted function will be raveled before they are returned. This is useful for
+        removing size-1 dimensions from sympy vectors.
+    stack_outputs: bool, default False
+        If true, stack all return values into a single vector. Otherwise they are returned as a tuple as usual.
 
     Returns
     -------
@@ -146,27 +209,19 @@ def numba_lambdify(
     -----
     The function returned by this function is pickleable.
     """
-
+    ZERO_PATTERN = re.compile(r"(?", code)
 
+            # Repair indexing -- we might have converted x[0] to x[0.0] or x[1] to x[1.0]
+            code = re.sub(ZERO_ONE_INDEX_PATTERN, r"\g<3>", code)
+
             code_name = f"retval_{i}"
             retvals.append(code_name)
             code = f"    {code_name} = np.array(\n{code}\n    )"
+            if ravel_outputs:
+                code += ".ravel()"
+
             codes.append(code)
         code = "\n".join(codes)
 
-    input_signature = "exog_inputs"
-    unpacked_inputs = "\n".join(
-        [
-            f"    {getattr(x, 'safe_name', x.name)} = exog_inputs[{i}]"
-            for i, x in enumerate(exog_vars)
-        ]
-    )
-    if endog_vars is not None:
-        input_signature += ", endog_inputs"
-        exog_unpacked = "\n".join(
-            [
-                f"    {getattr(x, 'safe_name', x.name)} = endog_inputs[{i}]"
-                for i, x in enumerate(endog_vars)
-            ]
-        )
-        unpacked_inputs += "\n" + exog_unpacked
+    input_signature = ", ".join([getattr(x, "safe_name", x.name) for x in inputs])
 
     assignments = "\n".join(
         [
-            f"    {x} = {sp.printing.numpy.NumPyPrinter().doprint(y).replace('numpy.', 'np.')}"
+            f"    {x} = {printer.doprint(y).replace('numpy.', 'np.')}"
             for x, y in sub_dict
         ]
     )
-    returns = f'[{",".join(retvals)}]' if len(retvals) > 1 else retvals[0]
-    full_code = f"{decorator}\ndef f({input_signature}):\n{unpacked_inputs}\n\n{assignments}\n\n{code}\n\n    return {returns}"
+    assignments = re.sub(ZERO_ONE_INDEX_PATTERN, r"\g<3>", assignments)
+
+    if len(retvals) > 1:
+        returns = f'({",".join(retvals)})'
+        if stack_outputs:
+            returns = f"np.stack({returns})"
+    else:
+        returns = retvals[0]
+    # returns = f'[{",".join(retvals)}]' if len(retvals) > 1 else retvals[0]
+    full_code = f"{decorator}\ndef f({input_signature}):\n\n{assignments}\n\n{code}\n\n    return {returns}"
 
     docstring = f"'''Automatically generated code:\n{full_code}'''"
-    code = f"{decorator}\ndef f({input_signature}):\n    {docstring}\n{len_checks}\n{unpacked_inputs}\n\n{assignments}\n\n{code}\n\n    return {returns}"
+    code = f"{decorator}\ndef f({input_signature}):\n    {docstring}\n\n{assignments}\n\n{code}\n\n    return {returns}"
 
     exec(code)
     return locals()["f"]
diff --git a/gEconpy/parser/constants.py b/gEconpy/parser/constants.py
index 9356cd0..575cf83 100644
--- a/gEconpy/parser/constants.py
+++ b/gEconpy/parser/constants.py
@@ -1,6 +1,7 @@
 import re
 
 import sympy as sp
+
 from sympy.abc import _clash1, _clash2
 
 LOCAL_DICT = {}
diff --git a/gEconpy/parser/dist_syntax.py b/gEconpy/parser/dist_syntax.py
new file mode 100644
index 0000000..744ccb2
--- /dev/null
+++ b/gEconpy/parser/dist_syntax.py
@@ -0,0 +1,46 @@
+import pyparsing as pp
+
+
+def evaluate_expression(parsed_expr):
+    if isinstance(parsed_expr, int | float):
+        return float(parsed_expr)
+    elif not parsed_expr:
+        return None
+    elif isinstance(parsed_expr, pp.ParseResults):
+        parsed_expr = parsed_expr.as_list()
+        if len(parsed_expr) == 1 and isinstance(parsed_expr[0], list):
+            parsed_expr = parsed_expr[0]
+        expr_str = "".join(map(str, parsed_expr))
+        return eval(expr_str, {"__builtins__": None}, {})
+    return parsed_expr
+
+
+var_name = pp.Word(pp.alphas, pp.alphanums + "_")
+dist_name = pp.Word(pp.alphas, pp.alphanums + "_")
+equals = pp.Literal("=").suppress()
+
+number = pp.pyparsing_common.number
+numeric_expr = pp.infixNotation(
+    number,
+    [
+        (pp.Literal("/"), 2, pp.opAssoc.LEFT),
+        (pp.Literal("*"), 2, pp.opAssoc.LEFT),
+        (pp.Literal("+"), 2, pp.opAssoc.LEFT),
+        (pp.Literal("-"), 2, pp.opAssoc.LEFT),
+    ],
+)
+
+value = numeric_expr | var_name
+
+key = pp.Word(pp.alphas, pp.alphanums + "_")
+key_value_pair = pp.Group(key + equals + value)
+
+args = (
+    pp.Suppress("(") + pp.Optional(pp.delimitedList(key_value_pair)) + pp.Suppress(")")
+)
+initial_value = pp.Optional(equals + numeric_expr, default=None)("initial_value")
+
+dist_syntax = dist_name("dist_name") + args("kwargs") + initial_value + pp.StringEnd()
+
+
+__all__ = ["dist_syntax", "evaluate_expression"]
diff --git a/gEconpy/parser/file_loaders.py b/gEconpy/parser/file_loaders.py
index 879e6e7..cfa0e36 100644
--- a/gEconpy/parser/file_loaders.py
+++ b/gEconpy/parser/file_loaders.py
@@ -1,3 +1,36 @@
+import logging
+
+from typing import Literal
+from warnings import warn
+
+import sympy as sp
+
+from gEconpy.classes.containers import SymbolDictionary
+from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
+from gEconpy.exceptions import (
+    DuplicateParameterError,
+    ExtraParameterError,
+    ExtraParameterWarning,
+    MultipleSteadyStateBlocksException,
+    OrphanParameterError,
+)
+from gEconpy.model.block import Block
+from gEconpy.model.simplification import simplify_constants, simplify_tryreduce
+from gEconpy.parser.constants import STEADY_STATE_NAMES
+from gEconpy.parser.gEcon_parser import (
+    ASSUMPTION_DICT,
+    parsed_block_to_dict,
+    preprocess_gcn,
+    split_gcn_into_dictionaries,
+)
+from gEconpy.parser.parse_distributions import create_prior_distribution_dictionary
+from gEconpy.parser.parse_equations import single_symbol_to_sympy
+from gEconpy.utilities import substitute_repeatedly, unpack_keys_and_values
+
+PARAM_DICTS = Literal["param_dict", "deterministic_dict", "calib_dict"]
+_log = logging.getLogger(__name__)
+
+
 def load_gcn(gcn_path: str) -> str:
     """
     Loads a model file as raw text.
@@ -16,3 +49,523 @@ def load_gcn(gcn_path: str) -> str:
     with open(gcn_path, encoding="utf-8") as file:
         gcn_raw = file.read()
     return gcn_raw
+
+
+def get_provided_ss_equations(
+    raw_blocks: dict[str, str],
+    assumptions: ASSUMPTION_DICT = None,
+) -> dict[str, sp.Expr]:
+    """
+    Extract user-provided steady state equations from the `raw_blocks` dictionary and store the resulting
+    relationships in self.steady_state_relationships.
+
+    Parameters
+    ----------
+    raw_blocks: dict[str, str]
+        Dictionary of block names and block contents extracted from a gEcon model.
+
+    assumptions: dict[str, dict[str, bool]]
+        Dictionary of assumptions about the model, with keys corresponding to variable names and values
+        corresponding to dictionaries of assumptions about the variable. See sympy documentation for more details.
+
+    Raises
+    ------
+    MultipleSteadyStateBlocksException
+        If there is more than one block in `raw_blocks` with a name from `STEADY_STATE_NAMES`.
+    """
+    block_names = raw_blocks.keys()
+    ss_block_names = [name for name in block_names if name in STEADY_STATE_NAMES]
+    n_ss_blocks = len(ss_block_names)
+
+    if n_ss_blocks == 0:
+        return {}
+    if n_ss_blocks > 1:
+        raise MultipleSteadyStateBlocksException(ss_block_names)
+
+    ss_key = next(iter(ss_block_names))
+    block_content = raw_blocks[ss_key]
+
+    block_dict = parsed_block_to_dict(block_content)
+    block = Block(name="steady_state", block_dict=block_dict, assumptions=assumptions)
+
+    sub_dict = SymbolDictionary()
+    steady_state_dict = SymbolDictionary()
+
+    if block.definitions is not None:
+        _, definitions = unpack_keys_and_values(block.definitions)
+        sub_dict = SymbolDictionary({eq.lhs: eq.rhs for eq in definitions})
+
+    if block.identities is not None:
+        _, identities = unpack_keys_and_values(block.identities)
+        for eq in identities:
+            subbed_rhs = substitute_repeatedly(eq.rhs, sub_dict)
+            steady_state_dict[eq.lhs] = subbed_rhs
+            sub_dict[eq.lhs] = subbed_rhs
+
+    for k, eq in steady_state_dict.items():
+        steady_state_dict[k] = substitute_repeatedly(eq, steady_state_dict)
+
+    provided_ss_equations = steady_state_dict.sort_keys().to_string().values_to_float()
+
+    del raw_blocks[ss_key]
+
+    return provided_ss_equations
+
+
+def simplify_provided_ss_equations(
+    ss_solution_dict: SymbolDictionary, variables: list[TimeAwareSymbol]
+) -> SymbolDictionary:
+    if not ss_solution_dict:
+        return SymbolDictionary()
+
+    ss_variables = [x.to_ss() for x in variables]
+    extra_equations = SymbolDictionary(
+        {k: v for k, v in ss_solution_dict.to_sympy().items() if k not in ss_variables}
+    )
+    if not extra_equations:
+        return ss_solution_dict
+
+    simplified_ss_dict = SymbolDictionary(
+        {k: v for k, v in ss_solution_dict.to_sympy().items() if k in ss_variables}
+    )
+    for var, eq in simplified_ss_dict.items():
+        if not hasattr(eq, "subs"):
+            continue
+        simplified_ss_dict[var] = substitute_repeatedly(eq, extra_equations)
+
+    return simplified_ss_dict
+
+
+def block_dict_to_equation_list(block_dict: dict[str, Block]) -> list[sp.Expr]:
+    equations = []
+    block_names, blocks = unpack_keys_and_values(block_dict)
+    for block in blocks:
+        equations.extend(block.system_equations)
+
+    return equations
+
+
+def block_dict_to_sub_dict(
+    block_dict: dict[str, Block],
+) -> dict[TimeAwareSymbol, sp.Expr]:
+    sub_dict = {}
+    block_names, blocks = unpack_keys_and_values(block_dict)
+    for block in blocks:
+        for group in ["identities", "objective", "constraints"]:
+            if getattr(block, group) is not None:
+                _, equations = unpack_keys_and_values(getattr(block, group))
+                for eq in equations:
+                    sub_dict[eq.lhs] = eq.rhs
+
+    return sub_dict
+
+
+def block_dict_to_param_dict(
+    block_dict: dict[str, Block], dict_name: PARAM_DICTS = "param_dict"
+) -> SymbolDictionary:
+    param_dict = SymbolDictionary()
+    block_names, blocks = unpack_keys_and_values(block_dict)
+    duplicates = set()
+
+    for block in blocks:
+        current_keys = set(param_dict.keys())
+        new_keys = set(getattr(block, dict_name).keys())
+
+        new_duplicates = current_keys.intersection(new_keys)
+        duplicates = duplicates.union(new_duplicates)
+        param_dict = param_dict | getattr(block, dict_name)
+
+    if len(duplicates) > 0:
+        raise DuplicateParameterError(duplicates)
+
+    return param_dict.sort_keys().to_string().values_to_float()
+
+
+def block_dict_to_variables_and_shocks(
+    block_dict: dict[str, Block],
+) -> tuple[list[TimeAwareSymbol], list[TimeAwareSymbol]]:
+    variables = []
+    shocks = []
+    block_names, blocks = unpack_keys_and_values(block_dict)
+    for block in blocks:
+        if block.variables is not None:
+            variables.extend(block.variables)
+        if block.shocks is not None:
+            shocks.extend(block.shocks)
+
+    # Sort variables and shocks alphabetically by name, and set all time indices to 0
+    shocks = sorted(list({x.set_t(0) for x in shocks}), key=lambda x: x.name)
+    variables = sorted(
+        list({x.set_t(0) for x in variables if x.set_t(0) not in shocks}),
+        key=lambda x: x.name,
+    )
+    return variables, shocks
+
+
+def prior_info_to_prior_dict(
+    prior_info: dict[str, str],
+    assumptions: dict[str, dict[str, bool]],
+    param_dict: SymbolDictionary,
+    backend: Literal["scipy", "pymc"] = "scipy",
+) -> tuple[SymbolDictionary, SymbolDictionary, SymbolDictionary]:
+    """
+    Parse prior information extracted from GCN file and return dictionaries of parameter and shock priors.
+
+    Parameters
+    ----------
+    prior_info: dict[str, str]
+        Dictionary mapping shock and parameter names to priors. The priors are strings that can be parsed by the
+        `parse_distributions` module.
+    assumptions: dict[str, dict[str, bool]]
+        Dictionary of assumptions about model parameters, with keys corresponding to variable names and values
+        corresponding to dictionaries of assumptions about the variable. See sympy documentation for more details.
+    param_dict: SymbolDictionary
+        Dictionary of model parameters.
+    backend: Literal["scipy", "pymc"]
+        The backend into which the priors should be parsed.
+
+    Returns
+    -------
+    param_priors: SymbolDictionary
+        Dictionary of parameter priors
+    shock_priors: SymbolDictionary
+        Dictionary of shock priors
+    hyper_priors_final: SymbolDictionary
+        Dictionary of hyperparameter priors
+    """
+    priors, hyper_priors = create_prior_distribution_dictionary(prior_info)
+    hyper_parameters = set(prior_info.keys()) - set(priors.keys())
+
+    # Remove hyperparameters from the free parameters
+    for parameter in hyper_parameters:
+        del param_dict[parameter]
+
+    param_priors = SymbolDictionary()
+    shock_priors = SymbolDictionary()
+    hyper_priors_final = SymbolDictionary()
+
+    for key, value in priors.items():
+        sympy_key = single_symbol_to_sympy(key, assumptions=assumptions)
+        if isinstance(sympy_key, TimeAwareSymbol):
+            shock_priors[sympy_key.base_name] = value
+        else:
+            param_priors[sympy_key.name] = value
+
+    for key, value in hyper_priors.items():
+        parent_rv, param_type, dist = value
+        parent_key = single_symbol_to_sympy(parent_rv, assumptions=assumptions)
+        param_key = single_symbol_to_sympy(key, assumptions=assumptions)
+
+        hyper_priors_final[param_key] = (parent_key, param_type, dist)
+
+    return param_priors, shock_priors, hyper_priors_final
+
+
+def parsed_model_to_data(
+    parsed_model: str, simplify_blocks: bool
+) -> tuple[
+    dict[str, Block], ASSUMPTION_DICT, dict[str, str], list[str], dict[str, sp.Expr]
+]:
+    """
+    Builds blocks of the gEconpy model using strings parsed from the GCN file.
+
+    Parameters
+    ----------
+    parsed_model: str
+        The GCN model as a string.
+    simplify_blocks : bool
+        Whether to try to simplify equations or not.
+
+    Returns
+    -------
+    blocks: dict[str, Block]
+        Dictionary of block names and block objects.
+    assumptions: dict[str, dict[str, bool]]
+        Dictionary of Sympy assumptions about model variables and parameters. Default is that variables are real, with
+        unknown sign. See Sympy documentation for more details.
+    options: dict[str, str]
+        Dictionary of model options.
+    tryreduce: list[str]
+        List of variables to try to eliminate from model equations via substitution.
+    provided_ss_equations: dict[str, sp.Expr]
+        Dictionary of user-provided steady-state equations. Keys are variable names, and values should be expressions
+        giving the steady-state value of the variable in terms of parameters only.
+    """
+
+    block_dict: dict[str, Block] = {}
+    raw_blocks, options, tryreduce, assumptions = split_gcn_into_dictionaries(
+        parsed_model
+    )
+    provided_ss_equations = get_provided_ss_equations(raw_blocks, assumptions)
+
+    for block_name, block_content in raw_blocks.items():
+        parsed_block_dict = parsed_block_to_dict(block_content)
+        block = Block(
+            name=block_name, block_dict=parsed_block_dict, assumptions=assumptions
+        )
+        block.solve_optimization(try_simplify=simplify_blocks)
+        block_dict[block.name] = block
+
+    return block_dict, assumptions, options, tryreduce, provided_ss_equations
+
+
+def gcn_to_block_dict(
+    gcn_path: str, simplify_blocks: bool
+) -> tuple[
+    dict[str, Block],
+    ASSUMPTION_DICT,
+    dict[str, str],
+    list[TimeAwareSymbol],
+    dict[str, sp.Expr],
+    dict[str, str],
+]:
+    raw_model = load_gcn(gcn_path)
+    parsed_model, prior_dict = preprocess_gcn(raw_model)
+    block_dict, assumptions, options, tryreduce, ss_solution_dict = (
+        parsed_model_to_data(parsed_model, simplify_blocks)
+    )
+
+    tryreduce = [single_symbol_to_sympy(x, assumptions) for x in tryreduce]
+
+    return block_dict, assumptions, options, tryreduce, ss_solution_dict, prior_dict
+
+
+def check_for_orphan_params(
+    equations: list[sp.Expr], param_dict: SymbolDictionary
+) -> None:
+    parameters = list(param_dict.to_sympy().keys())
+    param_equations = [x for x in param_dict.values() if isinstance(x, sp.Expr)]
+
+    orphans = [
+        atom
+        for eq in equations
+        for atom in eq.atoms()
+        if (
+            isinstance(atom, sp.Symbol)
+            and not isinstance(atom, TimeAwareSymbol)
+            and atom not in parameters
+            and not any(eq.has(atom) for eq in param_equations)
+        )
+    ]
+
+    if len(orphans) > 0:
+        raise OrphanParameterError(orphans)
+
+
+def check_for_extra_params(
+    equations: list[sp.Expr], param_dict: SymbolDictionary, on_unused_parameters="raise"
+):
+    parameters = list(param_dict.to_sympy().keys())
+    param_equations = [x for x in param_dict.values() if isinstance(x, sp.Expr)]
+
+    all_atoms = {atom for eq in equations + param_equations for atom in eq.atoms()}
+    extras = [parameter for parameter in parameters if parameter not in all_atoms]
+
+    if len(extras) > 0:
+        if on_unused_parameters == "raise":
+            raise ExtraParameterError(extras)
+        elif on_unused_parameters == "warn":
+            warn(ExtraParameterWarning(extras))
+        else:
+            return
+
+
+def apply_simplifications(
+    try_reduce_vars: list[TimeAwareSymbol],
+    equations: list[sp.Expr],
+    variables: list[TimeAwareSymbol],
+    tryreduce_sub_dict: dict[TimeAwareSymbol, sp.Expr] | None = None,
+    do_simplify_tryreduce: bool = True,
+    do_simplify_constants: bool = True,
+) -> tuple[
+    list[sp.Expr],
+    list[TimeAwareSymbol],
+    list[TimeAwareSymbol] | None,
+    list[TimeAwareSymbol] | None,
+]:
+    eliminated_variables = None
+    singletons = None
+
+    if do_simplify_tryreduce:
+        equations, variables, eliminated_variables = simplify_tryreduce(
+            try_reduce_vars, equations, variables, tryreduce_sub_dict
+        )
+
+    if do_simplify_constants:
+        equations, variables, singletons = simplify_constants(equations, variables)
+
+    return equations, variables, eliminated_variables, singletons
+
+
+def validate_results(
+    equations,
+    steady_state_relationships,
+    param_dict,
+    calib_dict,
+    deterministic_dict,
+    on_unused_parameters="raise",
+):
+    joint_dict = param_dict | calib_dict | deterministic_dict
+    check_for_orphan_params(equations + steady_state_relationships, joint_dict)
+    check_for_extra_params(
+        equations + steady_state_relationships, joint_dict, on_unused_parameters
+    )
+
+
+def block_dict_to_model_primitives(
+    block_dict: dict[str, Block],
+    assumptions: ASSUMPTION_DICT,
+    try_reduce_vars: list[TimeAwareSymbol],
+    prior_info: dict[str, str],
+    simplify_tryreduce: bool = True,
+    simplify_constants: bool = True,
+):
+    equations = block_dict_to_equation_list(block_dict)
+    param_dict = block_dict_to_param_dict(block_dict, "param_dict")
+    calib_dict = block_dict_to_param_dict(block_dict, "calib_dict")
+    deterministic_dict = block_dict_to_param_dict(block_dict, "deterministic_dict")
+    variables, shocks = block_dict_to_variables_and_shocks(block_dict)
+    param_priors, shock_priors, hyper_priors_final = prior_info_to_prior_dict(
+        prior_info, assumptions, param_dict
+    )
+
+    tryreduce_sub_dict = block_dict_to_sub_dict(block_dict)
+
+    equations, variables, eliminated_variables, singletons = apply_simplifications(
+        try_reduce_vars,
+        equations,
+        variables,
+        tryreduce_sub_dict,
+        do_simplify_tryreduce=simplify_tryreduce,
+        do_simplify_constants=simplify_constants,
+    )
+
+    return (
+        equations,
+        param_dict,
+        calib_dict,
+        deterministic_dict,
+        variables,
+        shocks,
+        param_priors,
+        shock_priors,
+        hyper_priors_final,
+        eliminated_variables,
+        singletons,
+    )
+
+
+def build_report(
+    equations: list[sp.Expr],
+    param_dict: SymbolDictionary,
+    calib_dict: SymbolDictionary,
+    variables: list[TimeAwareSymbol],
+    shocks: list[TimeAwareSymbol],
+    param_priors: SymbolDictionary,
+    shock_priors: SymbolDictionary,
+    reduced_vars: list[TimeAwareSymbol],
+    reduced_params: list[sp.Symbol],
+    singletons: list[TimeAwareSymbol],
+) -> None:
+    """
+    Write a diagnostic message after building the model. Note that successfully building the model does not
+    guarantee that the model is correctly specified. For example, it is possible to build a model with more
+    equations than parameters. This message will warn the user in this case.
+
+    Parameters
+    ----------
+    equations: list[sp.Expr]
+
+    param_dict: SymbolDictionary
+
+    calib_dict: SymbolDictionary
+
+    variables: list[TimeAwareSymbol]
+
+    shocks: list[TimeAwareSymbol]
+
+    param_priors: SymbolDictionary
+
+    shock_priors: SymbolDictionary
+
+    reduced_vars: list[TimeAwareSymbol]
+        A list of variables reduced by the `try_reduce` method. Used to print the names of eliminated variables.
+
+    reduced_params: list of Symbol
+        A list of "deterministic" parameters eliminated via substitution. These are parameters that are only used
+        in the definiton of other parameters.
+
+    singletons: list[TimeAwareSymbol]
+        A list of "singleton" variables -- those defined as time-invariant constants. Used ot print the sames of
+        eliminated variables.
+
+    verbose: bool
+        Flag to print the build report to the terminal. Default is True. Regardless of the flag, the function will
+        always issue a warning to the user if the system is not fully defined.
+
+    Returns
+    -------
+    None
+    """
+
+    n_equations = len(equations)
+    n_variables = len(variables)
+    n_shocks = len(shocks)
+    n_params_to_calibrate = len(calib_dict)
+    n_free_params = len(param_dict)
+
+    if singletons and len(singletons) == 0:
+        singletons = None
+
+    eq_str = "equation" if n_equations == 1 else "equations"
+    var_str = "variable" if n_variables == 1 else "variables"
+    shock_str = "shock" if n_shocks == 1 else "shocks"
+    free_par_str = "parameter" if len(param_dict) == 1 else "parameters"
+    calib_par_str = "parameter" if n_params_to_calibrate == 1 else "parameters"
+
+    n_params = n_free_params + n_params_to_calibrate
+
+    param_priors = param_priors.keys()
+    shock_priors = shock_priors.keys()
+
+    report = "Model Building Complete.\nFound:\n"
+    report += f"\t{n_equations} {eq_str}\n"
+    report += f"\t{n_variables} {var_str}\n"
+
+    if reduced_vars:
+        report += "\t\tThe following variables were eliminated at user request:\n"
+        report += "\t\t\t" + ", ".join([x.name for x in reduced_vars]) + "\n"
+
+    if singletons:
+        report += '\t\tThe following "variables" were defined as constants and have been substituted away:\n'
+        report += "\t\t\t" + ", ".join([x.name for x in singletons]) + "\n"
+
+    report += f"\t{n_shocks} stochastic {shock_str}\n"
+    report += (
+        f'\t\t {len(shock_priors)} / {n_shocks} {"have" if len(shock_priors) == 1 else "has"}'
+        f" a defined prior. \n"
+    )
+
+    report += f"\t{n_params} {free_par_str}\n"
+    if reduced_params:
+        report += "\t\tThe following parameters were eliminated via substitution into other parameters:\n"
+        report += "\t\t\t" + ", ".join([x.name for x in reduced_params]) + "\n"
+
+    report += (
+        f'\t\t {len(param_priors)} / {n_params} parameters {"have" if len(param_priors) == 1 else "has"} '
+        f"a defined prior. \n"
+    )
+
+    report += f"\t{n_params_to_calibrate} {calib_par_str} to calibrate.\n"
+
+    if n_equations == n_variables:
+        report += "Model appears well defined and ready to proceed to solving.\n"
+    else:
+        message = (
+            f"The model does not appear correctly specified, there are {n_equations} {eq_str} but "
+            f"{n_variables} {var_str}. It will not be possible to solve this model. Please check the "
+            f"specification using available diagnostic tools, and check the GCN file for typos."
+        )
+        warn(message)
+
+    _log.info(report)
diff --git a/gEconpy/parser/gEcon_parser.py b/gEconpy/parser/gEcon_parser.py
index 9e38a7d..f947313 100644
--- a/gEconpy/parser/gEcon_parser.py
+++ b/gEconpy/parser/gEcon_parser.py
@@ -1,13 +1,15 @@
 import re
+
 from collections import defaultdict
+from typing import Literal, cast
 
 import pyparsing as pp
+
 from sympy.core.assumptions import _assume_rules
 
-from gEconpy.exceptions.exceptions import GCNSyntaxError
+from gEconpy.exceptions import GCNSyntaxError
 from gEconpy.parser.constants import (
     DEFAULT_ASSUMPTIONS,
-    SPECIAL_BLOCK_NAMES,
     SYMPY_ASSUMPTIONS,
 )
 from gEconpy.parser.parse_equations import rebuild_eqs_from_parser_output
@@ -24,7 +26,15 @@
     find_typos_and_guesses,
     validate_key,
 )
-from gEconpy.shared.utilities import flatten_list
+from gEconpy.utilities import flatten_list
+
+SPECIAL_BLOCK = Literal["tryreduce", "assumptions", "options"]
+ASSUMPTION_DICT = dict[str, dict[str, bool]]
+SPECIAL_BLOCK_DEFAULT = {
+    "tryreduce": [],
+    "assumptions": defaultdict(lambda: DEFAULT_ASSUMPTIONS),
+    "options": {},
+}
 
 
 def block_to_clean_list(block: str) -> list[str]:
@@ -38,7 +48,7 @@ def block_to_clean_list(block: str) -> list[str]:
 
     Returns
     -------
-    List[str]
+    block: list of str
         The processed list of strings.
     """
 
@@ -56,12 +66,12 @@ def extract_assumption_sub_blocks(block_str) -> dict[str, list[str]]:
 
     Parameters
     ----------
-    block : List[str]
+    block: list of str
         The block of text to process.
 
     Returns
     -------
-    Dict[str, List[str]]
+    assumptions, dict
         A dictionary containing assumptions and variables, with the assumption names as keys and associated variables
         as values.
     """
@@ -126,13 +136,13 @@ def create_assumption_kwargs(
 
     Parameters
     ----------
-    assumption_dicts : Dict[str, List[str]]
+    assumption_dicts: dict
         A dictionary containing assumptions and variables, with the assumption names as keys and associated variables
         as values.
 
     Returns
     -------
-    Dict[str, Dict[str, bool]]
+    assumptions: dict
         A dictionary of flags and values keyed by variable names.
     """
 
@@ -180,8 +190,11 @@ def preprocess_gcn(gcn_raw: str) -> tuple[str, dict[str, str]]:
 
     Returns
     -------
-    Tuple[str, Dict[str, str]]
-        Model file with basic preprocessing and prior distributions, respectively.
+    gcn_processed: str
+        Model file with basic preprocessing applied
+
+    prior_dict: dict
+        Dictionary of variables and associated prior distributions
     """
 
     gcn_processed = remove_comments(gcn_raw)
@@ -203,7 +216,7 @@ def parse_options_flags(options: str) -> dict[str, bool] | None:
 
     Returns
     -------
-    Optional[Dict[str, bool]]
+    Optional[dict[str, bool]]
         A dictionary of flags and values if they exist, or None if no options were found.
 
     Notes
@@ -258,13 +271,13 @@ def extract_special_block(text: str, block_name: str) -> dict[str, list[str]]:
     }
 
     if block_name not in text:
-        return result
+        return result[block_name]
 
     block = re.search(block_name + " {.*?" + "};", text)[0]
     block = block.replace(block_name, "")
 
     if block_is_empty(block):
-        return result
+        return result[block_name]
 
     elif block_name == "options":
         block = parse_options_flags(block)
@@ -277,14 +290,50 @@ def extract_special_block(text: str, block_name: str) -> dict[str, list[str]]:
         validate_assumptions(block)
         block = create_assumption_kwargs(block)
 
-    result[block_name] = block
+    return block
 
-    return result
 
+def process_special_block_text(
+    text: str, name: SPECIAL_BLOCK
+) -> tuple[str, dict | list]:
+    """
+    Extract special blocks from a preprocessed GCN text string. Modifies the GCN text string in-place by deleting
+    the special block.
 
-def split_gcn_into_block_dictionary(text: str) -> dict[str, str]:
+    Parameters
+    ----------
+    text: str
+        Preprocessed GCN string
+    name: str
+        Name of special block. One of "tryreduce", "assumptions", "options"
+
+    Returns
+    -------
+    text: str
+        Preprocessed GCN file, with special block text removed
+
+    result: list or dict
+        Special block data. "tryreduce" returns a list, otherwise a dictionary
+    """
+    name = name.lower()
+    result = extract_special_block(text, name)
+    text = delete_block(text, name)
+
+    if result is None:
+        result = SPECIAL_BLOCK_DEFAULT[name]
+
+    return text, result
+
+
+def split_gcn_into_dictionaries(
+    text: str,
+) -> tuple[dict[str, str], dict[str, str], list[str], ASSUMPTION_DICT]:
     """
-    Split the preprocessed GCN text by block and stores the results in a dictionary.
+    Split the preprocessed GCN text by blocks.
+
+    Currently there are three special blocks: "options", "tryreduce", and "assumptions". These are extracted from
+    the text and removed from the main text block. The remaining blocks are organized into a dictionary with the
+    block name as the key and the (raw) block text as the value.
 
     Parameters
     ----------
@@ -294,31 +343,41 @@ def split_gcn_into_block_dictionary(text: str) -> dict[str, str]:
 
     Returns
     -------
-    Dict[str, str]
+    block_dict: dict[str, str]
         A "block dictionary" with key, value pairs of block_name:block_text. Special blocks are processed first
         (currently "options" and "tryreduce"), then deleted. Normal model blocks are assumed to follow a standard format
         of block NAME { component_1 { Equations }; component_2 { ... }; };
 
-    TODO: Add checks that model blocks follow the correct format and fail more helpfully.
+    options: dict[str, str]
+        A dictionary of flags and values from the "options" block.
+
+    tryreduce: list[str]
+        A list of variables to attempt to reduce.
+
+    assumptions: dict[str, dict[str, bool]]
+        Dictionary of assumption flags for each variable in the model. Keys are variable names, values are dictionaries
+        of assumption flags and values. If no assumptions are provided, the default assumptions are used. For more
+        details, see the Sympy documentation.
     """
-    results = dict()
 
-    for name in SPECIAL_BLOCK_NAMES:
-        name = name.lower()
-        result = extract_special_block(text, name)
-        results.update(result)
-        text = delete_block(text, name)
+    # TODO: Add checks that model blocks follow the correct format and fail more helpfully.
+
+    block_dict = dict()
+    text, tryreduce = process_special_block_text(text, "tryreduce")
+    text, options = process_special_block_text(text, "options")
+    text, assumptions = process_special_block_text(text, "assumptions")
 
-    if "assumptions" not in results:
-        results["assumptions"] = defaultdict(lambda x: DEFAULT_ASSUMPTIONS)
+    assumptions = cast(ASSUMPTION_DICT, assumptions)
+    tryreduce = cast(list[str], tryreduce)
+    options = cast(dict[str, str], options)
 
     gcn_blocks = [block for block in text.split("block") if len(block) > 0]
     for block in gcn_blocks:
         tokens = block.strip().split()
-        name = tokens[0]
-        results[name] = " ".join(tokens[1:])
+        name = tokens.pop(0)
+        block_dict[name] = " ".join(tokens)
 
-    return results
+    return block_dict, options, tryreduce, assumptions
 
 
 def parsed_block_to_dict(block: str) -> dict[str, list[list[str]]]:
@@ -332,18 +391,22 @@ def parsed_block_to_dict(block: str) -> dict[str, list[list[str]]]:
 
     Returns
     -------
-    Dict[str, List[List[str]]]
-        A defaultdict of lists, containing lists of equation tokens. Keys are the block components found
+    block_dict: dict[str, list[list[str]]]
+        A dict of lists, containing lists of equation tokens. Keys are the block components found
         in the block string. Equations are represented as lists of tokens, while sub-blocks are lists of equation lists.
 
-    Example:
+    Example
+    -------
+
+    .. code::python
+
     >> Input: {definition { u[] = log ( C[] ) + log( L[] ); }; objective { U[] = u[] + beta * E[][U[1]] ;} };
     >> Output: dict("definition" = ["u[]", "=", "log", "(", "C[]", ")", "+", "log", "(", "L[]", ")", ";"],
                     "objective" = ["U[]", "=", "u[]", "+", "beta", "*", "E[][U[1]]", ";"])
     """
     block_dict = defaultdict(list)
-    parsed_block = pp.nestedExpr("{", "};").parseString(block).asList()[0]
-    current_key = parsed_block[0]
+    parsed_block = next(iter(pp.nestedExpr("{", "};").parseString(block).asList()))
+    current_key = parsed_block.pop(0)
 
     if isinstance(current_key, list):
         # block[0] is an equation, should not be possible
@@ -351,7 +414,7 @@ def parsed_block_to_dict(block: str) -> dict[str, list[list[str]]]:
 
     validate_key(key=current_key, block_name=block)
 
-    for element in parsed_block[1:]:
+    for element in parsed_block:
         if isinstance(element, str):
             current_key = element
             validate_key(key=current_key, block_name=block)
diff --git a/gEconpy/parser/parse_distributions.py b/gEconpy/parser/parse_distributions.py
index e628de0..38c7bab 100644
--- a/gEconpy/parser/parse_distributions.py
+++ b/gEconpy/parser/parse_distributions.py
@@ -1,14 +1,16 @@
-import re
 from abc import ABC, abstractmethod
-from functools import partial, reduce
-from typing import Any
 from collections.abc import Callable
+from functools import partial, reduce
+from typing import Any, Literal
 from warnings import warn
 
 import numpy as np
+
+from pyparsing import ParseException
 from scipy import optimize
 from scipy.stats import (
     beta,
+    expon,
     gamma,
     halfnorm,
     invgamma,
@@ -20,7 +22,7 @@
 from scipy.stats._distn_infrastructure import rv_frozen
 
 from gEconpy.classes.containers import SymbolDictionary
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     DistributionOverDefinedException,
     IgnoredCloseMatchWarning,
     InsufficientDegreesOfFreedomException,
@@ -30,8 +32,9 @@
     RepeatedParameterException,
     UnusedParameterWarning,
 )
+from gEconpy.parser.dist_syntax import dist_syntax, evaluate_expression
 from gEconpy.parser.validation import find_typos_and_guesses
-from gEconpy.shared.utilities import is_number
+from gEconpy.utilities import is_number
 
 CANON_NAMES = [
     "normal",
@@ -41,7 +44,9 @@
     "inv_gamma",
     "uniform",
     "beta",
+    "exponential",
 ]
+
 NAME_TO_DIST_SCIPY_FUNC = dict(
     zip(CANON_NAMES, [norm, truncnorm, halfnorm, gamma, invgamma, uniform, beta])
 )
@@ -63,6 +68,7 @@
 ]
 UNIFORM_ALIASES = ["u", "uniform", "uni", "unif"]
 BETA_ALIASES = ["beta", "b"]
+EXPONENTIAL_ALIASES = ["expon", "exponential"]
 
 # Moment parameter names
 MEAN_ALIASES = ["mean"]
@@ -84,7 +90,10 @@
 BETA_SHAPE_ALIASES_2 = ["b", "beta"]
 
 GAMMA_SHAPE_ALIASES = ["a", "alpha", "k", "shape"]
-GAMMA_SCALE_ALIASES = ["b", "beta", "theta", "scale"]
+GAMMA_SCALE_ALIASES = ["theta", "scale"]
+GAMMA_RATE_ALIASES = ["b", "beta", "rate"]
+
+EXPONENTIAL_RATE_ALIASES = ["lambda", "rate"]
 
 DIST_ALIAS_LIST = [
     NORMAL_ALIASES,
@@ -94,6 +103,7 @@
     INVERSE_GAMMA_ALIASES,
     UNIFORM_ALIASES,
     BETA_ALIASES,
+    EXPONENTIAL_ALIASES,
 ]
 
 
@@ -102,7 +112,7 @@ def __init__(self, dist, **parameters):
         defined_params = {
             param: value
             for param, value in parameters.items()
-            if isinstance(value, (int, float))
+            if isinstance(value, int | float)
         }
 
         self.rv_params = {
@@ -137,7 +147,7 @@ def _unpack_pdf_dict(self, point_dict):
         }
         assert len(point_dict.keys()) == 1
 
-        point_val = list(point_dict.values())[0]
+        point_val = next(iter(point_dict.values()))
 
         return param_dict, point_val
 
@@ -189,6 +199,7 @@ def __init__(
         loc_param_name: str | None,
         scale_param_name: str,
         shape_param_name: str | None,
+        rate_param_name: str | None,
         upper_bound_param_name: str | None,
         lower_bound_param_name: str | None,
         n_params: int,
@@ -199,6 +210,8 @@ def __init__(
         self.loc_param_name = loc_param_name
         self.scale_param_name = scale_param_name
         self.shape_param_name = shape_param_name
+        self.rate_param_name = rate_param_name
+
         self.upper_bound_param_name = upper_bound_param_name
         self.lower_bound_param_name = lower_bound_param_name
         self.n_params = n_params
@@ -207,6 +220,7 @@ def __init__(
         self.used_parameters = []
         self.mean_constraint = None
         self.std_constraint = None
+        self.initial_value = None
 
     @abstractmethod
     def build_distribution(self, param_dict: dict[str, str]) -> rv_continuous:
@@ -347,7 +361,7 @@ def _parse_parameter_candidates(
                 self.variable_name, self.d_name, canon_param_name, candidates
             )
 
-        return list(candidates)[0]
+        return next(iter(candidates))
 
     def _warn_about_unused_parameters(self, param_dict: dict[str, str]) -> None:
         used_parameters = self.used_parameters
@@ -381,6 +395,7 @@ def __init__(self, variable_name: str):
             loc_param_name="loc",
             scale_param_name="scale",
             shape_param_name=None,
+            rate_param_name=None,
             lower_bound_param_name="a",
             upper_bound_param_name="b",
             n_params=2,
@@ -388,14 +403,19 @@ def __init__(self, variable_name: str):
         )
 
     def build_distribution(
-        self, param_dict: dict[str, str], package="scipy", model=None
+        self,
+        param_dict: dict[str, str],
+        backend="scipy",
+        model=None,
+        initial_value=None,
     ) -> rv_continuous:
         parsed_param_dict = self._parse_parameters(param_dict)
 
         self._warn_about_unused_parameters(param_dict)
         self._verify_distribution_parameterization(parsed_param_dict)
+        self.initial_value = initial_value
 
-        if package == "scipy":
+        if backend == "scipy":
             parsed_param_dict = self._postprocess_parameters(parsed_param_dict)
             parameters = list(parsed_param_dict.keys())
 
@@ -509,8 +529,9 @@ def moment_errors(x, target_mean, target_std, a, b):
                 )
 
                 if not result.success and result.fun > 1e-5:
-                    print(result)
-                    raise ValueError
+                    raise ValueError(
+                        f"Could not optimize {self.d_name}: {result.message}"
+                    )
 
                 loc, scale = result.x
                 param_dict[self.loc_param_name] = loc
@@ -582,6 +603,7 @@ def __init__(self, variable_name: str):
             loc_param_name="loc",
             scale_param_name="scale",
             shape_param_name=None,
+            rate_param_name=None,
             upper_bound_param_name=None,
             lower_bound_param_name=None,
             n_params=2,
@@ -589,13 +611,18 @@ def __init__(self, variable_name: str):
         )
 
     def build_distribution(
-        self, param_dict: dict[str, str], package="scipy", model=None
+        self,
+        param_dict: dict[str, str],
+        backend="scipy",
+        model=None,
+        initial_value=None,
     ) -> rv_continuous:
         parsed_param_dict = self._parse_parameters(param_dict)
         self._warn_about_unused_parameters(param_dict)
         self._verify_distribution_parameterization(parsed_param_dict)
+        self.initial_value = initial_value
 
-        if package == "scipy":
+        if backend == "scipy":
             parsed_param_dict = self._postprocess_parameters(parsed_param_dict)
 
             return halfnorm(**parsed_param_dict)
@@ -660,23 +687,32 @@ def __init__(self, variable_name: str):
             loc_param_name="loc",
             scale_param_name="scale",
             shape_param_name=None,
+            rate_param_name=None,
             lower_bound_param_name="a",
             upper_bound_param_name="b",
             n_params=2,
-            all_valid_parameters=["loc", "scale"]
-            + LOWER_BOUND_ALIASES
-            + UPPER_BOUND_ALIASES
-            + MOMENTS,
+            all_valid_parameters=[
+                "loc",
+                "scale",
+                *LOWER_BOUND_ALIASES,
+                *UPPER_BOUND_ALIASES,
+                *MOMENTS,
+            ],
         )
 
     def build_distribution(
-        self, param_dict: dict[str, str], package="scipy", model=None
+        self,
+        param_dict: dict[str, str],
+        backend="scipy",
+        model=None,
+        initial_value=None,
     ) -> rv_continuous:
         parsed_param_dict = self._parse_parameters(param_dict)
         self._warn_about_unused_parameters(param_dict)
         self._verify_distribution_parameterization(parsed_param_dict)
+        self.initial_value = initial_value
 
-        if package == "scipy":
+        if backend == "scipy":
             parsed_param_dict = self._postprocess_parameters(parsed_param_dict)
 
             return uniform(**parsed_param_dict)
@@ -782,6 +818,7 @@ def __init__(self, variable_name: str):
             loc_param_name="loc",
             scale_param_name="scale",
             shape_param_name="a",
+            rate_param_name=None,
             upper_bound_param_name=None,
             lower_bound_param_name=None,
             n_params=3,
@@ -791,13 +828,18 @@ def __init__(self, variable_name: str):
         )
 
     def build_distribution(
-        self, param_dict: dict[str, str], package="scipy", model=None
+        self,
+        param_dict: dict[str, str],
+        backend="scipy",
+        model=None,
+        initial_value=None,
     ) -> rv_continuous:
         parsed_param_dict = self._parse_parameters(param_dict)
         self._warn_about_unused_parameters(param_dict)
         self._verify_distribution_parameterization(parsed_param_dict)
+        self.initial_value = initial_value
 
-        if package == "scipy":
+        if backend == "scipy":
             parsed_param_dict = self._postprocess_parameters(parsed_param_dict)
             return invgamma(**parsed_param_dict)
 
@@ -891,6 +933,7 @@ def __init__(self, variable_name: str):
             loc_param_name="loc",
             scale_param_name="scale",
             shape_param_name=None,
+            rate_param_name=None,
             upper_bound_param_name=None,
             lower_bound_param_name=None,
             n_params=2,
@@ -904,13 +947,18 @@ def __init__(self, variable_name: str):
         self.shape_param_name_2 = "b"
 
     def build_distribution(
-        self, param_dict: dict[str, str], package="scipy", model=None
+        self,
+        param_dict: dict[str, str],
+        backend="scipy",
+        model=None,
+        initial_value=None,
     ) -> rv_continuous:
         parsed_param_dict = self._parse_parameters(param_dict)
         self._warn_about_unused_parameters(param_dict)
         self._verify_distribution_parameterization(parsed_param_dict)
+        self.initial_value = initial_value
 
-        if package == "scipy":
+        if backend == "scipy":
             parsed_param_dict = self._postprocess_parameters(parsed_param_dict)
             return beta(**parsed_param_dict)
 
@@ -967,13 +1015,17 @@ def _postprocess_parameters(self, param_dict: dict[str, float]) -> dict[str, flo
                 )
 
             if std <= 0:
-                used_name = list(set(used_parameters).intersection(set(STD_ALIASES)))[0]
+                used_name = next(
+                    iter(set(used_parameters).intersection(set(STD_ALIASES)))
+                )
                 raise InvalidParameterException(
                     self.variable_name, self.d_name, "mean", used_name, "sd > 0"
                 )
 
             if ((1 - mean) ** 2 * mean) < (std**2):
-                used_name = list(set(used_parameters).intersection(set(STD_ALIASES)))[0]
+                used_name = next(
+                    iter(set(used_parameters).intersection(set(STD_ALIASES)))
+                )
                 raise InvalidParameterException(
                     self.variable_name,
                     self.d_name,
@@ -1044,23 +1096,30 @@ def __init__(self, variable_name: str):
             loc_param_name="loc",
             scale_param_name="scale",
             shape_param_name="a",
+            rate_param_name="b",
             lower_bound_param_name=None,
             upper_bound_param_name=None,
             n_params=3,
             all_valid_parameters=GAMMA_SHAPE_ALIASES
             + GAMMA_SCALE_ALIASES
+            + GAMMA_RATE_ALIASES
             + MOMENTS
             + ["loc"],
         )
 
     def build_distribution(
-        self, param_dict: dict[str, str], package="scipy", model=None
+        self,
+        param_dict: dict[str, str],
+        backend="scipy",
+        model=None,
+        initial_value=None,
     ) -> rv_continuous:
         parsed_param_dict = self._parse_parameters(param_dict)
         self._warn_about_unused_parameters(param_dict)
         self._verify_distribution_parameterization(parsed_param_dict)
+        self.initial_value = initial_value
 
-        if package == "scipy":
+        if backend == "scipy":
             parsed_param_dict = self._postprocess_parameters(parsed_param_dict)
             return gamma(**parsed_param_dict)
 
@@ -1075,6 +1134,11 @@ def _parse_parameters(self, param_dict: dict[str, str]) -> dict[str, float]:
             canon_name=self.scale_param_name,
             aliases=GAMMA_SCALE_ALIASES,
         )
+        parse_rate_parameter = partial(
+            self._parse_parameter,
+            canon_name=self.rate_param_name,
+            aliases=GAMMA_RATE_ALIASES,
+        )
         parse_shape_parameter = partial(
             self._parse_parameter,
             canon_name=self.shape_param_name,
@@ -1087,6 +1151,7 @@ def _parse_parameters(self, param_dict: dict[str, str]) -> dict[str, float]:
             parse_loc_parameter,
             parse_scale_parameter,
             parse_shape_parameter,
+            parse_rate_parameter,
         ]
 
         parsed_param_dict = {}
@@ -1100,6 +1165,20 @@ def _postprocess_parameters(self, param_dict: dict[str, float]) -> dict[str, flo
 
         user_passed_scale = self.scale_param_name in parameters
         user_passed_shape = self.shape_param_name in parameters
+        user_passed_rate = self.rate_param_name in parameters
+
+        if user_passed_scale and user_passed_rate:
+            raise MultipleParameterDefinitionException(
+                self.variable_name,
+                self.d_name,
+                "scale",
+                [self.scale_param_name, self.rate_param_name],
+            )
+
+        elif user_passed_rate:
+            param_dict[self.scale_param_name] = 1 / param_dict[self.rate_param_name]
+            user_passed_scale = True
+            param_dict.pop(self.rate_param_name)
 
         if self._has_mean_constraint() and self._has_std_constraint():
             mean, std = self.mean_constraint, self.std_constraint
@@ -1140,6 +1219,126 @@ def _postprocess_parameters(self, param_dict: dict[str, float]) -> dict[str, flo
         return param_dict
 
 
+class ExponentialDistributionParser(BaseDistributionParser):
+    def __init__(self, variable_name: str):
+        super().__init__(
+            variable_name=variable_name,
+            d_name="exponential",
+            loc_param_name="loc",
+            scale_param_name="scale",
+            shape_param_name=None,
+            rate_param_name="lambda",
+            lower_bound_param_name=None,
+            upper_bound_param_name=None,
+            n_params=2,
+            all_valid_parameters=EXPONENTIAL_RATE_ALIASES + MOMENTS + ["loc", "scale"],
+        )
+
+    def build_distribution(
+        self,
+        param_dict: dict[str, str],
+        backend="scipy",
+        model=None,
+        initial_value=None,
+    ) -> rv_continuous:
+        parsed_param_dict = self._parse_parameters(param_dict)
+        self._warn_about_unused_parameters(param_dict)
+        self._verify_distribution_parameterization(parsed_param_dict)
+        self.initial_value = initial_value
+
+        if backend == "scipy":
+            parsed_param_dict = self._postprocess_parameters(parsed_param_dict)
+            return expon(**parsed_param_dict)
+
+    def _parse_parameters(self, param_dict: dict[str, str]) -> dict[str, float]:
+        parse_loc_parameter = partial(
+            self._parse_parameter,
+            canon_name=self.loc_param_name,
+            aliases=[self.loc_param_name],
+        )
+        parse_scale_parameter = partial(
+            self._parse_parameter,
+            canon_name=self.scale_param_name,
+            aliases=["scale"],
+        )
+        parse_rate_parameter = partial(
+            self._parse_parameter,
+            canon_name=self.rate_param_name,
+            aliases=EXPONENTIAL_RATE_ALIASES,
+        )
+
+        parsing_functions = [
+            self._parse_mean_constraint,
+            self._parse_std_constraint,
+            parse_loc_parameter,
+            parse_scale_parameter,
+            parse_rate_parameter,
+        ]
+
+        parsed_param_dict = {}
+        for f in parsing_functions:
+            parsed_param_dict.update(f(param_dict))
+
+        return parsed_param_dict
+
+    def _postprocess_parameters(self, param_dict: dict[str, float]) -> dict[str, float]:
+        parameters = list(param_dict.keys())
+        user_passed_rate = self.rate_param_name in parameters
+        user_passed_scale = self.scale_param_name in parameters
+
+        if user_passed_scale and user_passed_rate:
+            raise MultipleParameterDefinitionException(
+                self.variable_name,
+                self.d_name,
+                "scale",
+                [self.scale_param_name, self.rate_param_name],
+            )
+
+        elif user_passed_rate:
+            param_dict[self.scale_param_name] = 1 / param_dict[self.rate_param_name]
+            user_passed_scale = True
+            param_dict.pop(self.rate_param_name)
+
+        elif user_passed_scale:
+            param_dict[self.rate_param_name] = param_dict[self.scale_param_name]
+
+        if self._has_mean_constraint() and self._has_std_constraint():
+            mean, std = self.mean_constraint, self.std_constraint
+            if mean < 0:
+                raise InvalidParameterException(
+                    self.variable_name, self.d_name, "mean", "mean", "mean >= 0"
+                )
+            if std <= 0:
+                raise InvalidParameterException(
+                    self.variable_name, self.d_name, "std", "std", "std >= 0"
+                )
+
+            param_dict[self.scale_param_name] = 1 / std
+            param_dict[self.loc_param_name] = mean - 1 / std
+
+        elif self._has_mean_constraint():
+            mean = self.mean_constraint
+            if user_passed_scale:
+                param_dict[self.loc_param_name] = (
+                    mean - param_dict[self.scale_param_name]
+                )
+            else:
+                param_dict[self.scale_param_name] = 1 / mean
+
+        elif self._has_std_constraint():
+            std = self.std_constraint
+            if user_passed_scale:
+                raise MultipleParameterDefinitionException(
+                    self.variable_name,
+                    self.d_name,
+                    "scale",
+                    [self.scale_param_name, "std"],
+                )
+            param_dict[self.scale_param_name] = 1 / std
+
+        return param_dict
+
+
 def match_first_two_moments(
     target_mean: float, target_std: float, dist_object: rv_continuous
 ) -> tuple[float, float]:
@@ -1167,8 +1366,7 @@ def moment_errors(
     )
 
     if not result.success and result.fun > 1e-5:
-        print(result)
-        raise ValueError
+        raise ValueError(f"Could not optimize {dist_object}: {result.message}")
 
     loc, scale = result.x
     return loc, scale
@@ -1205,45 +1403,27 @@ def preprocess_distribution_string(
     """
     name_to_canon_dict = build_alias_to_canon_dict(DIST_ALIAS_LIST, CANON_NAMES)
 
-    # digit_pattern = r" ?\d*\.?\d* ?"
-    general_pattern = r" ?[\w\.]* ?"
-
-    # The not last args have a comma, while the last arg does not.
-    dist_name_pattern = r"(\w+)"
-    not_last_arg_pattern = rf"(\w+ ?={general_pattern}, ?)"
-    last_arg_pattern = rf"(\w+ ?={general_pattern})"
-    valid_pattern = (
-        rf"{dist_name_pattern}\({not_last_arg_pattern}*?{last_arg_pattern}\),?$"
-    )
-
-    # TODO: sort out where the typo is and tell the user.
-    if re.search(valid_pattern, d_string) is None:
-        raise InvalidDistributionException(variable_name, d_string)
-
-    d_name, params_string = d_string.split("(")
-    d_name = d_name.lower()
+    try:
+        parsed_result = dist_syntax.parseString(d_string)
+    except ParseException as e:
+        raise InvalidDistributionException(variable_name, d_string) from e
 
+    d_name = parsed_result["dist_name"].strip().lower()
     if d_name not in name_to_canon_dict.keys():
         raise InvalidDistributionException(variable_name, d_string)
 
-    params = [x.strip() for x in params_string.replace(")", "").split(",")]
-    params = [x for x in params if len(x) > 0]
-
-    new_params = []
-    for p in params:
-        chunks = p.split("=")
-        new_p = "=".join([chunks[0].lower(), chunks[1]])
-        new_params.append(new_p)
-
-    params = new_params
-
     param_dict = {}
-    for param in params:
-        key, value = (x.strip() for x in param.split("="))
+
+    for param in parsed_result["kwargs"]:
+        key, value = param[0], param[1]
         if key in param_dict.keys():
             raise RepeatedParameterException(variable_name, d_name, key)
 
-        param_dict[key] = value
+        param_dict[key.strip().lower()] = evaluate_expression(value)
+
+    param_dict["initial_value"] = evaluate_expression(parsed_result["initial_value"])
+    if variable_name.endswith("[]") and param_dict["initial_value"] is not None:
+        raise InvalidDistributionException(variable_name, d_string)
 
     return name_to_canon_dict[d_name], param_dict
 
@@ -1282,7 +1462,7 @@ def distribution_factory(
     variable_name: str,
     d_name: str,
     param_dict: dict[str, str],
-    package: str = "scipy",
+    backend: str = "scipy",
     model=None,
 ) -> rv_continuous:
     """
@@ -1294,8 +1474,8 @@ def distribution_factory(
         plaintext name of the distribution to parameterize, from the CANNON_NAMES list.
     param_dict: dict
         a dictionary of parameter: value pairs, or parameter: string pairs in the case of composite distributions
-    package: str
-        package of the distribution function to parameterize
+    backend: str
+        backend of the distribution function to parameterize
 
     Returns
     -------
@@ -1303,12 +1483,12 @@ def distribution_factory(
         a scipy distribution object object
     """
 
-    if package not in ["scipy"]:
+    if backend not in ["scipy"]:
         raise NotImplementedError
 
     parser = None
 
-    if d_name == "normal":
+    if d_name in ["normal", "truncnorm"]:
         parser = NormalDistributionParser(variable_name=variable_name)
 
     elif d_name == "halfnormal":
@@ -1326,11 +1506,18 @@ def distribution_factory(
     elif d_name == "uniform":
         parser = UniformDistributionParser(variable_name=variable_name)
 
-    if parser is None:
-        print(d_name)
-        raise ValueError("How did you even get here?")
+    elif d_name == "exponential":
+        parser = ExponentialDistributionParser(variable_name=variable_name)
+
+    else:
+        raise NotImplementedError(
+            f'Unknown distribution "{d_name}". Expected one of {CANON_NAMES}'
+        )
 
-    d = parser.build_distribution(param_dict, package=package, model=model)
+    initial_value = param_dict.pop("initial_value", None)
+    d = parser.build_distribution(
+        param_dict, backend=backend, model=model, initial_value=initial_value
+    )
     return d
 
 
@@ -1376,7 +1563,7 @@ def split_out_composite_distributions(
     composite_distributions = {}
 
     for variable_name, d_name, param_dict in zip(variable_names, d_names, param_dicts):
-        if all([is_number(x) for x in param_dict.values()]):
+        if all([is_number(x) or x is None for x in param_dict.values()]):
             basic_distributions[variable_name] = (d_name, param_dict)
         else:
             composite_distributions[variable_name] = (d_name, param_dict)
@@ -1387,7 +1574,7 @@ def split_out_composite_distributions(
 def fetch_rv_params(param_dict, model):
     return_dict = {}
     for k, v in param_dict.items():
-        if isinstance(v, (float, int)):
+        if isinstance(v, float | int):
             return_dict[k] = v
         elif isinstance(v, str):
             return_dict[k] = model[v]
@@ -1400,7 +1587,7 @@ def fetch_rv_params(param_dict, model):
 
 
 def composite_distribution_factory(
-    variable_name, d_name, param_dict, package="scipy", model=None
+    variable_name, d_name, param_dict, backend="scipy", model=None
 ) -> CompositeDistribution | None:
     """
     Parameters
@@ -1412,33 +1599,33 @@ def composite_distribution_factory(
     param_dict: dict
         Dictionary of parameter name, parameter value pairs. Parameter values should be either scipy rv_frozen objects
         or strings that can be converted to floats.
-    package: str
-        Which package to use to create the distributions. Currently "scipy".
+    backend: str
+        Which backend to use to create the distributions. Currently "scipy".
 
     Returns
     -------
     d: CompositeDistribution
          A wrapper around a set of scipy distributions with three methods: .rvs(), .pdf(), and .logpdf()
-    """
 
-    # TODO: This function is a huge mess of if-else statements. All of this should maybe be put into the parser classes
-    #     to take advantage of all the parameter checking that happens there. Consider this temporary.
-    #
-    # TODO: Currently no checks are done on the support of the parameter to ensure it matches parameter requirements
-    #     e.g. a > 0, b > 0 in the beta distribution.
-    #
-    # TODO: It might be possible to do moment matching in some limited sense. Currently the initial value for the
-    #     parameter distributions is thrown away, could use this value to moment match? Maybe not worth it.
+    TODO: This function is a huge mess of if-else statements. All of this should maybe be put into the parser classes
+        to take advantage of all the parameter checking that happens there. Consider this temporary.
+
+    TODO: Currently no checks are done on the support of the parameter to ensure it matches parameter requirements
+        e.g. a > 0, b > 0 in the beta distribution.
+
+    TODO: It might be possible to do moment matching in some limited sense. Currently the initial value for the
+        parameter distributions is thrown away, could use this value to moment match? Maybe not worth it.
+    """
 
     def tau_to_scale(key, value):
         if key in {"tau", "precision"}:
             return 1 / value
         return value
 
-    if package == "scipy":
+    if backend == "scipy":
         base_d = NAME_TO_DIST_SCIPY_FUNC[d_name]
     else:
-        raise NotImplementedError('Only package = "scipy"  is supported.')
+        raise NotImplementedError('Only backend = "scipy"  is supported.')
 
     param_dict = param_values_to_floats(param_dict)
 
@@ -1451,7 +1638,7 @@ def tau_to_scale(key, value):
             [x in set(param_dict.keys()) for x in LOWER_BOUND_ALIASES]
         )
 
-        if (has_upper_bound or has_lower_bound) and package == "scipy":
+        if (has_upper_bound or has_lower_bound) and backend == "scipy":
             warn(
                 'Moment conditions are not supported for compound distributions, and parameters "mean" and "std" will'
                 'be interpreted as "loc" and "scale". Since you have passed boundaries, the first and second moments'
@@ -1479,7 +1666,7 @@ def tau_to_scale(key, value):
             param_dict, UPPER_BOUND_ALIASES, "b", variable_name, d_name
         )
 
-    elif d_name == "halfnormal" and package == "scipy":
+    elif d_name == "halfnormal" and backend == "scipy":
         if any([x in set(param_dict.keys()) for x in MEAN_ALIASES]):
             warn(
                 "Moment conditions are not supported for compound distributions. If you pass a random variable as a "
@@ -1497,7 +1684,7 @@ def tau_to_scale(key, value):
         )
 
     elif d_name == "inv_gamma":
-        if any([x in set(param_dict.keys()) for x in MOMENTS]) and package == "scipy":
+        if any([x in set(param_dict.keys()) for x in MOMENTS]) and backend == "scipy":
             warn(
                 "Moment conditions are not supported for compound distributions. If you pass a random variable as a "
                 "parameter value, do not pass in mean or std.",
@@ -1513,7 +1700,7 @@ def tau_to_scale(key, value):
         )
 
     elif d_name == "beta":
-        if any([x in set(param_dict.keys()) for x in MOMENTS]) and package == "scipy":
+        if any([x in set(param_dict.keys()) for x in MOMENTS]) and backend == "scipy":
             warn(
                 "Moment conditions are not supported for compound distributions. If you pass a random variable as a "
                 "parameter value, do not pass in mean or std. These conditions will be ignored, and this may cause an"
@@ -1530,7 +1717,7 @@ def tau_to_scale(key, value):
             )
 
     elif d_name == "gamma":
-        if any([x in set(param_dict.keys()) for x in MOMENTS]) and package == "scipy":
+        if any([x in set(param_dict.keys()) for x in MOMENTS]) and backend == "scipy":
             warn(
                 "Moment conditions are not supported for compound distributions. If you pass a random variable as a "
                 "parameter value, do not pass in mean or std. These conditions will be ignored, and this may cause an"
@@ -1546,14 +1733,33 @@ def tau_to_scale(key, value):
                 param_dict, BETA_SHAPE_ALIASES_2, "b", variable_name, d_name
             )
 
-    if package == "scipy":
+    if backend == "scipy":
         d = CompositeDistribution(base_d, **param_dict)
         return d
 
 
 def create_prior_distribution_dictionary(
-    raw_prior_dict: dict[str, str],
-) -> dict[str, Any]:
+    raw_prior_dict: dict[str, str], backend: Literal["scipy", "pymc"] = "scipy"
+) -> tuple[SymbolDictionary, SymbolDictionary]:
+    """
+
+    Parameters
+    ----------
+    raw_prior_dict: dict[str, str]
+        Dictionary of variable name: distribution string pairs.
+
+    backend: Literal['scipy', 'pymc']
+        Which backend to use to create the distributions. Currently "scipy" and "pymc" are supported.
+
+    Returns
+    -------
+    prior_dict: SymbolDictionary
+        A dictionary of variable name: distribution pairs.
+
+    hyper_prior_dict: SymbolDictionary
+        A dictionary of variable name: (parent_rv, param, rv) pairs. This is used to keep track of the parent-child
+        relationships between distributions in the case of compound distributions.
+    """
     variable_names, d_names, param_dicts = preprocess_prior_dict(raw_prior_dict)
     basic_distributions, compound_distributions = split_out_composite_distributions(
         variable_names, d_names, param_dicts
@@ -1575,7 +1781,9 @@ def create_prior_distribution_dictionary(
                 param_dict[param] = prior_dict[value]
                 rvs_used_in_d.append((variable_name, param, value))
 
-        d = composite_distribution_factory(variable_name, d_name, param_dict)
+        d = composite_distribution_factory(
+            variable_name, d_name, param_dict, backend=backend
+        )
         prior_dict[variable_name] = d
         for parent_rv, param, rv in rvs_used_in_d:
             hyper_prior_dict[rv] = (parent_rv, param, prior_dict[rv])
diff --git a/gEconpy/parser/parse_equations.py b/gEconpy/parser/parse_equations.py
index 7bdabcc..2127957 100644
--- a/gEconpy/parser/parse_equations.py
+++ b/gEconpy/parser/parse_equations.py
@@ -1,4 +1,5 @@
 import re
+
 from collections import defaultdict
 
 import sympy as sp
@@ -185,12 +186,12 @@ def convert_to_python_operator(token: str) -> str:
         A string representing a mathematical operation.
 
     Returns
-    ---------
+    -------
     str
         A string representing the same operation in python syntax.
 
     Notes
-    ----------
+    -----
     The syntax of a gEcon GCN file is slightly different from what SymPy expects, this function resolves the
     differences. In particular:
         1. Exponents are marked with a caret "^" in the GCN file, and must be converted to python's **
@@ -308,7 +309,7 @@ def single_symbol_to_sympy(
     ----------
     variable : str
         A gEcon variable or parameter.
-    assumptions : Optional[Dict]
+    assumptions : dict, optional
         Assumptions for the symbol.
 
     Returns
@@ -322,8 +323,12 @@ def single_symbol_to_sympy(
     if "[" not in variable and "]" not in variable:
         return sp.Symbol(variable, **assumptions[variable])
 
-    variable_name, time_part = variable.split("[")
-    time_part = time_part.replace("]", "")
+    try:
+        variable_name, time_part = variable.split("[")
+        time_part = time_part.replace("]", "")
+    except Exception as e:
+        raise ValueError(f"Error encountered while parsing: {variable}") from e
+
     if time_part == "ss":
         return TimeAwareSymbol(variable_name, 0).to_ss()
     else:
@@ -405,9 +410,9 @@ def build_sympy_equations(
         try:
             eq_sympy = sp.parse_expr(eq_str, evaluate=False, local_dict=sub_dict)
         except Exception as e:
-            print(f"Error encountered while parsing {eq_str}")
-            print(e)
-            raise e
+            raise ValueError(
+                f"Error encountered the following error while parsing: {eq_str}\n"
+            ) from e
 
         eq_sympy = sp.Eq(*eq_sympy)
         flags["is_calibrating"] = calibrating_parameter is not None
diff --git a/gEconpy/parser/parse_plaintext.py b/gEconpy/parser/parse_plaintext.py
index 035b532..33dec39 100644
--- a/gEconpy/parser/parse_plaintext.py
+++ b/gEconpy/parser/parse_plaintext.py
@@ -1,6 +1,6 @@
 import re
 
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     DistributionParsingError,
     MissingParameterValueException,
 )
@@ -108,9 +108,9 @@ def extract_distributions(text: str) -> tuple[str, dict[str, str]]:
 
     Returns
     -------
-    str
+    outputs: str
         Model file with prior distribution information removed.
-    Dict[str, str]
+    prior_dict: dict
         Dictionary of the form parameter:distribution.
 
     Examples
@@ -136,14 +136,16 @@ def extract_distributions(text: str) -> tuple[str, dict[str, str]]:
             # This is a parameter definition, but it might be missing a default value
             else:
                 # Extract the distribution declaration
-                *dist_info, param_value = other.split("=")
+                dist_info = other.replace(";", "").split("=")
+                param_value = dist_info[-1]
+
                 dist_info = "=".join(dist_info)
 
                 # This should only happen in the user didn't give a default value
                 if ")" in param_value:
                     raise MissingParameterValueException(param_name)
 
-                new_line = f"{param_name.strip()} = {param_value.strip()}"
+                new_line = f"{param_name.strip()} = {param_value.strip()};"
             output.append(new_line)
             prior_dict[param_name.strip()] = dist_info.strip()
         else:
diff --git a/gEconpy/exceptions/__init__.py b/gEconpy/parser/sympy_to_pytensor.py
similarity index 100%
rename from gEconpy/exceptions/__init__.py
rename to gEconpy/parser/sympy_to_pytensor.py
diff --git a/gEconpy/parser/validation.py b/gEconpy/parser/validation.py
index dcddca4..fbc1b5f 100644
--- a/gEconpy/parser/validation.py
+++ b/gEconpy/parser/validation.py
@@ -1,4 +1,4 @@
-from gEconpy.exceptions.exceptions import InvalidComponentNameException
+from gEconpy.exceptions import InvalidComponentNameException
 from gEconpy.parser.constants import BLOCK_COMPONENTS
 
 
diff --git a/gEconpy/plotting/plotting.py b/gEconpy/plotting.py
similarity index 77%
rename from gEconpy/plotting/plotting.py
rename to gEconpy/plotting.py
index c8f670b..6933d56 100644
--- a/gEconpy/plotting/plotting.py
+++ b/gEconpy/plotting.py
@@ -1,30 +1,38 @@
 from itertools import combinations_with_replacement
-from typing import Any
+from typing import Any, cast
 
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import xarray as xr
+
 from matplotlib.colors import Colormap
 from matplotlib.figure import Figure
 from matplotlib.gridspec import GridSpec
 from scipy import stats
+from xarray_einstats.linalg import diagonal as xr_diagonal
+
+from gEconpy.model.model import check_bk_condition
 
 
-def prepare_gridspec_figure(n_cols: int, n_plots: int) -> tuple[GridSpec, list]:
+def prepare_gridspec_figure(
+    n_cols: int, n_plots: int, figure: plt.Figure | None = None
+) -> tuple[GridSpec, list]:
     """
      Prepare a figure with a grid of subplots. Centers the last row of plots if the number of plots is not square.
 
-     Parameters
-     ----------
+    Parameters
+    ----------
      n_cols : int
          The number of columns in the grid.
      n_plots : int
          The number of subplots in the grid.
+    figure : Figure, optional
+        The figure object to use
 
-     Returns
-     -------
+    Returns
+    -------
      GridSpec
          A matplotlib GridSpec object representing the layout of the grid.
     list of tuple(slice, slice)
@@ -35,7 +43,7 @@ def prepare_gridspec_figure(n_cols: int, n_plots: int) -> tuple[GridSpec, list]:
     has_remainder = remainder > 0
     n_rows = n_plots // n_cols + int(has_remainder)
 
-    gs = GridSpec(2 * n_rows, 2 * n_cols)
+    gs = GridSpec(2 * n_rows, 2 * n_cols, figure=figure)
     plot_locs = []
 
     for i in range(n_rows - int(has_remainder)):
@@ -52,13 +60,23 @@ def prepare_gridspec_figure(n_cols: int, n_plots: int) -> tuple[GridSpec, list]:
     return gs, plot_locs
 
 
-def _plot_single_variable(data, ax, ci=None, cmap=None, fill_color="tab:blue"):
+def set_axis_cmap(axis, cmap):
+    cycler = None
+    if cmap is not None:
+        color = getattr(plt.cm, cmap)(np.linspace(0, 1, 20))
+        cycler = plt.cycler(color=color)
+    axis.set_prop_cycle(cycler)
+
+
+def _plot_single_variable(
+    data: xr.DataArray, ax, ci=None, cmap=None, fill_color="tab:blue", **line_kwargs
+):
     """
     Plot the mean and optionally a confidence interval for a single variable.
 
     Parameters
     ----------
-    data : pd.DataFrame
+    data : xr.DataArray
         A DataFrame with one or more columns containing the data to plot.
     ax : Matplotlib Axes
         The Axes object to plot on.
@@ -68,32 +86,49 @@ def _plot_single_variable(data, ax, ci=None, cmap=None, fill_color="tab:blue"):
         The color map to use for the data.
     fill_color : str, optional
         The color to use to fill the confidence interval.
+    line_kwargs: optional
+        Additional keyword arguments to pass to the line plot.
 
     Returns
     -------
     None
     """
+    set_axis_cmap(ax, cmap)
 
     if ci is None:
-        data.plot(ax=ax, legend=False, cmap=cmap)
+        hue = "shock" if "shock" in data.coords else None
+        data.plot.line(x="time", ax=ax, add_legend=False, hue=hue, **line_kwargs)
+        if hue is not None:
+            lines = ax.get_lines()
+            for line, shock in zip(lines, data.coords["shock"].values):
+                line.set_label(shock)
 
     else:
         q_low, q_high = ((1 - ci) / 2), 1 - ((1 - ci) / 2)
-        ci_bounds = data.quantile([q_low, q_high], axis=1).T
+        ci_bounds = data.quantile([q_low, q_high], dim=["simulation"])
 
-        data.mean(axis=1).plot(ax=ax, legend=False, cmap=cmap)
-        ci_bounds.plot(ax=ax, ls="--", lw=0.5, color="k", legend=False)
+        data.mean(dim="simulation").plot.line(
+            x="time", ax=ax, add_legend=False, **line_kwargs
+        )
+        ci_bounds.plot.line(
+            ax=ax,
+            x="time",
+            hue="quantile",
+            ls="--",
+            lw=0.5,
+            color="k",
+            add_legend=False,
+        )
         ax.fill_between(
-            ci_bounds.index,
-            y1=ci_bounds.iloc[:, 0],
-            y2=ci_bounds.iloc[:, 1],
+            ci_bounds.coords["time"].values,
+            *ci_bounds.transpose("quantile", "time").values,
             color=fill_color,
             alpha=0.25,
         )
 
 
 def plot_simulation(
-    simulation: pd.DataFrame,
+    simulation: xr.DataArray,
     vars_to_plot: list[str] | None = None,
     ci: float | None = None,
     n_cols: int | None = None,
@@ -133,24 +168,24 @@ def plot_simulation(
     """
 
     if vars_to_plot is None:
-        vars_to_plot = simulation.index
+        vars_to_plot = simulation.coords["variable"].values.tolist()
     n_plots = len(vars_to_plot)
     n_cols = min(4, n_plots) if n_cols is None else n_cols
 
-    gs, plot_locs = prepare_gridspec_figure(n_cols, n_plots)
     fig = plt.figure(figsize=figsize, dpi=dpi)
+    gs, plot_locs = prepare_gridspec_figure(n_cols, n_plots)
 
     for idx, variable in enumerate(vars_to_plot):
-        if variable not in simulation.index:
+        if variable not in simulation.coords["variable"]:
             raise ValueError(f"{variable} not found among model variables.")
         axis = fig.add_subplot(gs[plot_locs[idx]])
 
         _plot_single_variable(
-            simulation.loc[variable].unstack(1),
+            simulation.sel(variable=variable),
             ci=ci,
             ax=axis,
-            cmap=cmap,
             fill_color=fill_color,
+            cmap=cmap,
         )
 
         axis.set(title=variable)
@@ -162,9 +197,9 @@ def plot_simulation(
 
 
 def plot_irf(
-    irf: pd.DataFrame,
-    vars_to_plot: list[str] | None = None,
-    shocks_to_plot: list[str] | None = None,
+    irf: xr.DataArray | list[xr.DataArray] | dict[str, xr.DataArray],
+    vars_to_plot: str | list[str] | None = None,
+    shocks_to_plot: str | list[str] | None = None,
     n_cols: int | None = None,
     legend: bool = False,
     cmap: str | Colormap | None = None,
@@ -177,9 +212,10 @@ def plot_irf(
 
     Parameters
     ----------
-    irf : pd.DataFrame
-        A DataFrame with the impulse response functions. The index should contain the variables to plot, and the columns
-        should contain the shocks, with a multi-index for the period and shock type.
+    irf : xr.DataArray, list of xr.DataArray, or dict of xr.DataArray
+        A DataArray with the impulse response functions. The index should contain the variables to plot, and the columns
+        should contain the shocks, with a multi-index for the period and shock type. When plotting multiple scenarios,
+        provide a list of DataArrays or a dictionary with the scenario names as keys.
     vars_to_plot : list of str, optional
         A list of variables to plot. If not provided, all variables in the DataFrame will be plotted.
     shocks_to_plot : list of str, optional
@@ -203,29 +239,43 @@ def plot_irf(
     matplotlib.figure.Figure
         The figure object.
     """
+    if not isinstance(vars_to_plot, str | list | None):
+        raise ValueError(
+            f"Expected strings or list of strings for parameter vars_to_plot, got {vars_to_plot} of "
+            f"type {type(vars_to_plot)}"
+        )
+
+    if isinstance(irf, xr.DataArray):
+        irf = {"": irf}
+    elif isinstance(irf, list):
+        irf = {f"Scenario {i}": irf[i] for i in range(len(irf))}
+
+    coords = irf[next(iter(irf.keys()))].coords
 
     if vars_to_plot is None:
-        vars_to_plot = irf.index.values.tolist()
+        vars_to_plot = coords["variable"].values.tolist()
+    if isinstance(vars_to_plot, str):
+        vars_to_plot = [vars_to_plot]
 
-    else:
-        for var in vars_to_plot:
-            if var not in irf.index:
-                raise ValueError(f"{var} not found among simulated impulse responses.")
+    for var in vars_to_plot:
+        if var not in coords["variable"]:
+            raise ValueError(f"{var} not found among simulated impulse responses.")
 
-    if not isinstance(vars_to_plot, list):
-        raise ValueError(
-            f"Expected list for parameter vars_to_plot, got {vars_to_plot} of type {type(vars_to_plot)}"
-        )
+    if "shock" in coords:
+        shock_list = coords["shock"].values.tolist()
+    else:
+        shock_list = None
 
-    shock_list = irf.columns.get_level_values(1).unique().tolist()
     if shocks_to_plot is None:
         shocks_to_plot = shock_list
-    else:
-        for shock in shocks_to_plot:
-            if shock not in shock_list:
-                raise ValueError(
-                    f"{shock} not found among shocks used in impulse response data."
-                )
+    if isinstance(shocks_to_plot, str):
+        shocks_to_plot = [shocks_to_plot]
+
+    for shock in shocks_to_plot:
+        if shock not in shock_list:
+            raise ValueError(
+                f"{shock} not found among shocks used in impulse response data."
+            )
 
     if not isinstance(shocks_to_plot, list):
         raise ValueError(
@@ -236,34 +286,55 @@ def plot_irf(
     n_plots = len(vars_to_plot)
     n_cols = min(4, n_plots) if n_cols is None else n_cols
 
-    gs, plot_locs = prepare_gridspec_figure(n_cols, n_plots)
-    fig = plt.figure(figsize=figsize, dpi=dpi)
+    markers = ["-", "--", "-.", ":"]
+    scenario_names = list(irf.keys())
+
+    fig = plt.figure(figsize=figsize, dpi=dpi, constrained_layout=True)
+    gs, plot_locs = prepare_gridspec_figure(n_cols, n_plots, figure=fig)
+
+    plot_row_idxs = [x[0].stop // 2 - 1 for x in plot_locs]
+    plot_rows = sorted(list(set(plot_row_idxs)))
+    is_square = all([plot_row_idxs.count(i) == n_cols for i in plot_rows])
+    last_row_idxs = [plot_rows[-1]] if is_square else plot_rows[-2:]
 
     for idx, variable in enumerate(vars_to_plot):
-        axis = fig.add_subplot(gs[plot_locs[idx]])
+        loc = plot_locs[idx]
+        row_idx = plot_row_idxs[idx]
+
+        axis = fig.add_subplot(gs[loc])
+        sel_dict = {"variable": variable}
+        if shocks_to_plot is not None:
+            sel_dict["shock"] = shocks_to_plot
+
+        for scenario_idx, (scenario, irf_data) in enumerate(irf.items()):
+            _plot_single_variable(
+                irf_data.sel(**sel_dict),
+                ax=axis,
+                cmap=cmap,
+                ls=markers[scenario_idx % 4],
+            )
 
-        _plot_single_variable(
-            irf.loc[variable, pd.IndexSlice[:, shocks_to_plot]].unstack(1),
-            ax=axis,
-            cmap=cmap,
-        )
+        if (idx == 0) and len(scenario_names) > 1 and scenario_names[0] != "":
+            lines = axis.get_lines()
+            axis.legend(handles=lines, labels=scenario_names)
 
         axis.set(title=variable)
+        if row_idx not in last_row_idxs:
+            axis.set(xticklabels=[], xlabel="")
+
         [spine.set_visible(False) for spine in axis.spines.values()]
         axis.grid(ls="--", lw=0.5)
 
-    fig.tight_layout()
-
     if legend:
         if legend_kwargs is None:
+            n_shocks_to_plot = len(shocks_to_plot) if shocks_to_plot is not None else 1
             legend_kwargs = {
-                "ncol": min(4, len(shocks_to_plot)),
-                "loc": "center",
-                "bbox_to_anchor": (0.5, 1.05),
-                "bbox_transform": fig.transFigure,
+                "ncol": min(4, n_shocks_to_plot),
+                "loc": "lower center",
+                "bbox_to_anchor": (0.5, 1.0),
             }
-
-        fig.axes[0].legend(**legend_kwargs)
+        handles = fig.axes[0].get_lines()
+        fig.legend(handles=handles, labels=shocks_to_plot, **legend_kwargs)
 
     return fig
 
@@ -287,7 +358,7 @@ def plot_prior_solvability(
         The number of samples to draw from the prior distributions.
     seed : int, optional
         The seed to use for the random number generator.
-    params_to_plot : List[str], optional
+    params_to_plot : list of str, optional
         A list of parameter names to include in the plots. If not provided, all parameters will be plotted.
 
     Returns
@@ -296,7 +367,7 @@ def plot_prior_solvability(
         The Figure object containing the plots
 
     Notes
-    ----------
+    -----
     - Parameters will be sampled from prior distributions defined in the GCN.
     - The following failure modes are considered:
         - Steady state: The steady state of the model could not be calculated.
@@ -428,7 +499,19 @@ def plot_prior_solvability(
     return fig
 
 
-def plot_eigenvalues(model: Any, figsize: tuple[float, float] = None, dpi: int = None):
+def plot_eigenvalues(
+    model: Any,
+    A: np.ndarray | None = None,
+    B: np.ndarray | None = None,
+    C: np.ndarray | None = None,
+    D: np.ndarray | None = None,
+    linearize_model_kwargs: dict | None = None,
+    fig: plt.Figure | None = None,
+    figsize: tuple[float, float] | None = None,
+    dpi: int | None = None,
+    plot_circle: bool = True,
+    **parameter_updates,
+):
     """
     Plot the eigenvalues of the model solution, along with a unit circle. Eigenvalues with modulus greater than 1 are
     shown in red, while those with modulus less than 1 are shown in blue. Eigenvalues greater than 10 in modulus
@@ -437,11 +520,31 @@ def plot_eigenvalues(model: Any, figsize: tuple[float, float] = None, dpi: int =
     Parameters
     ----------
     model : gEconModel
-        The model to plot the eigenvalues of.
-    figsize : Tuple[float, float], optional
+        DSGE model object
+    A : np.ndarray, optional
+        Matrix of partial derivative, linearized around the steady state. Derivatives taken with respect to variables
+        at t-1. If provided, all of A, B, C and D must be provided.
+    B : np.ndarray, optional
+        Matrix of partial derivative, linearized around the steady state. Derivatives taken with respect to variables
+        at t. If provided, all of A, B, C and D must be provided.
+    C : np.ndarray, optional
+        Matrix of partial derivative, linearized around the steady state. Derivatives taken with respect to variables
+        at t+1. If provided, all of A, B, C and D must be provided.
+    D : np.ndarray, optional
+        Matrix of partial derivative, linearized around the steady state. Derivatives taken with respect to exogenous
+        shocks. If provided, all of A, B, C and D must be provided.
+    linearize_model_kwargs: dict, optional
+        Arguments passed to model.linearize_model. Ignored if A, B, C, D are provided.
+    fig: Matplotlib Figure, optional
+        The figure object to plot on. If not provided, a new figure will be created.
+    figsize : tuple[float, float], optional
         The size of the figure to create.
     dpi : int, optional
         The resolution of the figure to create.
+    plot_circle: bool, optional
+        Whether to plot the unit circle. Default is True.
+    parameter_updates
+        A dictionary of parameter at which to linearize the model.
 
     Returns
     -------
@@ -454,15 +557,35 @@ def plot_eigenvalues(model: Any, figsize: tuple[float, float] = None, dpi: int =
     if dpi is None:
         dpi = 100
 
-    fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
-    data = model.check_bk_condition(verbose=False)
-    n_infinity = (data.Modulus > 10).sum()
+    if fig is None:
+        fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
+    else:
+        ax = fig.axes[0]
+
+    if linearize_model_kwargs is None:
+        linearize_model_kwargs = {}
+
+    data = cast(
+        pd.DataFrame,
+        check_bk_condition(
+            model,
+            A=A,
+            B=B,
+            C=C,
+            D=D,
+            verbose=False,
+            return_value="dataframe",
+            **linearize_model_kwargs,
+        ),
+    )
 
+    n_infinity = (data["Modulus"] > 10).sum()
     data = data[data.Modulus < 10]
 
-    x_circle = np.linspace(-2 * np.pi, 2 * np.pi, 1000)
+    if plot_circle:
+        x_circle = np.linspace(-2 * np.pi, 2 * np.pi, 1000)
+        ax.plot(np.cos(x_circle), np.sin(x_circle), color="k", lw=1)
 
-    ax.plot(np.cos(x_circle), np.sin(x_circle), color="k", lw=1)
     ax.set_aspect("equal")
     colors = ["tab:red" if x > 1.0 else "tab:blue" for x in data.Modulus]
     ax.scatter(data.Real, data.Imaginary, color=colors, s=50, lw=1, edgecolor="k")
@@ -510,6 +633,7 @@ def plot_covariance_matrix(
         Keyword arguments forwarded to plt.imshow
     annotation_kwargs: dict, optional
         Keyword arguments forwarded to gEconpy.plotting.annotate_heatmap
+
     Returns
     -------
     matplotlib.figure.Figure
@@ -635,7 +759,7 @@ def annotate_heatmap(
         the text labels.
     """
 
-    if not isinstance(data, (list, np.ndarray)):
+    if not isinstance(data, list | np.ndarray):
         data = im.get_array()
 
     # Normalize the threshold to the images color range.
@@ -666,7 +790,7 @@ def annotate_heatmap(
 
 
 def plot_acf(
-    acorr_matrix: pd.DataFrame,
+    acorr: np.ndarray | xr.DataArray,
     vars_to_plot: list[str] | None = None,
     figsize: tuple[int, int] | None = (14, 4),
     dpi: int | None = 100,
@@ -677,8 +801,8 @@ def plot_acf(
 
     Parameters
     ----------
-    acorr_matrix: pandas.DataFrame
-        Matrix of autocorrelation values. Rows represent variables and columns represent lags.
+    acorr_matrix: DataArray
+        Tensor of correlations.
     vars_to_plot: list of str, optional
         List of variables to plot. If not provided, all variables in `acorr_matrix` will be plotted.
     figsize: tuple, optional
@@ -693,13 +817,13 @@ def plot_acf(
     matplotlib.figure.Figure
         Figure object containing the plots.
     """
-
+    all_variables = acorr.coords["variable"].values
     if vars_to_plot is None:
-        vars_to_plot = acorr_matrix.index
+        vars_to_plot = all_variables
 
     else:
         for var in vars_to_plot:
-            if var not in acorr_matrix.index:
+            if var not in all_variables:
                 raise ValueError(
                     f"Can not plot variable {var}, it was not found in the provided covariance matrix"
                 )
@@ -707,21 +831,23 @@ def plot_acf(
     n_plots = len(vars_to_plot)
     n_cols = min(n_cols, n_plots)
 
-    fig = plt.figure(figsize=figsize, dpi=dpi)
-    gc, plot_locs = prepare_gridspec_figure(n_cols=n_cols, n_plots=n_plots)
+    fig = plt.figure(figsize=figsize, dpi=dpi, layout="constrained")
+    gc, plot_locs = prepare_gridspec_figure(n_cols=n_cols, n_plots=n_plots, figure=fig)
 
-    x_values = acorr_matrix.columns
+    acorr_matrix = xr_diagonal(acorr, dims=["variable", "variable_aux"]).sel(
+        variable=vars_to_plot
+    )
+    x_values = acorr_matrix.coords["lag"]
 
     for variable, plot_loc in zip(vars_to_plot, plot_locs):
         axis = fig.add_subplot(gc[plot_loc])
-        axis.scatter(x_values, acorr_matrix.loc[variable, :])
-        axis.vlines(x_values, 0, acorr_matrix.loc[variable, :])
+        axis.scatter(x_values, acorr_matrix.sel(variable=variable).values)
+        axis.vlines(x_values, 0, acorr_matrix.sel(variable=variable).values)
 
         [spine.set_visible(False) for spine in axis.spines.values()]
         axis.grid(ls="--", lw=0.5)
         axis.set(title=variable)
 
-    fig.tight_layout()
     return fig
 
 
@@ -745,9 +871,9 @@ def plot_corner(
     ----------
     idata : arviz.InferenceData
         An arviz idata object with a posterior group.
-    var_names : List[str], optional
+    var_names : list of str, optional
         A list of strings specifying the variables to plot. If not provided, all variables in `idata` will be plotted.
-    figsize : Tuple[int, int], optional
+    figsize : tuple, optional
         The size of the figure in inches. Default is (14, 14).
     dpi : int, optional
         The resolution of the figure in dots per inch. Default is 144.
@@ -763,7 +889,7 @@ def plot_corner(
         Whether or not to show the modes of the marginal distributions. Default is True.
 
     Returns
-    ----------
+    -------
     matplotlib.figure.Figure
         Figure object containing the plots.
     """
@@ -963,3 +1089,16 @@ def plot_kalman_filter(
 
     fig.tight_layout()
     return fig
+
+
+__all__ = [
+    "prepare_gridspec_figure",
+    "plot_simulation",
+    "plot_irf",
+    "plot_prior_solvability",
+    "plot_eigenvalues",
+    "plot_covariance_matrix",
+    "plot_acf",
+    "plot_corner",
+    "plot_kalman_filter",
+]
diff --git a/gEconpy/plotting/__init__.py b/gEconpy/plotting/__init__.py
deleted file mode 100644
index 0dbe254..0000000
--- a/gEconpy/plotting/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from gEconpy.plotting.plotting import (
-    plot_acf,
-    plot_corner,
-    plot_covariance_matrix,
-    plot_eigenvalues,
-    plot_irf,
-    plot_kalman_filter,
-    plot_prior_solvability,
-    plot_simulation,
-    prepare_gridspec_figure,
-)
-
-__all__ = [
-    "prepare_gridspec_figure",
-    "plot_simulation",
-    "plot_irf",
-    "plot_prior_solvability",
-    "plot_eigenvalues",
-    "plot_covariance_matrix",
-    "plot_acf",
-    "plot_corner",
-    "plot_kalman_filter",
-]
diff --git a/gEconpy/sampling/__init__.py b/gEconpy/sampling/__init__.py
deleted file mode 100644
index cc9ae5b..0000000
--- a/gEconpy/sampling/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from gEconpy.sampling.posterior_utilities import (
-    kalman_filter_from_posterior,
-    simulate_trajectories_from_posterior,
-)
-from gEconpy.sampling.prior_utilities import (
-    kalman_filter_from_prior,
-    prior_solvability_check,
-    simulate_trajectories_from_prior,
-)
-
-__all__ = [
-    "prior_solvability_check",
-    "simulate_trajectories_from_prior",
-    "kalman_filter_from_prior",
-    "simulate_trajectories_from_posterior",
-    "kalman_filter_from_posterior",
-]
diff --git a/gEconpy/sampling/posterior_utilities.py b/gEconpy/sampling/posterior_utilities.py
deleted file mode 100644
index 797f16f..0000000
--- a/gEconpy/sampling/posterior_utilities.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import numpy as np
-import pandas as pd
-import xarray as xr
-
-from gEconpy.classes.progress_bar import ProgressBar
-from gEconpy.estimation.estimate import build_Q_and_H, build_Z_matrix, split_param_dict
-from gEconpy.estimation.kalman_filter import kalman_filter
-from gEconpy.estimation.kalman_smoother import kalman_smoother
-from gEconpy.sampling.prior_utilities import get_initial_time_index
-from gEconpy.shared.utilities import split_random_variables
-
-
-def simulate_trajectories_from_posterior(
-    model, posterior, n_samples=1000, n_simulations=100, simulation_length=40
-):
-    simulations = []
-    model_var_names = [x.base_name for x in model.variables]
-    shock_names = [x.base_name for x in model.shocks]
-
-    random_idx = np.random.choice(
-        posterior.coords["sample"], replace=False, size=n_samples
-    )
-    progress_bar = ProgressBar(n_samples, "Sampling")
-    for i, idx in enumerate(random_idx):
-        index = dict(zip(["chain", "draw"], idx))
-        param_dict = {
-            k: v["data"]
-            for k, v in posterior.sel(**index).to_dict()["data_vars"].items()
-        }
-        free_param_dict, shock_dict, obs_dict = split_random_variables(
-            param_dict, shock_names, model_var_names
-        )
-        model.free_param_dict.update(free_param_dict)
-        progress_bar.start()
-
-        try:
-            model.steady_state(verbose=False)
-            model.solve_model(verbose=False, on_failure="ignore")
-
-            data = model.simulate(
-                simulation_length=simulation_length,
-                n_simulations=n_simulations,
-                show_progress_bar=False,
-            )
-            simulaton_ids = np.arange(n_simulations).astype(int)
-
-            data = data.rename(
-                axis=1,
-                level=1,
-                mapper=dict(zip(simulaton_ids, simulaton_ids + (n_simulations * i))),
-            )
-
-            simulations.append(data)
-
-        except ValueError:
-            continue
-
-        finally:
-            progress_bar.stop()
-
-    simulations = pd.concat(simulations, axis=1)
-    return simulations
-
-
-def kalman_filter_from_posterior(
-    model, data, posterior, n_samples=1000, filter_type="univariate"
-):
-    observed_vars = data.columns.tolist()
-    model_var_names = [x.base_name for x in model.variables]
-    shock_names = [x.base_name for x in model.shocks]
-
-    results = []
-    model_var_names = [x.base_name for x in model.variables]
-    shock_names = [x.base_name for x in model.shocks]
-
-    random_idx = np.random.choice(
-        posterior.coords["sample"], replace=False, size=n_samples
-    )
-    progress_bar = ProgressBar(n_samples, "Sampling")
-    for idx in random_idx:
-        index = dict(zip(["chain", "draw"], idx))
-        all_param_dict = {
-            k: v["data"]
-            for k, v in posterior.sel(**index).to_dict()["data_vars"].items()
-        }
-
-        param_dict, a0_dict, P0_dict = split_param_dict(all_param_dict)
-        free_param_dict, shock_dict, noise_dict = split_random_variables(
-            param_dict, shock_names, model_var_names
-        )
-
-        model.free_param_dict.update(free_param_dict)
-        progress_bar.start()
-
-        model.steady_state(verbose=False)
-        model.solve_model(verbose=False, on_failure="error")
-
-        T, R = model.T.values, model.R.values
-        T = np.ascontiguousarray(T)
-        R = np.ascontiguousarray(R)
-        Z = build_Z_matrix(observed_vars, model_var_names)
-        Q, H = build_Q_and_H(shock_dict, shock_names, observed_vars, noise_dict)
-
-        a0 = np.array(list(a0_dict.values()))[:, None] if len(a0_dict) > 0 else None
-        P0 = (
-            np.eye(len(P0_dict)) * np.array(list(P0_dict.keys()))
-            if len(P0_dict) > 0
-            else None
-        )
-
-        filter_results = kalman_filter(
-            np.ascontiguousarray(data.values),
-            T,
-            Z,
-            R,
-            H,
-            Q,
-            a0=a0,
-            P0=P0,
-            filter_type=filter_type,
-        )
-        filtered_states, _, filtered_covariances, *_ = filter_results
-
-        smoother_results = kalman_smoother(
-            T, R, Q, filtered_states, filtered_covariances
-        )
-        results.append(list(filter_results) + list(smoother_results))
-
-        progress_bar.stop()
-
-    coords = {
-        "sample": np.arange(n_samples),
-        "time": data.index.values,
-        "variable": model_var_names,
-    }
-
-    pred_coords = {
-        "sample": np.arange(n_samples),
-        "time": np.r_[
-            get_initial_time_index(data),
-            data.index.values,
-        ],
-        "variable": model_var_names,
-    }
-
-    cov_coords = {
-        "sample": np.arange(n_samples),
-        "time": data.index.values,
-        "variable": model_var_names,
-        "variable2": model_var_names,
-    }
-
-    pred_cov_coords = {
-        "sample": np.arange(n_samples),
-        "time": np.r_[
-            get_initial_time_index(data),
-            data.index.values,
-        ],
-        "variable": model_var_names,
-        "variable2": model_var_names,
-    }
-
-    kf_data = xr.Dataset(
-        {
-            "Filtered_State": xr.DataArray(
-                data=np.stack([results[i][0] for i in range(n_samples)]), coords=coords
-            ),
-            "Predicted_State": xr.DataArray(
-                data=np.stack([results[i][1] for i in range(n_samples)]),
-                coords=pred_coords,
-            ),
-            "Smoothed_State": xr.DataArray(
-                data=np.stack([results[i][5] for i in range(n_samples)]), coords=coords
-            ),
-            "Filtered_Cov": xr.DataArray(
-                data=np.stack([results[i][2] for i in range(n_samples)]),
-                coords=cov_coords,
-            ),
-            "Predicted_Cov": xr.DataArray(
-                data=np.stack([results[i][3] for i in range(n_samples)]),
-                coords=pred_cov_coords,
-            ),
-            "Smoothed_Cov": xr.DataArray(
-                data=np.stack([results[i][6] for i in range(n_samples)]),
-                coords=cov_coords,
-            ),
-            "loglikelihood": xr.DataArray(
-                data=np.stack([results[i][4] for i in range(n_samples)]),
-                coords={"sample": np.arange(n_samples), "time": data.index.values},
-            ),
-        }
-    )
-
-    return kf_data
diff --git a/gEconpy/sampling/prior_utilities.py b/gEconpy/sampling/prior_utilities.py
deleted file mode 100644
index 47d7e0d..0000000
--- a/gEconpy/sampling/prior_utilities.py
+++ /dev/null
@@ -1,315 +0,0 @@
-import numpy as np
-import pandas as pd
-import xarray as xr
-from numpy.linalg import LinAlgError
-
-from gEconpy.classes.progress_bar import ProgressBar
-from gEconpy.estimation.estimate import build_Q_and_H, build_Z_matrix
-from gEconpy.estimation.kalman_filter import kalman_filter
-from gEconpy.estimation.kalman_smoother import kalman_smoother
-
-
-def prior_solvability_check(
-    model, n_samples, seed=None, param_subset=None, pert_solver="cycle_reduction"
-):
-    # Discard the noise priors here, we don't need them
-    param_dicts, *_ = model.sample_param_dict_from_prior(n_samples, seed, param_subset)
-
-    data = pd.DataFrame(param_dicts)
-    progress_bar = ProgressBar(n_samples, verb="Sampling")
-
-    if pert_solver not in ["cycle_reduction", "gensys"]:
-        raise ValueError(
-            f'Argument pert_solver must be one of "cycle_reduction" or "gensys", found {pert_solver}'
-        )
-
-    def check_solvable(param_dict):
-        try:
-            results = model.f_ss(param_dict)
-
-            ss_dict = results["ss_dict"]
-            calib_dict = results["calib_dict"]
-            ss_success = results["success"]
-            param_dict = param_dict | calib_dict
-
-        except ValueError:
-            return "steady_state"
-
-        if not ss_success:
-            return "steady_state"
-
-        try:
-            max_iter = 1000
-            tol = 1e-18
-            verbose = False
-
-            exog, endog = (
-                np.array(list(param_dict.values())),
-                np.array(list(ss_dict.values())),
-            )
-            A, B, C, D = model.build_perturbation_matrices(exog, endog)
-
-            if pert_solver == "cycle_reduction":
-                solver = (
-                    model.perturbation_solver.solve_policy_function_with_cycle_reduction
-                )
-                T, R, result, log_norm = solver(A, B, C, D, max_iter, tol, verbose)
-                pert_success = log_norm < 1e-8
-
-            elif pert_solver == "gensys":
-                solver = model.perturbation_solver.solve_policy_function_with_gensys
-                G_1, constant, impact, f_mat, f_wt, y_wt, gev, eu, loose = solver(
-                    A, B, C, D, tol, verbose
-                )
-                T = G_1[: model.n_variables, :][:, : model.n_variables]
-                R = impact[: model.n_variables, :]
-                pert_success = G_1 is not None
-
-        except (ValueError, LinAlgError):
-            return "perturbation"
-
-        if not pert_success:
-            return "perturbation"
-
-        bk_success = model.check_bk_condition(
-            system_matrices=[A, B, C, D], verbose=False, return_value="bool"
-        )
-        if not bk_success:
-            return "blanchard-kahn"
-
-        (
-            _,
-            variables,
-            _,
-        ) = model.perturbation_solver.make_all_variable_time_combinations()
-        gEcon_matrices = model.perturbation_solver.statespace_to_gEcon_representation(
-            A, T, R, variables, tol
-        )
-        P, Q, _, _, A_prime, R_prime, S_prime = gEcon_matrices
-
-        resid_norms = model.perturbation_solver.residual_norms(
-            B, C, D, Q, P, A_prime, R_prime, S_prime
-        )
-        norm_deterministic, norm_stochastic = resid_norms
-
-        if norm_deterministic > 1e-8:
-            return "deterministic_norm"
-        if norm_stochastic > 1e-8:
-            return "stochastic_norm"
-
-        return None
-
-    param_dicts = data.T.to_dict().values()
-    results = []
-
-    # TODO: How to parallelize this? The problem is the huge model object causes massive overhead.
-    free_params = model.free_param_dict.copy()
-    for param_dict in param_dicts:
-        progress_bar.start()
-        free_params.update(param_dict)
-        result = check_solvable(free_params)
-        results.append(result)
-        progress_bar.stop()
-
-    data["failure_step"] = results
-
-    return data
-
-
-def get_initial_time_index(df):
-    t0 = df.index[0]
-
-    if isinstance(df.index, pd.DatetimeIndex):
-        freq = df.index.inferred_freq
-        base_freq = freq.split("-")[0]
-
-        if "Q" in base_freq:
-            offset = pd.DateOffset(months=3)
-        elif "M" in base_freq:
-            offset = pd.DateOffset(months=1)
-        elif "Y" in base_freq:
-            offset = pd.DateOffset(years=1)
-        else:
-            raise NotImplementedError("Data isn't one of: Quarterly, Monthly, Annual")
-
-        return np.array(t0 - offset, dtype="datetime64")
-
-    else:
-        return np.array(t0 - 1)
-
-
-def simulate_trajectories_from_prior(
-    model,
-    n_samples=1000,
-    n_simulations=100,
-    simulation_length=40,
-    seed=None,
-    param_subset=None,
-    pert_kwargs=None,
-):
-    if pert_kwargs is None:
-        pert_kwargs = {}
-
-    simulations = []
-
-    free_param_dicts, shock_dicts, _ = model.sample_param_dict_from_prior(
-        n_samples, seed, param_subset
-    )
-    free_param_dicts = pd.DataFrame(free_param_dicts).T.to_dict()
-    shock_dicts = pd.DataFrame(shock_dicts).T.to_dict()
-
-    i = 0
-    progress_bar = ProgressBar(n_samples, "Sampling")
-    free_params = model.free_param_dict.copy()
-    for param_dict, shock_dict in zip(free_param_dicts.values(), shock_dicts.values()):
-        progress_bar.start()
-        free_params.update(param_dict)
-
-        try:
-            model.steady_state(verbose=False)
-            model.solve_model(verbose=False, on_failure="ignore", **pert_kwargs)
-
-            data = model.simulate(
-                simulation_length=simulation_length,
-                n_simulations=n_simulations,
-                shock_dict=shock_dict,
-                show_progress_bar=False,
-            )
-
-            simulaton_ids = np.arange(n_simulations).astype(int)
-
-            data = data.rename(
-                axis=1,
-                level=1,
-                mapper=dict(zip(simulaton_ids, simulaton_ids + (n_simulations * i))),
-            )
-
-            simulations.append(data)
-            i += 1
-
-        except ValueError:
-            continue
-
-        finally:
-            progress_bar.stop()
-
-    simulations = pd.concat(simulations, axis=1)
-    return simulations
-
-
-def safe_get_idx_as_dict(df, idx):
-    if idx >= df.shape[0]:
-        return {}
-    else:
-        return df.iloc[idx].to_dict()
-
-
-def kalman_filter_from_prior(
-    model, data, n_samples, filter_type="univariate", seed=None
-):
-    observed_vars = data.columns.tolist()
-    model_var_names = [x.base_name for x in model.variables]
-    shock_names = [x.name for x in model.shocks]
-
-    results = []
-    dicts_of_samples = model.sample_param_dict_from_prior(n_samples, seed=seed)
-    param_dicts, shock_dicts, noise_dicts = map(pd.DataFrame, dicts_of_samples)
-
-    progress_bar = ProgressBar(n_samples, "Sampling")
-    i = 0
-
-    while i < n_samples:
-        try:
-            param_dict = safe_get_idx_as_dict(param_dicts, i)
-            shock_dict = safe_get_idx_as_dict(shock_dicts, i)
-            obs_dict = safe_get_idx_as_dict(noise_dicts, i)
-
-            progress_bar.start()
-            model.free_param_dict.update(param_dict)
-
-            model.steady_state(verbose=False)
-            model.solve_model(verbose=False, on_failure="error")
-
-            T, R = model.T.values, model.R.values
-            Z = build_Z_matrix(observed_vars, model_var_names)
-            Q, H = build_Q_and_H(shock_dict, shock_names, observed_vars, obs_dict)
-
-            filter_results = kalman_filter(
-                data.values, T, Z, R, H, Q, a0=None, P0=None, filter_type=filter_type
-            )
-            filtered_states, _, filtered_covariances, *_ = filter_results
-
-            smoother_results = kalman_smoother(
-                T, R, Q, filtered_states, filtered_covariances
-            )
-            results.append(list(filter_results) + list(smoother_results))
-
-            i += 1
-            progress_bar.stop()
-        except (ValueError, np.linalg.LinAlgError):
-            continue
-
-    coords = {
-        "sample": np.arange(n_samples),
-        "time": data.index.values,
-        "variable": model_var_names,
-    }
-
-    pred_coords = {
-        "sample": np.arange(n_samples),
-        "time": np.r_[
-            get_initial_time_index(data),
-            data.index.values,
-        ],
-        "variable": model_var_names,
-    }
-
-    cov_coords = {
-        "sample": np.arange(n_samples),
-        "time": data.index.values,
-        "variable": model_var_names,
-        "variable2": model_var_names,
-    }
-
-    pred_cov_coords = {
-        "sample": np.arange(n_samples),
-        "time": np.r_[
-            get_initial_time_index(data),
-            data.index.values,
-        ],
-        "variable": model_var_names,
-        "variable2": model_var_names,
-    }
-
-    kf_data = xr.Dataset(
-        {
-            "Filtered_State": xr.DataArray(
-                data=np.stack([results[i][0] for i in range(n_samples)]), coords=coords
-            ),
-            "Predicted_State": xr.DataArray(
-                data=np.stack([results[i][1] for i in range(n_samples)]),
-                coords=pred_coords,
-            ),
-            "Smoothed_State": xr.DataArray(
-                data=np.stack([results[i][5] for i in range(n_samples)]), coords=coords
-            ),
-            "Filtered_Cov": xr.DataArray(
-                data=np.stack([results[i][2] for i in range(n_samples)]),
-                coords=cov_coords,
-            ),
-            "Predicted_Cov": xr.DataArray(
-                data=np.stack([results[i][3] for i in range(n_samples)]),
-                coords=pred_cov_coords,
-            ),
-            "Smoothed_Cov": xr.DataArray(
-                data=np.stack([results[i][6] for i in range(n_samples)]),
-                coords=cov_coords,
-            ),
-            "loglikelihood": xr.DataArray(
-                data=np.stack([results[i][4] for i in range(n_samples)]),
-                coords={"sample": np.arange(n_samples), "time": data.index.values},
-            ),
-        }
-    )
-
-    return kf_data
diff --git a/gEconpy/shared/__init__.py b/gEconpy/shared/__init__.py
deleted file mode 100644
index c4a70d3..0000000
--- a/gEconpy/shared/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from gEconpy.shared.dynare_convert import make_mod_file
-from gEconpy.shared.statsmodel_convert import compile_to_statsmodels
-
-__all__ = ["make_mod_file", "compile_to_statsmodels"]
diff --git a/gEconpy/shared/dynare_convert.py b/gEconpy/shared/dynare_convert.py
deleted file mode 100644
index 6b018c6..0000000
--- a/gEconpy/shared/dynare_convert.py
+++ /dev/null
@@ -1,375 +0,0 @@
-import re
-
-import sympy as sp
-from sympy.abc import greeks
-
-from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.shared.utilities import make_all_var_time_combos
-
-OPERATORS = list("+-/*^()=")
-
-
-def get_name(x: str | sp.Symbol) -> str:
-    """
-    This function returns the name of a string, TimeAwareSymbol, or sp.Symbol object.
-
-    Parameters
-    ----------
-    x : str, or sp.Symbol
-        The object whose name is to be returned. If str, x is directly returned.
-
-    Returns
-    -------
-    str
-        The name of the object.
-    """
-
-    if isinstance(x, str):
-        return x
-
-    elif isinstance(x, TimeAwareSymbol):
-        return x.safe_name
-
-    elif isinstance(x, sp.Symbol):
-        return x.name
-
-
-def build_hash_table(
-    items_to_hash: list[str | sp.Symbol],
-) -> tuple[dict[str, str], dict[str, str]]:
-    """
-    This function builds a pair of hash tables, one mapping variable names to hash values
-    and the other mapping hash values to variable names.
-
-    To safely distinguish between numeric values, variables, parameters, and time-indices
-    when converting sympy code to a Dynare model, all variables are first hashed to
-    strictly positive int64 objects using the square of the built-in `hash` function.
-
-    Parameters
-    ----------
-    items_to_hash : str or sp.Symbol
-        A list of variables to be hashed. Can contain strings or sp.Symbol objects.
-
-    Returns
-    -------
-    tuple of (dict, dict)
-        A tuple containing two dictionaries: the first maps variable names to
-         hash values, and the second maps hash values to variable names.
-    """
-
-    var_to_hash = {}
-    hash_to_var = {}
-    name_list = [get_name(x) for x in items_to_hash]
-    for thing in sorted(name_list, key=len, reverse=True):
-        # ensure the hash value is positive so the minus sign isn't confused as part of the equation
-        hashkey = str(hash(thing) ** 2)
-        var_to_hash[thing] = hashkey
-        hash_to_var[hashkey] = thing
-
-    return var_to_hash, hash_to_var
-
-
-def substitute_equation_from_dict(eq_str: str, hash_dict: dict[str, str]) -> str:
-    """
-    This function substitutes variables in an equation string with their corresponding values from a dictionary.
-
-    Parameters
-    ----------
-    eq_str : str
-        The equation string containing variables to be replaced.
-    hash_dict : Dict[str, str]
-        A dictionary mapping variables to their corresponding values.
-
-    Returns
-    -------
-    str
-        The equation string with the variables replaced by their values.
-    """
-    # tokens = eq_str.split()
-    # hash_tokens = [hash_dict.get(x) for x in tokens]
-    # return ' '.join(hash_tokens)
-
-    for key, value in hash_dict.items():
-        eq_str = eq_str.replace(key, value)
-    return eq_str
-
-
-def make_var_to_matlab_sub_dict(
-    var_list: list[str | TimeAwareSymbol | sp.Symbol], clash_prefix: str = "a"
-) -> dict[str | TimeAwareSymbol | sp.Symbol, str]:
-    """
-    This function builds a dictionary that maps variables to their corresponding names that
-    can be used in a Matlab script.
-
-    Parameters
-    ----------
-    var_list : List[Union[str, TimeAwareSymbol, sp.Symbol]]
-        A list of variables to be mapped. Can contain strings, TimeAwareSymbol objects,
-         or sp.Symbol objects.
-    clash_prefix : str, optional
-        A prefix to add to the names of variables that might clash with Matlab keywords
-        (e.g. greek letters). Default is 'a'.
-
-    Returns
-    -------
-    Dict[Union[str, TimeAwareSymbol, sp.Symbol], str]
-        A dictionary mapping the variables in `var_list` to their corresponding
-        names that can be used in a Matlab script.
-
-    Examples
-    --------
-    .. code-block:: py
-        make_var_to_matlab_sub_dict([sp.Symbol('beta')])
-        # {sp.Symbol('beta'): 'abeta'}
-    """
-
-    sub_dict = {}
-
-    for var in var_list:
-        if isinstance(var, str):
-            var_name = var if var.lower() not in greeks else clash_prefix + var
-        elif isinstance(var, TimeAwareSymbol):
-            var_name = (
-                var.base_name
-                if var.base_name.lower() not in greeks
-                else clash_prefix + var.base_name
-            )
-            time_index = var.safe_name.split("_")[-1]
-            var_name += f"_{time_index}"
-        elif isinstance(var, sp.Symbol):
-            var_name = (
-                var.name if var.name.lower() not in greeks else clash_prefix + var.name
-            )
-        else:
-            raise ValueError(
-                "var_list should contain only strings, symbols, or TimeAwareSymbols"
-            )
-
-        sub_dict[var] = var_name
-
-    return sub_dict
-
-
-def convert_var_timings_to_matlab(var_list: list[str]) -> list[str]:
-    """
-    This function converts the timing notation in a list of variable names to a
-    form that can be used in a Dynare mod file.
-
-    Parameters
-    ----------
-    var_list : list of str
-        A list of variable names with "mathematical" timing notation (e.g. '_t+1', '_t-1', '_t').
-
-    Returns
-    -------
-    list of str
-        A list of variable names with the timing notation converted to a
-        form that can be used in a Dynare mod file (e.g. '(1)', '(-1)', '').
-    """
-    matlab_var_list = [
-        var.replace("_t+1", "(1)").replace("_t-1", "(-1)").replace("_t", "")
-        for var in var_list
-    ]
-
-    return matlab_var_list
-
-
-def write_lines_from_list(
-    items_to_write: list[str], file: str, line_start: str = "", line_max: int = 50
-) -> str:
-    """
-    This function writes a list of items to a string, inserting line
-    breaks at a specified maximum line length.
-
-    Parameters
-    ----------
-    items_to_write : list of strings
-        A list of items to be written to the string.
-    file : str
-        A string to which the items will be appended.
-    line_start : str, optional
-        A string to be prepended to each line. Default is an empty string.
-    line_max : int, optional
-        The maximum line length. Default is 50.
-
-    Returns
-    -------
-    str
-        The modified `file` string with the items from `l` appended to it.
-    """
-
-    line = line_start
-    for item in sorted(items_to_write):
-        line += f" {item},"
-        if len(line) > line_max:
-            line = line[:-1]
-            line = line + ";\n"
-            file += line
-            line = line_start
-
-    if line != line_start:
-        line = line[:-1]
-        file += line + ";\n"
-
-    return file
-
-
-UNDER_T_PATTERN = r"_t(?=[^\w]|$)"
-
-
-def make_mod_file(model) -> str:
-    """
-    This function generates a string representation of a Dynare model file for
-    a dynamic stochastic general equilibrium (DSGE) model. For more information,
-    see [1].
-
-    Parameters
-    ----------
-    model : gEconModel
-        A gEconModel object with solved steady state.
-
-    Returns
-    -------
-    str
-        A string representation of a Dynare model file.
-
-    References
-    ----------
-    ..[1] Adjemian, Stéphane, et al. "Dynare: Reference manual, version 4." (2011).
-
-    TODO: This function needs a lot of work, including:
-        - Output deterministics as # declarations
-        - Output priors
-        - Add a flag for linear models
-        - Output user-provided steady state equations
-        - Check that the steady state has been solved
-    """
-
-    var_list = model.variables.copy()
-    param_dict = model.free_param_dict | model.calib_param_dict
-
-    shocks = model.shocks
-    ss_value_dict = model.steady_state_dict.copy()
-
-    var_to_matlab = make_var_to_matlab_sub_dict(
-        make_all_var_time_combos(var_list), clash_prefix="var_"
-    )
-    par_to_matlab = make_var_to_matlab_sub_dict(
-        param_dict.keys(), clash_prefix="param_"
-    )
-    shock_to_matlab = make_var_to_matlab_sub_dict(shocks, clash_prefix="exog_")
-
-    items_to_hash = (
-        list(var_to_matlab.keys())
-        + list(par_to_matlab.keys())
-        + list(shock_to_matlab.keys())
-    )
-
-    file = ""
-    file = write_lines_from_list(
-        [re.sub(UNDER_T_PATTERN, "", var_to_matlab[x]) for x in model.variables],
-        file,
-        line_start="var",
-    )
-    file = write_lines_from_list(
-        [re.sub(UNDER_T_PATTERN, "", x) for x in shock_to_matlab.values()],
-        file,
-        line_start="varexo",
-    )
-    file += "\n"
-    file = write_lines_from_list(
-        list(par_to_matlab.values()), file, line_start="parameters"
-    )
-    file += "\n"
-
-    for model_param in sorted(param_dict.keys()):
-        matlab_param = par_to_matlab[model_param]
-        value = param_dict[model_param]
-        file += f"{matlab_param} = {value};\n"
-
-    file += "\n"
-    file += "model;\n"
-    for var, val in ss_value_dict.items():
-        if var in var_to_matlab.keys():
-            matlab_var = var_to_matlab[var]
-            file += f"#{matlab_var}_ss = {val:0.4f};\n"
-
-    for eq in model.system_equations:
-        matlab_subdict = {}
-
-        for atom in eq.atoms():
-            if not isinstance(atom, TimeAwareSymbol) and isinstance(
-                atom, sp.core.Symbol
-            ):
-                if atom in par_to_matlab.keys():
-                    matlab_subdict[atom] = sp.Symbol(par_to_matlab[atom])
-            elif isinstance(atom, TimeAwareSymbol):
-                if atom in var_to_matlab.keys():
-                    matlab_subdict[atom] = var_to_matlab[atom]
-                elif atom in shock_to_matlab.keys():
-                    matlab_subdict[atom] = shock_to_matlab[atom]
-
-        eq_str = eq.subs(matlab_subdict)
-        eq_str = str(eq_str)
-        prepare_eq = eq_str.replace("**", "^")
-        var_to_hash, hash_to_var = build_hash_table(items_to_hash)
-
-        hash_eq = substitute_equation_from_dict(prepare_eq, var_to_hash)
-
-        for operator in OPERATORS:
-            hash_eq = hash_eq.replace(operator, " " + operator + " ")
-        hash_eq = re.sub(" +", " ", hash_eq)
-        hash_eq = hash_eq.strip()
-        final_eq = substitute_equation_from_dict(hash_eq, hash_to_var)
-
-        matlab_eq = final_eq.replace("_tp1", "(1)").replace("_tm1", "(-1)")
-        split_eq = matlab_eq.split(" ")
-
-        new_eq = []
-        for atom in split_eq:
-            if atom in par_to_matlab.keys():
-                atom = par_to_matlab[atom]
-            elif atom in var_to_matlab.keys():
-                atom = var_to_matlab[atom]
-            elif atom in shock_to_matlab.keys():
-                atom = shock_to_matlab[atom]
-
-            new_eq.append(atom)
-
-        matlab_eq = ""
-        for i, atom in enumerate(new_eq):
-            if i == 0:
-                matlab_eq += atom
-            elif i == 1 and new_eq[0] == "-":
-                matlab_eq += atom
-            else:
-                if atom in "()":
-                    matlab_eq += atom
-                elif new_eq[i - 1] in "(":
-                    matlab_eq += atom
-                else:
-                    matlab_eq += " " + atom
-        matlab_eq += " = 0;"
-        matlab_eq = re.sub(UNDER_T_PATTERN, "", matlab_eq)
-
-        file += matlab_eq + "\n"
-
-    file += "end;\n\n"
-
-    file += "initval;\n"
-    for var, val in ss_value_dict.to_sympy().items():
-        matlab_var = var_to_matlab[var].replace("_ss", "")
-        file += f"{matlab_var} = {val:0.4f};\n"
-
-    file += "end;\n\n"
-    file += "steady;\n"
-    file += "check(qz_zero_threshold=1e-20);\n\n"
-
-    file += "shocks;\n"
-    for shock in shocks:
-        file += "var " + re.sub(UNDER_T_PATTERN, "", shock_to_matlab[shock]) + ";\n"
-        file += "stderr 0.01;\n"
-    file += "end;\n\n"
-    file += "stoch_simul(order=1, irf=100, qz_zero_threshold=1e-20);"
-
-    return file
diff --git a/gEconpy/shared/statsmodel_convert.py b/gEconpy/shared/statsmodel_convert.py
deleted file mode 100644
index b181ece..0000000
--- a/gEconpy/shared/statsmodel_convert.py
+++ /dev/null
@@ -1,716 +0,0 @@
-from collections.abc import Callable
-
-import numpy as np
-import pandas as pd
-from statsmodels.tsa.statespace.kalman_filter import INVERT_UNIVARIATE, SOLVE_LU
-from statsmodels.tsa.statespace.mlemodel import MLEModel, _handle_args
-
-from gEconpy.classes.transformers import IdentityTransformer, PositiveTransformer
-
-
-def compile_to_statsmodels(model) -> MLEModel:
-    """
-    Compile a gEconModel object into a Statsmodels MLEModel object.
-
-    Statsmodels includes a full suite of tools for solving and fitting linear state space
-    models via Maximum Likelihood. This function takes a solved gEconpy model object
-    and uses it to implement a `statsmodels.tsa.statespace` state space model.
-
-    Parameters
-    ----------
-    model : gEconModel
-        A gEconModel object to be compiled into a Statsmodels MLEModel object.
-
-    Returns
-    -------
-    MLEModel
-        A Statsmodels MLEModel object compiled from the gEconModel object.
-
-    """
-
-    class DSGEModel(MLEModel):
-        def __init__(
-            self,
-            data: pd.DataFrame,
-            initialization: str,
-            param_start_dict: dict[str, float],
-            shock_start_dict: dict[str, float],
-            noise_start_dict: dict[str, float] | None = None,
-            param_transforms: dict[str, Callable] | None = None,
-            shock_transforms: dict[str, Callable] | None = None,
-            noise_transforms: dict[str, Callable] | None = None,
-            x0: np.ndarray | None = None,
-            P0: np.ndarray | None = None,
-            fit_MAP: bool = False,
-            **kwargs,
-        ):
-            """
-            Create a DSGEModel object for maximum-likelihood estimation, subclassed from
-            `statsmodels.tsa.statespace.MLEModel`.
-
-            Parameters
-            ----------
-            model: A DSGE model object
-                The model object to be used to create the DSGEModel
-            data: pd.DataFrame
-                A pandas DataFrame containing the data to be used for estimation
-            initialization: string
-                The type of Kalman filter initialization to use.  One of 'approximate_diffuse',
-                'stationary', 'known', 'fixed', 'diffuse' or 'none'
-            param_start_dict: dict
-                A dictionary of parameter starting values, where keys are parameter names
-                and values are floats. Parameters not included this dictionary will not be
-                estimated when `.fit()`. is called.
-            shock_start_dict: dict
-                A dictionary of shock variance starting values, where keys are shock names and values
-                are floats. All shocks not include in this dictionary will be dropped from the model
-                when `.fit()` is called.
-            noise_start_dict: dict, optional
-                A dictionary of observation noise starting values, where keys are observed state names
-                and values are floats. Default is zero for all observed variables.
-            param_transforms: dict, optional
-                A dictionary of functions to transform parameters before they are passed to the likelihood
-                function.  Keys are parameter names, values are functions. Default is the identity
-                function for all parameters.
-            shock_transforms: dict, optional
-                A dictionary of functions to transform shock variance terms before they are passed to
-                the likelihood function.  Keys are shock names, values are functions. Default is
-                the square function for all variances.
-            noise_transforms: dict, optional
-                A dictionary of functions to transform observation noise variances before they are
-                 passed to the likelihood function.  Keys are noise state names, values are
-                  functions. Default is the square function for all variannces.
-            x0: array_like, optional
-                A 1-d array of starting values for the state vector
-            P0: array_like, optional
-                A 2-d array of starting values for the state covariance matrix
-            fit_MAP: bool, optional
-                If True, fit the model in maximum a posteriori (MAP) sense rather than maximum
-                likelihood sense.  Defaults to False.
-            kwargs:
-                Additional arguments to pass to the MLEModel constructor
-            """
-            k_states = model.n_variables
-            k_observed = data.shape[1]
-            k_posdef = model.n_shocks
-
-            noise_start_dict = noise_start_dict or {}
-
-            self.model = model
-            self.data = data
-            self.fit_MAP = fit_MAP
-
-            self.shock_names = [x.base_name for x in self.model.shocks]
-            self.dsge_params = list(model.free_param_dict.keys())
-
-            param_priors = self.model.param_priors.copy()
-            shock_priors = self.model.shock_priors.copy()
-            noise_priors = self.model.observation_noise_priors.copy()
-
-            self.prior_dict = param_priors.copy()
-            self.prior_dict.update(
-                {k: d.rv_params["scale"] for k, d in shock_priors.items()}
-            )
-            self.prior_dict.update(noise_priors)
-
-            n_shocks = len(self.shock_names)
-
-            self.params_to_estimate = list(param_start_dict.keys())
-            self.shocks_to_estimate = list(shock_start_dict.keys())
-            self.noisy_states = list(noise_start_dict.keys())
-
-            self.start_dict = param_start_dict.copy()
-            self.start_dict.update(shock_start_dict)
-            self.start_dict.update(noise_start_dict)
-
-            self._validate_start_dict(
-                param_start_dict, shock_start_dict, noise_start_dict
-            )
-            self._build_transform_dict(
-                param_transforms, shock_transforms, noise_transforms
-            )
-            self._validate_priors(param_priors, shock_priors, noise_priors)
-
-            super().__init__(
-                endog=data,
-                k_states=k_states,
-                k_posdef=k_posdef,
-                initialization=initialization,
-                **kwargs,
-            )
-
-            model_names = [x.base_name for x in model.variables]
-            missing_vars = [x for x in data.columns if x not in model_names]
-            if any(missing_vars):
-                msg = "Data contains the following columns not associated with variables in the model:"
-                msg += ", ".join(missing_vars)
-                raise ValueError(msg)
-
-            Z_idx = [model_names.index(x) for x in data.columns if x in model_names]
-
-            self.ssm["design"][np.arange(k_observed), Z_idx] = 1
-            self.ssm["state_cov"] = np.eye(k_posdef) * 0.1
-            self.ssm["obs_cov"] = np.zeros((k_observed, k_observed))
-
-            self.state_cov_idxs = (
-                np.arange(n_shocks, dtype="int"),
-                np.arange(n_shocks, dtype="int"),
-            )
-            self.obs_cov_idxs = np.where(np.isin(data.columns, self.noisy_states))
-
-        def _validate_start_dict(
-            self,
-            param_start_dict: dict[str, float],
-            shock_start_dict: dict[str, float],
-            noise_start_dict: dict[str, float],
-        ) -> None:
-            """
-            Validate that all the parameters, shocks, and observation noises that are supposed to be
-             estimated have starting values, and that any starting values provided correspond to
-             parameters, shocks, or observation noises that exist in the model or data.
-
-            Parameters
-            ----------
-            param_start_dict: Dict[str, float]
-                A dictionary of starting values for parameters that are to be estimated.
-            shock_start_dict: Dict[str, float]
-                A dictionary of starting values for shocks that are to be estimated.
-            noise_start_dict: Dict[str, float]
-                A dictionary of starting values for observation noises that are to be estimated.
-            """
-            missing_vars = [
-                x for x in self.params_to_estimate if x not in param_start_dict.keys()
-            ]
-            missing_shocks = [
-                x for x in self.shocks_to_estimate if x not in shock_start_dict.keys()
-            ]
-            missing_noise = [
-                x for x in self.noisy_states if x not in noise_start_dict.keys()
-            ]
-            msg = (
-                "The following {} to be estimated were not assigned a starting value: "
-            )
-
-            if any(missing_vars):
-                raise ValueError(msg.format("parameters") + ", ".join(missing_vars))
-
-            if any(missing_shocks):
-                raise ValueError(msg.format("shocks") + ", ".join(missing_shocks))
-
-            if any(missing_noise):
-                raise ValueError(
-                    msg.format("observation noises") + ", ".join(missing_noise)
-                )
-
-            extra_vars = [
-                x
-                for x in param_start_dict.keys()
-                if x not in self.model.free_param_dict.keys()
-            ]
-            extra_shocks = [
-                x
-                for x in shock_start_dict.keys()
-                if x not in [x.base_name for x in self.model.shocks]
-            ]
-            extra_noise = [
-                x for x in noise_start_dict.keys() if x not in self.data.columns
-            ]
-
-            msg = "The following {} were given starting values, but did not appear in the {}: "
-            if any(extra_vars):
-                raise ValueError(
-                    msg.format("parameters", "model definition") + ", ".join(extra_vars)
-                )
-
-            if any(extra_shocks):
-                raise ValueError(
-                    msg.format("shocks", "model definition") + ", ".join(missing_shocks)
-                )
-
-            if any(extra_noise):
-                raise ValueError(
-                    msg.format("observation noises", "data") + ", ".join(missing_noise)
-                )
-
-        def _build_transform_dict(
-            self, param_transforms, shock_transforms, noise_transforms
-        ):
-            self.transform_dict = {}
-            for param in self.params_to_estimate:
-                if param in param_transforms.keys():
-                    self.transform_dict[param] = param_transforms[param]
-                else:
-                    print(
-                        f"Parameter {param} was not assigned a transformation, assigning IdentityTransform"
-                    )
-                    self.transform_dict[param] = IdentityTransformer()
-
-            if shock_transforms is None:
-                self.transform_dict.update(
-                    {k: PositiveTransformer() for k in self.shocks_to_estimate}
-                )
-            else:
-                for shock in self.shocks_to_estimate:
-                    if shock in shock_transforms.keys():
-                        self.transform_dict[shock] = shock_transforms[shock]
-                    else:
-                        print(
-                            f"Shock {shock} was not assigned a transformation, assigning IdentityTransform"
-                        )
-                        self.transform_dict[shock] = IdentityTransformer()
-
-            if noise_transforms is None:
-                self.transform_dict.update(
-                    {k: PositiveTransformer() for k in self.noisy_states}
-                )
-            else:
-                for noise in self.noisy_states:
-                    if noise in noise_transforms.keys():
-                        self.transform_dict[noise] = noise_transforms[noise]
-                    else:
-                        print(
-                            f"Noise for state {noise} was not assigned a transformation, assigning IdentityTransform"
-                        )
-                        self.transform_dict[noise] = IdentityTransformer()
-
-        def _validate_priors(self, param_priors, shock_priors, noise_priors):
-            if not self.fit_MAP:
-                return
-
-            missing_vars = [
-                x for x in self.params_to_estimate if x not in param_priors.keys()
-            ]
-            missing_shocks = [
-                x for x in self.shocks_to_estimate if x not in shock_priors.keys()
-            ]
-            missing_noise = [
-                x for x in self.noisy_states if x not in noise_priors.keys()
-            ]
-            msg = "The following {} to be estimated were not assigned a prior: "
-            if any(missing_vars):
-                raise ValueError(msg.format("parameters") + ", ".join(missing_vars))
-
-            if any(missing_shocks):
-                raise ValueError(msg.format("shocks") + ", ".join(missing_shocks))
-
-            if any(missing_noise):
-                raise ValueError(
-                    msg.format("observation noises") + ", ".join(missing_noise)
-                )
-
-        @property
-        def param_names(self):
-            shock_names = [f"sigma2.{x}" for x in self.shocks_to_estimate]
-            noise_names = [f"sigma2.{x}" for x in self.noisy_states]
-            return self.params_to_estimate + shock_names + noise_names
-
-        @property
-        def external_param_names(self):
-            return self.params_to_estimate + self.shocks_to_estimate + self.noisy_states
-
-        @property
-        def state_names(self):
-            return [x.base_name for x in self.model.variables]
-
-        @property
-        def start_params(self):
-            param_names = self.external_param_names
-            start_params = []
-
-            for name in param_names:
-                start_params.append(self.start_dict[name])
-            return np.array(start_params)
-
-        def unpack_statespace(self):
-            T = np.ascontiguousarray(self.ssm["transition"])
-            Z = np.ascontiguousarray(self.ssm["design"])
-            R = np.ascontiguousarray(self.ssm["selection"])
-            H = np.ascontiguousarray(self.ssm["obs_cov"])
-            Q = np.ascontiguousarray(self.ssm["state_cov"])
-
-            return T, Z, R, H, Q
-
-        def transform_params(self, real_line_params):
-            """
-            Take in optimizer values on R and map them into parameter space.
-
-            Example: variances must be positive, so apply x ** 2.
-            """
-            param_space_params = np.zeros_like(real_line_params)
-            for i, (name, param) in enumerate(
-                zip(self.external_param_names, real_line_params)
-            ):
-                param_space_params[i] = self.transform_dict[name].constrain(param)
-
-            return param_space_params
-
-        def untransform_params(self, param_space_params):
-            """
-            Take in parameters living in the parameter space and apply an "inverse transform"
-            to put them back to where the optimizer's last guess was.
-
-            Example: We applied x ** 2 to ensure x is positive, apply x ** (1 / 2).
-            """
-            real_line_params = np.zeros_like(param_space_params)
-            for i, (name, param) in enumerate(
-                zip(self.external_param_names, param_space_params)
-            ):
-                real_line_params[i] = self.transform_dict[name].unconstrain(param)
-
-            return real_line_params
-
-        def make_param_update_dict(self, params):
-            shock_names = self.shock_names
-            dsge_params = self.dsge_params
-            param_names = self.external_param_names
-
-            param_update_dict = {}
-            shock_params = []
-            observation_noise_params = []
-
-            for name, param in zip(param_names, params):
-                if name in dsge_params:
-                    param_update_dict[name] = param
-                elif name in shock_names:
-                    shock_params.append(param)
-                else:
-                    observation_noise_params.append(param)
-
-            return (
-                param_update_dict,
-                np.array(shock_params),
-                np.array(observation_noise_params),
-            )
-
-        def update(self, params, **kwargs):
-            params = super().update(params, **kwargs)
-
-            update_dict, shock_params, obs_params = self.make_param_update_dict(params)
-            # original_params = model.free_param_dict.copy()
-
-            self.model.free_param_dict.update(update_dict)
-            try:
-                self.model.steady_state(verbose=False)
-                self.model.solve_model(verbose=False)
-                pert_success = True
-            except Exception:
-                pert_success = False
-
-            try:
-                condition_satisfied = model.check_bk_condition(
-                    verbose=False, return_value="bool"
-                )
-            except Exception:
-                condition_satisfied = False
-
-            self.ssm["transition"] = self.model.T.values
-            self.ssm["selection"] = self.model.R.values
-
-            cov_idx = self.state_cov_idxs
-            self.ssm["state_cov", cov_idx, cov_idx] = shock_params
-
-            obs_idx = self.obs_cov_idxs
-            self.ssm["obs_cov", obs_idx, obs_idx] = obs_params
-
-            return pert_success & condition_satisfied
-
-        def loglike(self, params, *args, **kwargs):
-            """
-            Loglikelihood evaluation
-
-            Parameters
-            ----------
-            params : array_like
-                Array of parameters at which to evaluate the loglikelihood
-                function.
-            transformed : bool, optional
-                Whether or not `params` is already transformed. Default is True.
-            **kwargs
-                Additional keyword arguments to pass to the Kalman filter. See
-                `KalmanFilter.filter` for more details.
-
-            See Also
-            --------
-            update : modifies the internal state of the state space model to
-                     reflect new params
-
-            Notes
-            -----
-            [1]_ recommend maximizing the average likelihood to avoid scale issues;
-            this is done automatically by the base Model fit method.
-
-            References
-            ----------
-            .. [1] Koopman, Siem Jan, Neil Shephard, and Jurgen A. Doornik. 1999.
-               Statistical Algorithms for Models in State Space Using SsfPack 2.2.
-               Econometrics Journal 2 (1): 107-60. doi:10.1111/1368-423X.00023.
-            """
-            transformed, includes_fixed, complex_step, kwargs = _handle_args(
-                MLEModel._loglike_param_names,
-                MLEModel._loglike_param_defaults,
-                *args,
-                **kwargs,
-            )
-
-            params = self.handle_params(
-                params, transformed=transformed, includes_fixed=includes_fixed
-            )
-            success = self.update(
-                params,
-                transformed=transformed,
-                includes_fixed=includes_fixed,
-                complex_step=complex_step,
-            )
-
-            if complex_step:
-                kwargs["inversion_method"] = INVERT_UNIVARIATE | SOLVE_LU
-
-            if success:
-                loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)
-                if self.fit_MAP:
-                    for name, param in zip(self.external_param_names, params):
-                        loglike += max(-1e6, self.prior_dict[name].logpdf(param))
-
-            else:
-                # If the parameters are invalid, return a large negative number
-                loglike = -1e6
-
-            # Koopman, Shephard, and Doornik recommend maximizing the average
-            # likelihood to avoid scale issues, but the averaging is done
-            # automatically in the base model `fit` method
-            return loglike
-
-        def loglikeobs(
-            self,
-            params,
-            transformed=True,
-            includes_fixed=False,
-            complex_step=False,
-            **kwargs,
-        ):
-            """
-            Loglikelihood evaluation
-
-            Parameters
-            ----------
-            params : array_like
-                Array of parameters at which to evaluate the loglikelihood
-                function.
-            transformed : bool, optional
-                Whether or not `params` is already transformed. Default is True.
-            **kwargs
-                Additional keyword arguments to pass to the Kalman filter. See
-                `KalmanFilter.filter` for more details.
-
-            See Also
-            --------
-            update : modifies the internal state of the Model to reflect new params
-
-            Notes
-            -----
-            [1]_ recommend maximizing the average likelihood to avoid scale issues;
-            this is done automatically by the base Model fit method.
-
-            References
-            ----------
-            .. [1] Koopman, Siem Jan, Neil Shephard, and Jurgen A. Doornik. 1999.
-               Statistical Algorithms for Models in State Space Using SsfPack 2.2.
-               Econometrics Journal 2 (1): 107-60. doi:10.1111/1368-423X.00023.
-            """
-            params = self.handle_params(
-                params, transformed=transformed, includes_fixed=includes_fixed
-            )
-
-            # If we're using complex-step differentiation, then we cannot use
-            # Cholesky factorization
-            if complex_step:
-                kwargs["inversion_method"] = INVERT_UNIVARIATE | SOLVE_LU
-
-            success = self.update(
-                params,
-                transformed=transformed,
-                includes_fixed=includes_fixed,
-                complex_step=complex_step,
-            )
-
-            if success:
-                ll_obs = self.ssm.loglikeobs(complex_step=complex_step, **kwargs)
-                if self.fit_MAP:
-                    for name, param in zip(self.external_param_names, params):
-                        ll_obs += (
-                            max(-1e6, self.prior_dict[name].logpdf(param)) / self.nobs
-                        )
-                return ll_obs
-
-            else:
-                # Large negative likelihood for all observations if the parameters are invalid
-                return np.full(self.endog.shape[0], -1e6)
-
-        def fit(
-            self,
-            start_params=None,
-            transformed=True,
-            includes_fixed=False,
-            cov_type=None,
-            cov_kwds=None,
-            method="lbfgs",
-            maxiter=50,
-            full_output=1,
-            disp=5,
-            callback=None,
-            return_params=False,
-            optim_score=None,
-            optim_complex_step=None,
-            optim_hessian=None,
-            flags=None,
-            low_memory=False,
-            **kwargs,
-        ):
-            """
-            Fits the model by maximum likelihood via Kalman filter.
-
-            Parameters
-            ----------
-            start_params : array_like, optional
-                Initial guess of the solution for the loglikelihood maximization.
-                If None, the default is given by Model.start_params.
-            transformed : bool, optional
-                Whether or not `start_params` is already transformed. Default is
-                True.
-            includes_fixed : bool, optional
-                If parameters were previously fixed with the `fix_params` method,
-                this argument describes whether or not `start_params` also includes
-                the fixed parameters, in addition to the free parameters. Default
-                is False.
-            cov_type : str, optional
-                The `cov_type` keyword governs the method for calculating the
-                covariance matrix of parameter estimates. Can be one of:
-
-                - 'opg' for the outer product of gradient estimator
-                - 'oim' for the observed information matrix estimator, calculated
-                  using the method of Harvey (1989)
-                - 'approx' for the observed information matrix estimator,
-                  calculated using a numerical approximation of the Hessian matrix.
-                - 'robust' for an approximate (quasi-maximum likelihood) covariance
-                  matrix that may be valid even in the presence of some
-                  misspecifications. Intermediate calculations use the 'oim'
-                  method.
-                - 'robust_approx' is the same as 'robust' except that the
-                  intermediate calculations use the 'approx' method.
-                - 'none' for no covariance matrix calculation.
-
-                Default is 'opg' unless memory conservation is used to avoid
-                computing the loglikelihood values for each observation, in which
-                case the default is 'approx'.
-            cov_kwds : dict or None, optional
-                A dictionary of arguments affecting covariance matrix computation.
-
-                **opg, oim, approx, robust, robust_approx**
-
-                - 'approx_complex_step' : bool, optional - If True, numerical
-                  approximations are computed using complex-step methods. If False,
-                  numerical approximations are computed using finite difference
-                  methods. Default is True.
-                - 'approx_centered' : bool, optional - If True, numerical
-                  approximations computed using finite difference methods use a
-                  centered approximation. Default is False.
-            method : str, optional
-                The `method` determines which solver from `scipy.optimize`
-                is used, and it can be chosen from among the following strings:
-
-                - 'newton' for Newton-Raphson
-                - 'nm' for Nelder-Mead
-                - 'bfgs' for Broyden-Fletcher-Goldfarb-Shanno (BFGS)
-                - 'lbfgs' for limited-memory BFGS with optional box constraints
-                - 'powell' for modified Powell's method
-                - 'cg' for conjugate gradient
-                - 'ncg' for Newton-conjugate gradient
-                - 'basinhopping' for global basin-hopping solver
-
-                The explicit arguments in `fit` are passed to the solver,
-                with the exception of the basin-hopping solver. Each
-                solver has several optional arguments that are not the same across
-                solvers. See the notes section below (or scipy.optimize) for the
-                available arguments and for the list of explicit arguments that the
-                basin-hopping solver supports.
-            maxiter : int, optional
-                The maximum number of iterations to perform.
-            full_output : bool, optional
-                Set to True to have all available output in the Results object's
-                mle_retvals attribute. The output is dependent on the solver.
-                See LikelihoodModelResults notes section for more information.
-            disp : bool, optional
-                Set to True to print convergence messages.
-            callback : callable callback(xk), optional
-                Called after each iteration, as callback(xk), where xk is the
-                current parameter vector.
-            return_params : bool, optional
-                Whether or not to return only the array of maximizing parameters.
-                Default is False.
-            optim_score : {'harvey', 'approx'} or None, optional
-                The method by which the score vector is calculated. 'harvey' uses
-                the method from Harvey (1989), 'approx' uses either finite
-                difference or complex step differentiation depending upon the
-                value of `optim_complex_step`, and None uses the built-in gradient
-                approximation of the optimizer. Default is None. This keyword is
-                only relevant if the optimization method uses the score.
-            optim_complex_step : bool, optional
-                Whether or not to use complex step differentiation when
-                approximating the score; if False, finite difference approximation
-                is used. Default is True. This keyword is only relevant if
-                `optim_score` is set to 'harvey' or 'approx'.
-            optim_hessian : {'opg','oim','approx'}, optional
-                The method by which the Hessian is numerically approximated. 'opg'
-                uses outer product of gradients, 'oim' uses the information
-                matrix formula from Harvey (1989), and 'approx' uses numerical
-                approximation. This keyword is only relevant if the
-                optimization method uses the Hessian matrix.
-            low_memory : bool, optional
-                If set to True, techniques are applied to substantially reduce
-                memory usage. If used, some features of the results object will
-                not be available (including smoothed results and in-sample
-                prediction), although out-of-sample forecasting is possible.
-                Default is False.
-            **kwargs
-                Additional keyword arguments to pass to the optimizer.
-
-            Returns
-            -------
-            results
-                Results object holding results from fitting a state space model.
-
-            See Also
-            --------
-            statsmodels.base.model.LikelihoodModel.fit
-            statsmodels.tsa.statespace.mlemodel.MLEResults
-            statsmodels.tsa.statespace.structural.UnobservedComponentsResults
-            """
-
-            # Disable complex step approximations by default
-            optim_complex_step = optim_complex_step or False
-            cov_kwds = cov_kwds or {
-                "approx_complex_step": False,
-                "approx_centered": True,
-            }
-
-            return super().fit(
-                start_params=start_params,
-                transformed=transformed,
-                includes_fixed=includes_fixed,
-                cov_type=cov_type,
-                cov_kwds=cov_kwds,
-                method=method,
-                maxiter=maxiter,
-                full_output=full_output,
-                disp=disp,
-                callback=callback,
-                return_params=return_params,
-                optim_score=optim_score,
-                optim_complex_step=optim_complex_step,
-                optim_hessian=optim_hessian,
-                flags=flags,
-                low_memory=low_memory,
-                **kwargs,
-            )
-
-    return DSGEModel
diff --git a/gEconpy/solvers/cycle_reduction.py b/gEconpy/solvers/cycle_reduction.py
index 0bac2bd..e2e752a 100644
--- a/gEconpy/solvers/cycle_reduction.py
+++ b/gEconpy/solvers/cycle_reduction.py
@@ -1,17 +1,28 @@
+import numba as nb
 import numpy as np
-from numba import njit
-from numpy.typing import ArrayLike
+import pytensor
+import pytensor.tensor as pt
 
+from pytensor.compile import get_mode
+from pytensor.compile.builders import OpFromGraph
+from pytensor.graph import Apply, Op
 
-@njit(cache=True)
-def cycle_reduction(
-    A0: ArrayLike,
-    A1: ArrayLike,
-    A2: ArrayLike,
+from gEconpy.model.perturbation import _log
+from gEconpy.solvers.shared import (
+    o1_policy_function_adjoints,
+    pt_compute_selection_matrix,
+    stabilize,
+)
+
+
+@nb.jit(cache=True)
+def nb_cycle_reduction(
+    A0: np.ndarray,
+    A1: np.ndarray,
+    A2: np.ndarray,
     max_iter: int = 1000,
     tol: float = 1e-7,
-    verbose: bool = True,
-) -> tuple[ArrayLike | None, str, float]:
+) -> tuple[np.ndarray | None, np.ndarray | None, str, float]:
     """
     Solve quadratic matrix equation of the form $A0x^2 + A1x + A2 = 0$ via cycle reduction algorithm of [1].
     Useful in the DSGE context to solve for the implicit derivative of the policy function, g, with respect to
@@ -35,14 +46,21 @@ def cycle_reduction(
         Maximum number of iterations to perform before giving up.
     tol: float, default: 1e-7
         Floating point tolerance used to detect algorithmic convergence
-    verbose: bool, default: True
-        If true, prints the sum of squared residuals that result when the system is computed used the solution.
 
     Returns
     -------
+    X: array
+        Solution to matrix quadratic equation
+    res: array
+        Residual of the matrix quadratic equation, or None if the algorithm fails to converge
+    result: str
+        String indicating the result of the optimization. If the algorithm converges, this will be "Optimization
+        successful". If the algorithm fails to converge, this will be "Iteration on all matrices failed to converged"
+    log_norm: float
+        Logarithm of the L1 norm of the matrix A1. This is useful for diagnosing the success of the algorithm.
 
     References
-    -------
+    ----------
     ..[1] D.A. Bini, G. Latouche, B. Meini (2002), "Solving matrix polynomial equations
           arising in queueing problems", Linear Algebra and its Applications 340, pp. 222-244
     ..[2]
@@ -51,23 +69,20 @@ def cycle_reduction(
     result = "Optimization successful"
     log_norm = 0
     X = None
+    res = None
 
     A0_initial = A0.copy()
     A1_hat = A1.copy()
 
-    if verbose:
-        A1_initial = A1.copy()
-        A2_initial = A2.copy()
+    A1_initial = A1.copy()
+    A2_initial = A2.copy()
 
     n, _ = A0.shape
     idx_0 = np.arange(n)
     idx_1 = idx_0 + n
 
-    # Pre-allocate this so it doesn't have to be repeatedly created
-    EYE = np.eye(A1.shape[0])
-
-    for i in range(max_iter):
-        tmp = np.vstack((A0, A2)) @ np.linalg.solve(A1, EYE) @ np.hstack((A0, A2))
+    for i in range(int(max_iter)):
+        tmp = np.vstack((A0, A2)) @ np.linalg.solve(A1, np.hstack((A0, A2)))
 
         A1 = A1 - tmp[idx_0, :][:, idx_1] - tmp[idx_1, :][:, idx_0]
         A0 = -tmp[idx_0, :][:, idx_0]
@@ -90,19 +105,16 @@ def cycle_reduction(
                 result = "Iteration on all matrices failed to converged"
                 log_norm = np.log(np.linalg.norm(A1, 1))
 
-            return X, result, log_norm
+            return X, res, result, log_norm
 
     X = -np.linalg.solve(A1_hat, A0_initial)
+    res = A0_initial + A1_initial @ X + A2_initial @ X @ X
 
-    if verbose:
-        res = A0_initial + A1_initial @ X + A2_initial @ X @ X
-        print("Solution found, sum of squared residuals: ", (res**2).sum())
+    return X, res, result, log_norm
 
-    return X, result, log_norm
 
-
-@njit(cache=True)
-def solve_shock_matrix(B, C, D, G_1):
+@nb.njit(cache=True)
+def nb_solve_shock_matrix(B, C, D, G_1):
     """
     Given the partial solution to the linear approximate policy function G_1, solve for the remaining component of the
     policy function, R.
@@ -120,6 +132,7 @@ def solve_shock_matrix(B, C, D, G_1):
     G_1: ArrayLike
         Transition matrix T in state space jargon. Gives the effect of variable values at time t on the
         values of the variables at time t+1.
+
     Returns
     -------
     impact: ArrayLike
@@ -128,4 +141,199 @@ def solve_shock_matrix(B, C, D, G_1):
 
     """
 
-    return -np.linalg.solve(C @ G_1 + B, np.eye(C.shape[0])) @ D
+    return -np.linalg.solve(C @ G_1 + B, D.astype(C.dtype))
+
+
+def _linear_policy_jvp(inputs, outputs, output_grads):
+    A, B, C = inputs
+    [T] = outputs
+    [T_bar] = output_grads
+
+    return o1_policy_function_adjoints(A, B, C, T, T_bar)
+
+
+class CycleReductionWrapper(Op):
+    def __init__(self, max_iter=1000, tol=1e-9):
+        self.max_iter = int(max_iter)
+        self.tol = tol
+        super().__init__()
+
+    def make_node(self, A, B, C) -> Apply:
+        inputs = list(map(pt.as_tensor, [A, B, C]))
+        outputs = [pt.dmatrix("T")]
+
+        return Apply(self, inputs, outputs)
+
+    def perform(
+        self, node: Apply, inputs: list[np.ndarray], outputs: list[list[None]]
+    ) -> None:
+        A, B, C = inputs
+        T, res, result, log_norm = nb_cycle_reduction(
+            A, B, C, max_iter=self.max_iter, tol=self.tol
+        )
+
+        outputs[0][0] = np.asarray(T)
+
+    def L_op(self, inputs, outputs, output_grads):
+        return _linear_policy_jvp(inputs, outputs, output_grads)
+
+
+def cycle_reduction_pt(A, B, C, D, max_iter=1000, tol=1e-9):
+    T = CycleReductionWrapper(max_iter=max_iter, tol=tol)(A, B, C)
+    R = pt_compute_selection_matrix(B, C, D, T)
+    return T, R
+
+
+def _scan_cycle_reduction(
+    A, B, C, max_iter: int = 1000, tol: float = 1e-7, mode=None
+) -> pt.Variable:
+    def noop(A0, A1, A2, A1_hat, norm, step_num):
+        return A0, A1, A2, A1_hat, norm, step_num
+
+    def cycle_step(A0, A1, A2, A1_hat, step_num, idx_0, idx_1):
+        tmp = pt.dot(
+            pt.vertical_stack(A0, A2),
+            pt.linalg.solve(
+                stabilize(A1),
+                pt.horizontal_stack(A0, A2),
+                assume_a="gen",
+                check_finite=False,
+            ),
+        )
+
+        A1 = A1 - tmp[idx_0, :][:, idx_1] - tmp[idx_1, :][:, idx_0]
+        A0 = -tmp[idx_0, :][:, idx_0]
+        A2 = -tmp[idx_1, :][:, idx_1]
+        A1_hat = A1_hat - tmp[idx_1, :][:, idx_0]
+
+        A0_L1_norm = pt.linalg.norm(A0, ord=1)
+
+        return A0, A1, A2, A1_hat, A0_L1_norm, step_num + 1
+
+    def step(A0, A1, A2, A1_hat, norm, step_num, idx_0, idx_1, tol):
+        state = pytensor.ifelse(
+            norm < tol,
+            noop(A0, A1, A2, A1_hat, norm, step_num),
+            cycle_step(A0, A1, A2, A1_hat, step_num, idx_0, idx_1),
+        )
+        return state
+
+    n = A.shape[0]
+    idx_0 = pt.arange(n)
+    idx_1 = idx_0 + n
+    norm = np.array(1e9, dtype="float64")
+    step_num = pt.zeros((), dtype="int32")
+    (*_, A1_hat, norm, n_steps), updates = pytensor.scan(
+        step,
+        outputs_info=[A, B, C, B, norm, step_num],
+        non_sequences=[idx_0, idx_1, tol],
+        n_steps=max_iter,
+        mode=get_mode(mode),
+    )
+    A1_hat = A1_hat[-1]
+
+    T = -pt.linalg.solve(stabilize(A1_hat), A, assume_a="gen", check_finite=False)
+
+    return [T, n_steps[-1]]
+
+
+def scan_cycle_reduction(
+    A: pt.TensorLike,
+    B: pt.TensorLike,
+    C: pt.TensorLike,
+    D: pt.TensorLike,
+    max_iter: int = 50,
+    tol: float = 1e-7,
+    mode: str | None = None,
+    use_adjoint_gradients: bool = True,
+):
+    A = pt.as_tensor_variable(A, name="A")
+    B = pt.as_tensor_variable(B, name="B")
+    C = pt.as_tensor_variable(C, name="C")
+    D = pt.as_tensor_variable(D, name="D")
+
+    output = _scan_cycle_reduction(A, B, C, max_iter, tol, mode=mode)
+
+    ScanCycleReducation = OpFromGraph(
+        inputs=[A, B, C],
+        outputs=output,
+        lop_overrides=_linear_policy_jvp if use_adjoint_gradients else None,
+        name="ScanCycleReduction",
+        inline=True,
+    )
+
+    T, n_steps = ScanCycleReducation(A, B, C)
+    R = pt_compute_selection_matrix(B, C, D, T)
+
+    return T, R, n_steps
+
+
+def solve_policy_function_with_cycle_reduction(
+    A: np.ndarray,
+    B: np.ndarray,
+    C: np.ndarray,
+    D: np.ndarray,
+    max_iter: int = 100,
+    tol: float = 1e-8,
+    verbose: bool = True,
+) -> tuple[np.ndarray, np.ndarray, str, float]:
+    """
+    Solve quadratic matrix equation of the form $A0x^2 + A1x + A2 = 0$ via cycle reduction algorithm of [1] to
+    obtain the first-order linear approxiate policy matrices T and R.
+
+    Parameters
+    ----------
+    A: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to past variables
+        values that are known when decision-making: those with t-1 subscripts.
+    B: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to variables that
+        are observed when decision-making: those with t subscripts.
+    C: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to variables that
+        enter in expectation when decision-making: those with t+1 subscripts.
+    D: np.ndarray
+        Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to exogenous shocks.
+    max_iter: int, default: 1000
+        Maximum number of iterations to perform before giving up.
+    tol: float, default: 1e-7
+        Floating point tolerance used to detect algorithmic convergence
+    verbose: bool, default: True
+        If true, prints the sum of squared residuals that result when the system is computed used the solution.
+
+    Returns
+    -------
+    T: ArrayLike
+        Transition matrix T in state space jargon. Gives the effect of variable values at time t on the
+        values of the variables at time t+1.
+    R: ArrayLike
+        Selection matrix R in state space jargon. Gives the effect of exogenous shocks at the t on the values of
+        variables at time t+1.
+    result: str
+        String describing result of the cycle reduction algorithm
+    log_norm: float
+        Log L1 matrix norm of the first matrix (A2 -> A1 -> A0) that did not converge.
+    """
+
+    # Sympy gives back integers in the case of x/dx = 1, which can screw up the dtypes when passing to numba if
+    # a Jacobian matrix is all constants (i.e. dF/d_shocks) -- cast everything to float64 here to avoid
+    # a numba warning.
+    T, R = None, None
+    T, res, result, log_norm = nb_cycle_reduction(A, B, C, max_iter, tol)
+    T = np.ascontiguousarray(T)
+
+    if verbose:
+        if result == "Optimization successful":
+            _log.info(
+                f"Solution found, sum of squared residuals: {(res ** 2).sum():0.9f}",
+            )
+        else:
+            _log.info(
+                f"Solution not found. Solver returned: {result}\n,"
+                f"Log norm of the solution at the final iteration: {log_norm:0.9f}"
+            )
+
+    if T is not None:
+        R = nb_solve_shock_matrix(B, C, D, T)
+
+    return T, R, result, log_norm
diff --git a/gEconpy/solvers/gensys.py b/gEconpy/solvers/gensys.py
index 7d64fca..d719672 100644
--- a/gEconpy/solvers/gensys.py
+++ b/gEconpy/solvers/gensys.py
@@ -1,79 +1,39 @@
+import numba as nb
 import numpy as np
-from numpy.typing import ArrayLike
-from scipy import linalg
-
-
-def qzdiv(
-    stake: float, A: ArrayLike, B: ArrayLike, Q: ArrayLike, Z: ArrayLike
-) -> tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike]:
-    """
-     Christopher Sim's qzdiv
-
-    Takes upper-triangular matrices :math:`A`, :math:`B` and orthonormal matrices :math:`Q`, :math:`Z`, and rearranges
-    them so that all cases of ``abs(B(i, i) / A(i, i)) > stake`` are in the lower-right corner, while preserving
-    upper-triangular and orthonormal properties, and maintaining the relationships :math:`Q^TAZ'` and :math:`Q^TBZ'`.
-    The columns of v are sorted correspondingly.
-
-    Matrices :math:`A`, :math:`B`, :math:`Q`, and :math:`Z` are the output of the generalized Schur decomposition
-    (QZ decomposition) of the system matrices :math:`G_0` and :math:`G_1`. A and B are upper triangular, with the
-    properties :math:`QAZ^T = G_0` and :math:`QBZ^T = G_1`.
-
-     Parameters
-     ----------
-     stake : float
-         Largest positive value for which an eigenvalue is considered stable.
-     A : ArrayLike
-         Upper-triangular matrix.
-     B : ArrayLike
-         Upper-triangular matrix.
-     Q : ArrayLike
-         Matrix of left Schur vectors.
-     Z : ArrayLike
-         Matrix of right Schur vectors.
+import pytensor
+import pytensor.tensor as pt
 
-     Returns
-     -------
-     tuple of ArrayLike
-         A, B, Q, Z matrices sorted such that all unstable roots are placed in the lower-right corners of the matrices.
-
-     Notes
-     -----
-     Adapted from http://sims.princeton.edu/yftp/gensys/mfiles/qzdiv.m
-    """
-
-    # TODO: scipy offers a sorted qz routine, ordqz, which automatically sorts the matrices by size of eigenvalue. This
-    #     seems to be what the functions qzdiv and qzswitch do, so it might be worthwhile to see if we can just use
-    #     ordqz instead.
-    #
-    # TODO: Add shape information to the Typing (see PEP 646)
-
-    n, _ = A.shape
-
-    root = np.hstack([np.diag(A)[:, None], np.diag(B)[:, None]])
-    root = np.abs(root)
-    root[:, 0] = root[:, 0] - (root[:, 0] < 1e-13) * (root[:, 0] + root[:, 1])
-    root[:, 1] = root[:, 1] / root[:, 0]
+from pytensor.graph.basic import Apply
+from pytensor.graph.op import Op
+from scipy import linalg
 
-    for i in range(n - 1, -1, -1):
-        m = None
-        for j in range(i, -1, -1):
-            if (root[j, 1] > stake) or (root[j, 1] < -0.1):
-                m = j
-                break
+from gEconpy.solvers.shared import (
+    o1_policy_function_adjoints,
+    pt_compute_selection_matrix,
+)
 
-        if m is None:
-            return A, B, Q, Z
+# A very small number
+EPSILON = np.spacing(1)
+floatX = pytensor.config.floatX
 
-        for k in range(m, i):
-            A, B, Q, Z = qzswitch(k, A, B, Q, Z)
-            root[k, 1], root[k + 1, 1] = root[k + 1, 1], root[k, 1]
 
-    return A, B, Q, Z
+@nb.njit(cache=True)
+def neg_conj_flip(x):
+    x_conj = x.conj()
+    x[:] = np.array((-x_conj[1], x_conj[0]))
+    return x
 
 
+@nb.njit(
+    [
+        "UniTuple(c16[::1, :], 4)(i8, c16[::1, :], c16[::1, :], c16[::1, :], c16[::1, :])",
+        "UniTuple(f8[::1, :], 4)(i8, f8[::1, :], f8[::1, :] ,f8[::1, :], f8[::1, :])",
+    ],
+    cache=True,
+)
 def qzswitch(
-    i: int, A: ArrayLike, B: ArrayLike, Q: ArrayLike, Z: ArrayLike
-) -> tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike]:
+    i: int, A: np.ndarray, B: np.ndarray, Q: np.ndarray, Z: np.ndarray
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """
     Christopher Sim's qzswitch.
 
@@ -87,26 +47,26 @@ def qzswitch(
     ----------
     i : int
         Index of matrix diagonal to switch.
-    A : ArrayLike
+    A : np.ndarray
         Upper-triangular matrix.
-    B : ArrayLike
+    B : np.ndarray
         Upper-triangular matrix.
-    Q : ArrayLike
+    Q : np.ndarray
         Matrix of left Schur vectors.
-    Z : ArrayLike
+    Z : np.ndarray
         Matrix of right Schur vectors.
 
     Returns
     -------
-    tuple of ArrayLike
+    tuple of np.ndarray
         Contains four elements:
-            A : ArrayLike
+            A : np.ndarray
                 Upper-triangular matrix with switched diagonal elements.
-            B : ArrayLike
+            B : np.ndarray
                 Upper-triangular matrix with switched diagonal elements.
-            Q : ArrayLike
+            Q : np.ndarray
                 Orthonormal matrix of left Schur vectors.
-            Z : ArrayLike
+            Z : np.ndarray
                 Orthonormal matrix of right Schur vectors.
 
     Notes
@@ -122,56 +82,155 @@ def qzswitch(
     e = B[i, i + 1]
     f = B[i + 1, i + 1]
 
+    wz = np.empty((2, 2), dtype=A.dtype)
+    xy = np.empty((2, 2), dtype=A.dtype)
+
     if (abs(c) < eps) & (abs(f) < eps):
         if abs(a) < eps:
             return A, B, Q, Z
 
         else:
-            wz = np.c_[b, -a].T
-            wz = wz / np.sqrt(wz.conj().T @ wz)
-            wz = np.hstack([wz, np.c_[wz[1].conj().T, -wz[0].conj().T].T])
-            xy = np.eye(2)
+            wz_row = np.array((b, -a))
+            wz_inner = (wz_row * wz_row.conj()).sum()
+            wz_row = wz_row / np.sqrt(wz_inner)
+
+            wz[:, 0] = wz_row
+            wz[:, 1] = neg_conj_flip(wz_row)
+            xy[:] = np.eye(2).astype(wz.dtype)
 
     elif (abs(a) < eps) & (abs(d) < eps):
         if abs(c) < eps:
             return A, B, Q, Z
         else:
-            wz = np.eye(2)
-            xy = np.c_[c, -b].T
-            xy = xy / np.sqrt(xy @ xy.conj().T)
-            xy = np.hstack([np.c_[xy[1].conj().T, -xy[0].conj().T].T, xy])
+            xy_row = np.array((c, -b))
+            xy_inner = (xy_row * xy_row.conj()).sum()
+            xy_row = xy_row / np.sqrt(xy_inner)
+
+            xy[:, 0] = neg_conj_flip(xy_row)
+            xy[:, 1] = xy_row
+            wz[:] = np.eye(2).astype(xy.dtype)
 
     else:
-        wz = np.c_[c * e - f * b, (c * d - f * a).conj()]
-        xy = np.c_[(b * d - e * a).conj(), (c * d - f * a).conj()]
+        wz_row = np.array((c * e - f * b, (c * d - f * a).conjugate()))
+        xy_row = np.array(((b * d - e * a).conjugate(), (c * d - f * a).conjugate()))
+
+        wz_inner = (wz_row * wz_row.conj()).sum()
+        xy_inner = (xy_row * xy_row.conj()).sum()
 
-        n = np.sqrt(wz @ wz.conj().T)
-        m = np.sqrt(xy @ xy.conj().T)
+        n = np.sqrt(wz_inner)
+        m = np.sqrt(xy_inner)
 
-        if m < eps * 100:
+        if np.abs(m) < eps * 100:
             return A, B, Q, Z
 
-        wz = wz / n
-        xy = xy / m
+        wz_row = wz_row / n
+        xy_row = xy_row / m
 
-        wz = np.vstack([wz, np.c_[-wz[:, 1].conj(), wz[:, 0].conj()]])
-        xy = np.vstack([xy, np.c_[-xy[:, 1].conj(), xy[:, 0].conj()]])
+        # xy = np.row_stack((xy, neg_conj_flip(xy)))
+        xy[0, :] = xy_row
+        xy[1, :] = neg_conj_flip(xy_row)
+
+        # wz = np.row_stack((wz, neg_conj_flip(wz)))
+        wz[0, :] = wz_row
+        wz[1, :] = neg_conj_flip(wz_row)
 
     idx_slice = slice(i, i + 2)
 
-    A[idx_slice, :] = xy @ A[idx_slice, :]
-    B[idx_slice, :] = xy @ B[idx_slice, :]
-    Q[idx_slice, :] = xy @ Q[idx_slice, :]
+    A[idx_slice, :] = xy @ np.asfortranarray(A[idx_slice, :])
+    B[idx_slice, :] = xy @ np.asfortranarray(B[idx_slice, :])
+    Q[idx_slice, :] = xy @ np.asfortranarray(Q[idx_slice, :])
 
-    A[:, idx_slice] = A[:, idx_slice] @ wz
-    B[:, idx_slice] = B[:, idx_slice] @ wz
-    Z[:, idx_slice] = Z[:, idx_slice] @ wz
+    A[:, idx_slice] = np.asfortranarray(A[:, idx_slice]) @ wz
+    B[:, idx_slice] = np.asfortranarray(B[:, idx_slice]) @ wz
+    Z[:, idx_slice] = np.asfortranarray(Z[:, idx_slice]) @ wz
 
     return A, B, Q, Z
 
 
+@nb.njit(
+    [
+        "UniTuple(c16[::1, :], 4)(f8, c16[::1, :], c16[::1, :] ,c16[::1, :], c16[::1, :])",
+        "UniTuple(f8[::1, :], 4)(f8, f8[::1, :], f8[::1, :] ,f8[::1, :], f8[::1, :])",
+    ],
+    cache=True,
+)
+def qzdiv(
+    stake: float, A: np.ndarray, B: np.ndarray, Q: np.ndarray, Z: np.ndarray
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """
+     Christopher Sim's qzdiv
+
+    Takes upper-triangular matrices :math:`A`, :math:`B` and orthonormal matrices :math:`Q`, :math:`Z`, and rearranges
+    them so that all cases of ``abs(B(i, i) / A(i, i)) > stake`` are in the lower-right corner, while preserving
+    upper-triangular and orthonormal properties, and maintaining the relationships :math:`Q^TAZ'` and :math:`Q^TBZ'`.
+    The columns of v are sorted correspondingly.
+
+    Matrices :math:`A`, :math:`B`, :math:`Q`, and :math:`Z` are the output of the generalized Schur decomposition
+    (QZ decomposition) of the system matrices :math:`G_0` and :math:`G_1`. A and B are upper triangular, with the
+    properties :math:`QAZ^T = G_0` and :math:`QBZ^T = G_1`.
+
+    Parameters
+    ----------
+     stake : float
+         Largest positive value for which an eigenvalue is considered stable.
+     A : np.ndarray
+         Upper-triangular matrix.
+     B : np.ndarray
+         Upper-triangular matrix.
+     Q : np.ndarray
+         Matrix of left Schur vectors.
+     Z : np.ndarray
+         Matrix of right Schur vectors.
+
+    Returns
+    -------
+     tuple of np.ndarray
+         A, B, Q, Z matrices sorted such that all unstable roots are placed in the lower-right corners of the matrices.
+
+    Notes
+    -----
+     Adapted from http://sims.princeton.edu/yftp/gensys/mfiles/qzdiv.m
+    """
+
+    # TODO: scipy offers a sorted qz routine, ordqz, which automatically sorts the matrices by size of eigenvalue. This
+    #     seems to be what the functions qzdiv and qzswitch do, so it might be worthwhile to see if we can just use
+    #     ordqz instead.
+    #
+    # TODO: Add shape information to the Typing (see PEP 646)
+
+    n, _ = A.shape
+
+    root = np.hstack((np.diag(A)[:, None], np.diag(B)[:, None]))
+    root = np.abs(root)
+    root[:, 0] = root[:, 0] - (root[:, 0] < 1e-13) * (root[:, 0] + root[:, 1])
+    root[:, 1] = root[:, 1] / root[:, 0]
+
+    for i in range(n - 1, -1, -1):
+        m = None
+        for j in range(i, -1, -1):
+            if (root[j, 1] > stake) or (root[j, 1] < -0.1):
+                m = j
+                break
+
+        if m is None:
+            return A, B, Q, Z
+
+        for k in range(m, i):
+            A[:], B[:], Q[:], Z[:] = qzswitch(k, A, B, Q, Z)
+            root[k, 1], root[k + 1, 1] = root[k + 1, 1], root[k, 1]
+
+    return A, B, Q, Z
+
+
+@nb.njit(
+    [
+        "Tuple((f8, i8, b1))(f8[::1, :], f8[::1, :], optional(f8), f8)",
+        "Tuple((f8, i8, b1))(c16[::1, :], c16[::1, :], optional(f8), f8)",
+    ],
+    cache=True,
+)
 def determine_n_unstable(
-    A: ArrayLike, B: ArrayLike, div: float, realsmall: float
+    A: np.ndarray, B: np.ndarray, div: float | None, realsmall: float
 ) -> tuple[float, int, bool]:
     """
     Determines how many roots of the system described by A and B are unstable.
@@ -182,8 +241,9 @@ def determine_n_unstable(
         Upper-triangular matrix, output of QZ decomposition.
     B : array
         Upper-triangular matrix, output of QZ decomposition.
-    div : float
-        Largest positive value for which an eigenvalue is considered stable.
+    div : float, Optional
+        Largest positive value for which an eigenvalue is considered stable. If None, a suitable value is calculated
+        based on the input matrices.
     realsmall : float
         An arbitrarily small number.
 
@@ -205,8 +265,12 @@ def determine_n_unstable(
     n, _ = A.shape
     n_unstable = 0
     zxz = False
+
+    realsmall = np.spacing(1) if realsmall is None else realsmall
     compute_div = div is None
-    div = 1.01 if div is None else div
+
+    if div is None:
+        div = 1.01
 
     for i in range(n):
         if compute_div:
@@ -221,26 +285,33 @@ def determine_n_unstable(
     return div, n_unstable, zxz
 
 
+@nb.njit(
+    [
+        "UniTuple(f8[::1, :], 2)(f8[::1, :],  i8)",
+        "UniTuple(c16[::1, :], 2)(c16[::1, :], i8)",
+    ],
+    cache=True,
+)
 def split_matrix_on_eigen_stability(
-    A: ArrayLike, n_unstable: int
-) -> tuple[ArrayLike, ArrayLike]:
+    A: np.ndarray, n_unstable: int
+) -> tuple[np.ndarray, np.ndarray]:
     """
     Splits a matrix into stable and unstable parts based on the number of unstable roots.
 
     Parameters
     ----------
-    A : ArrayLike
+    A : np.ndarray
         Array to split.
     n_unstable : int
         Number of unstable roots in the system.
 
     Returns
     -------
-    tuple of ArrayLike
+    tuple of np.ndarray
         Contains two elements:
-            A1 : ArrayLike
+            A1 : np.ndarray
                 Matrix containing all stable roots.
-            A2 : ArrayLike
+            A2 : np.ndarray
                 Matrix containing all unstable roots.
 
     Notes
@@ -251,19 +322,24 @@ def split_matrix_on_eigen_stability(
     stable_slice = slice(None, n - n_unstable)
     unstable_slice = slice(n - n_unstable, None)
 
-    A1 = A[stable_slice]
-    A2 = A[unstable_slice]
+    A1 = np.asfortranarray(A[stable_slice])
+    A2 = np.asfortranarray(A[unstable_slice])
 
     return A1, A2
 
 
-def build_u_v_d(eta: ArrayLike, realsmall: float):
+# @nb.njit(['Tuple((f8[:,::1], f8[:,::1], f8[:,::1], i8[::1]))(f8[:,::1], f8)',
+#           'Tuple((c16[:,::1], c16[:,::1], c16[:,::1], i8[::1]))(c16[:,::1], f8)'],
+#          cache=True)
+def build_u_v_d(
+    eta: np.ndarray, realsmall: float = EPSILON
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     """
     Computes the singular value decomposition (SVD) of the input matrix `eta` and identifies non-zero indices.
 
     Parameters
     ----------
-    eta : ArrayLike
+    eta : np.ndarray
         Input matrix for which to compute the SVD.
     realsmall : float
         A small threshold value to determine non-zero singular values.
@@ -272,40 +348,37 @@ def build_u_v_d(eta: ArrayLike, realsmall: float):
     -------
     tuple
         Contains two elements:
-            (U, V, D) : tuple of ArrayLike
+            (U, V, D) : tuple of np.ndarray
                 SVD decomposition of `eta` where `U` and `V` are orthogonal matrices and `D` is a diagonal matrix.
-            non_zero_indices : ArrayLike
+            non_zero_indices : np.ndarray
                 Array of non-zero indices based on the threshold `realsmall`.
 
     Notes
     -----
     Adapted from http://sims.princeton.edu/yftp/gensys/mfiles/gensys.m
     """
-    u_eta, d_eta, v_eta = linalg.svd(eta)
-    d_eta = np.diag(d_eta)  # match matlab output of svd
-    v_eta = v_eta.conj().T  # match matlab output of svd
+    u_eta, d_eta, vh_eta = linalg.svd(eta, compute_uv=True, full_matrices=False)
+    v_eta = vh_eta.conj().T
 
-    md = min(d_eta.shape)
-    big_ev = np.where(np.diagonal(d_eta[:md, :md] > realsmall))[0]
+    big_ev = np.flatnonzero(d_eta > realsmall)
 
     u_eta = u_eta[:, big_ev]
     v_eta = v_eta[:, big_ev]
-    d_eta = d_eta[big_ev, big_ev]
-
-    if d_eta.ndim == 1:
-        d_eta = np.diag(d_eta)
+    d_eta = np.diag(d_eta[big_ev])
 
     return u_eta, v_eta, d_eta, big_ev
 
 
+# @nb.njit(cache=True)
 def gensys(
-    g0: ArrayLike,
-    g1: ArrayLike,
-    c: ArrayLike,
-    psi: ArrayLike,
-    pi: ArrayLike,
+    g0: np.ndarray,
+    g1: np.ndarray,
+    c: np.ndarray,
+    psi: np.ndarray,
+    pi: np.ndarray,
     div: float | None = None,
     tol: float | None = 1e-8,
+    return_all_matrices: bool = True,
 ) -> tuple:
     """
     Christopher Sim's gensys
@@ -331,38 +404,40 @@ def gensys(
 
     Parameters
     ----------
-    g0 : ArrayLike
+    g0 : np.ndarray
         Coefficient matrix of the dynamic system corresponding to the time-t variables.
-    g1 : ArrayLike
+    g1 : np.ndarray
         Coefficient matrix of the dynamic system corresponding to the time t-1 variables.
-    c : ArrayLike
+    c : np.ndarray
         Vector of constant terms.
-    psi : ArrayLike
+    psi : np.ndarray
         Coefficient matrix of the dynamic system corresponding to the exogenous shock terms.
-    pi : ArrayLike
+    pi : np.ndarray
         Coefficient matrix of the dynamic system corresponding to the endogenously determined
         expectational errors.
     div : float
         Threshold value for determining stable and unstable roots.
     tol : float, default: 1e-8
         Level of floating point precision.
+    return_all_matrices: bool, default True
+        Whether to return all matrices or just the policy function.
 
     Returns
     -------
-    G1 : ArrayLike
+    G1 : np.ndarray
         Policy function relating the current timestep to the next, transition matrix T in state space jargon.
-    C : ArrayLike
+    C : np.ndarray
         Array of system means, intercept vector c in state space jargon.
-    impact : ArrayLike
+    impact : np.ndarray
         Policy function component relating exogenous shocks observed at the t to variable values in t+1, selection
         matrix R in state space jargon.
-    fmat : ArrayLike
+    fmat : np.ndarray
         Matrix used in the transformation of the system to handle unstable roots.
-    fwt : ArrayLike
+    fwt : np.ndarray
         Weight matrix corresponding to fmat.
-    ywt : ArrayLike
+    ywt : np.ndarray
         Weight matrix corresponding to the stable part of the system.
-    gev : ArrayLike
+    gev : np.ndarray
         Generalized left and right eigenvalues generated by qz(g0, g1), sorted such that stable roots are in the
         top-left corner.
     eu : tuple
@@ -389,7 +464,9 @@ def gensys(
 
     n, _ = g1.shape
     A, B, Q, Z = linalg.qz(g0, g1, "complex")
-    Q = Q.conj().T  # q is transposed relative to matlab, see scipy docs
+    Q = np.asfortranarray(
+        Q.conj().T
+    )  # q is transposed relative to matlab, see scipy docs
 
     div, n_unstable, zxz = determine_n_unstable(A, B, div, tol)
     n_stable = n - n_unstable
@@ -398,8 +475,8 @@ def gensys(
         eu = [-2, -2, 0]
         return None, None, None, None, None, None, None, eu, None
 
-    A, B, Q, Z = qzdiv(div, A, B, Q, Z)
-    gev = np.c_[np.diagonal(A), np.diagonal(B)]
+    A[:], B[:], Q[:], Z[:] = qzdiv(div, A, B, Q, Z)
+    gev = np.column_stack((np.diagonal(A), np.diagonal(B)))
 
     Q1, Q2 = split_matrix_on_eigen_stability(Q, n_unstable)
 
@@ -408,7 +485,9 @@ def gensys(
 
     # No stable roots
     if n_unstable == 0:
-        big_ev = 0
+        big_ev = np.zeros(
+            0,
+        )
 
         u_eta = np.zeros((0, 0))
         d_eta = np.zeros((0, 0))
@@ -435,7 +514,7 @@ def gensys(
         unique = True
     else:
         loose = v_eta_1 - v_eta @ v_eta.T @ v_eta_1
-        ul, dl, vl = linalg.svd(loose)
+        [ul, dl, vl] = linalg.svd(loose)
         if dl.ndim == 1:
             dl = np.diag(dl)
 
@@ -454,33 +533,42 @@ def gensys(
         @ u_eta_1.conj().T
     )
 
-    T_mat = np.c_[np.eye(n_stable), -inner_term.conj().T]
-    G_0 = np.r_[T_mat @ A, np.c_[np.zeros((n_unstable, n_stable)), np.eye(n_unstable)]]
+    T_mat = np.column_stack((np.eye(n_stable), -inner_term.conj().T))
+    G_0 = np.row_stack(
+        (
+            T_mat @ A,
+            np.column_stack((np.zeros((n_unstable, n_stable)), np.eye(n_unstable))),
+        )
+    )
 
-    G_1 = np.r_[T_mat @ B, np.zeros((n_unstable, n))]
+    G_1 = np.row_stack((T_mat @ B, np.zeros((n_unstable, n))))
 
     G_0_inv = linalg.inv(G_0)
     G_1 = G_0_inv @ G_1
+    G_1 = (Z @ G_1 @ Z.conj().T).real
+
+    if not return_all_matrices:
+        return G_1, eu
 
     idx = slice(n_stable, n)
 
-    C = np.r_[T_mat @ Q @ c, linalg.solve(A[idx, idx] - B[idx, idx], Q2) @ c]
+    C = np.row_stack((T_mat @ Q @ c, linalg.solve(A[idx, idx] - B[idx, idx], Q2) @ c))
 
-    impact = G_0_inv @ np.r_[T_mat @ Q @ psi, np.zeros((n_unstable, psi.shape[1]))]
+    impact = G_0_inv @ np.row_stack(
+        (T_mat @ Q @ psi, np.zeros((n_unstable, psi.shape[1])))
+    )
 
     f_mat = linalg.solve(B[idx, idx], A[idx, idx])
     f_wt = -linalg.solve(B[idx, idx], Q2) @ psi
     y_wt = G_0_inv[:, idx]
 
-    loose = (
-        G_0_inv
-        @ np.r_[
+    loose = G_0_inv @ np.row_stack(
+        (
             eta_wt_1 @ (np.eye(n_eta) - v_eta @ v_eta.conj().T),
             np.zeros((n_unstable, n_eta)),
-        ]
+        )
     )
 
-    G_1 = (Z @ G_1 @ Z.conj().T).real
     C = (Z @ C).real
     impact = (Z @ impact).real
     loose = (Z @ loose).real
@@ -509,19 +597,141 @@ def interpret_gensys_output(eu):
         A message describing the existence and uniqueness of the solution based on the values in `eu`.
     """
 
-    message = ""
+    message = (
+        f"Gensys return codes: {' '.join(map(str, eu))}, with the following meaning:\n"
+    )
     if eu[0] == -2 and eu[1] == -2:
-        message = "Coincident zeros.  Indeterminacy and/or nonexistence. Check that your system is correctly defined."
+        message += "Coincident zeros.  Indeterminacy and/or nonexistence. Check that your system is correctly defined."
     elif eu[0] == -1:
-        message = (
+        message += (
             f"System is indeterminate. There are {eu[2]} loose endogenous variables."
         )
     elif eu[1] == -1:
-        message = "Solution exists, but it is not unique -- sunspots."
+        message += "Solution exists, but it is not unique -- sunspots."
     elif eu[0] == 0 and eu[1] == 0:
-        message = "Solution does not exist."
+        message += "Solution does not exist."
     elif eu[0] == 1 and eu[1] == 0:
-        message = "Solution exists, but is not unique."
+        message += "Solution exists, but is not unique."
     elif eu[0] == 1 and eu[1] == 1:
-        message = "Gensys found a unique solution."
-    return message
+        message += "Gensys found a unique solution."
+    else:
+        message += "Unknown return code. Check the gensys documentation."
+    return message.strip()
+
+
+@nb.njit(cache=True)
+def _get_variable_counts(A, D):
+    n_eq, n_vars = A.shape
+    _, n_shocks = D.shape
+
+    return n_eq, n_vars, n_shocks
+
+
+@nb.njit(cache=True)
+def _find_lead_variables(C, tol=1e-8):
+    return np.where(np.sum(np.abs(C), axis=0) > tol)[0]
+
+
+@nb.njit(cache=True)
+def _gensys_setup(A, B, C, D, tol=1e-8):
+    n_eq, n_vars, n_shocks = _get_variable_counts(A, D)
+
+    lead_var_idx = _find_lead_variables(C, tol)
+    eqs_and_leads_idx = np.concatenate(
+        (np.arange(n_vars), lead_var_idx + n_vars), axis=0
+    )
+    n_leads = len(lead_var_idx)
+
+    Gamma_0 = np.vstack(
+        (np.hstack((B, C)), np.hstack((-np.eye(n_eq), np.zeros((n_eq, n_eq)))))
+    )
+
+    Gamma_1 = np.vstack(
+        (
+            np.hstack((A, np.zeros((n_eq, n_eq)))),
+            np.hstack((np.zeros((n_eq, n_eq)), np.eye(n_eq))),
+        )
+    )
+
+    Pi = np.vstack((np.zeros((n_eq, n_eq)), np.eye(n_eq)))
+
+    Psi = np.vstack((D, np.zeros((n_eq, n_shocks))))
+
+    Gamma_0 = Gamma_0[eqs_and_leads_idx, :][:, eqs_and_leads_idx]
+    Gamma_1 = Gamma_1[eqs_and_leads_idx, :][:, eqs_and_leads_idx]
+    Psi = Psi[eqs_and_leads_idx, :]
+    Pi = Pi[eqs_and_leads_idx, :][:, lead_var_idx]
+
+    G0 = -Gamma_0
+    C = np.asfortranarray(np.zeros(shape=(n_vars + n_leads, 1)))
+
+    return G0, Gamma_1, C, Psi, Pi
+
+
+def solve_policy_function_with_gensys(
+    A: np.ndarray,
+    B: np.ndarray,
+    C: np.ndarray,
+    D: np.ndarray,
+    tol: float = 1e-8,
+    reutrn_all_matrices: bool = True,
+) -> tuple:
+    g0, g1, c, psi, pi = _gensys_setup(A, B, C, D, tol)
+    G_1, constant, impact, f_mat, f_wt, y_wt, gev, eu, loose = gensys(
+        g0, g1, c, psi, pi
+    )
+
+    if reutrn_all_matrices:
+        return G_1, constant, impact, f_mat, f_wt, y_wt, gev, eu, loose
+
+    else:
+        return G_1, eu
+
+
+class GensysWrapper(Op):
+    def __init__(self, tol=1e-8):
+        self.tol = tol
+        super().__init__()
+
+    def make_node(self, A, B, C, D) -> Apply:
+        inputs = list(map(pt.as_tensor, [A, B, C, D]))
+        n_variables = inputs[0].type.shape[0]
+
+        outputs = [
+            pt.tensor("T", shape=(n_variables, n_variables)),
+            pt.scalar("success", dtype="bool"),
+        ]
+
+        return Apply(self, inputs, outputs)
+
+    def perform(
+        self, node: Apply, inputs: list[np.ndarray], outputs: list[list[None]]
+    ) -> None:
+        A, B, C, D = inputs
+        G_1, eu = solve_policy_function_with_gensys(
+            A, B, C, D, tol=self.tol, reutrn_all_matrices=False
+        )
+
+        n_vars = A.shape[0]
+        T = G_1[:n_vars, :n_vars]
+        success = all([x == 1 for x in eu[:2]])
+
+        outputs[0][0] = np.asarray(T)
+        outputs[1][0] = np.asarray(success)
+
+    def L_op(self, inputs, outputs, output_grads):
+        A, B, C, D = inputs
+        T, success = outputs
+        T_bar, success_bar = output_grads
+
+        A_bar, B_bar, C_bar = o1_policy_function_adjoints(A, B, C, T, T_bar)
+        D_bar = pt.zeros_like(D).astype(floatX)
+
+        return [A_bar, B_bar, C_bar, D_bar]
+
+
+def gensys_pt(A, B, C, D, tol=1e-8):
+    T, success = GensysWrapper(tol=tol)(A, B, C, D)
+    R = pt_compute_selection_matrix(B, C, D, T)
+
+    return T, R, success
diff --git a/gEconpy/solvers/perturbation.py b/gEconpy/solvers/perturbation.py
deleted file mode 100644
index 4d0d079..0000000
--- a/gEconpy/solvers/perturbation.py
+++ /dev/null
@@ -1,278 +0,0 @@
-import numpy as np
-import sympy as sp
-from numpy.typing import ArrayLike
-from scipy import linalg
-from sympy.solvers.solveset import NonlinearError
-
-from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.shared.utilities import eq_to_ss
-from gEconpy.solvers.cycle_reduction import cycle_reduction, solve_shock_matrix
-from gEconpy.solvers.gensys import gensys
-
-
-class PerturbationSolver:
-    def __init__(self, model):
-        self.steady_state_dict = model.steady_state_dict
-        self.steady_state_solved = model.steady_state_solved
-        self.param_dict = model.free_param_dict
-        self.system_equations = model.system_equations
-        self.variables = model.variables
-
-        self.shocks = model.shocks
-        self.n_shocks = model.n_shocks
-
-    @staticmethod
-    def solve_policy_function_with_gensys(
-        A: ArrayLike,
-        B: ArrayLike,
-        C: ArrayLike,
-        D: ArrayLike,
-        tol: float = 1e-8,
-        verbose: bool = True,
-    ) -> tuple:
-        n_eq, n_vars = A.shape
-        _, n_shocks = D.shape
-
-        lead_var_idx = np.where(np.sum(np.abs(C), axis=0) > tol)[0]
-        eqs_and_leads_idx = np.r_[np.arange(n_vars), lead_var_idx + n_vars].tolist()
-
-        n_leads = len(lead_var_idx)
-
-        Gamma_0 = np.vstack(
-            [np.hstack([B, C]), np.hstack([-np.eye(n_eq), np.zeros((n_eq, n_eq))])]
-        )
-
-        Gamma_1 = np.vstack(
-            [
-                np.hstack([A, np.zeros((n_eq, n_eq))]),
-                np.hstack([np.zeros((n_eq, n_eq)), np.eye(n_eq)]),
-            ]
-        )
-
-        Pi = np.vstack([np.zeros((n_eq, n_eq)), np.eye(n_eq)])
-
-        Psi = np.vstack([D, np.zeros((n_eq, n_shocks))])
-
-        Gamma_0 = Gamma_0[eqs_and_leads_idx, :][:, eqs_and_leads_idx]
-        Gamma_1 = Gamma_1[eqs_and_leads_idx, :][:, eqs_and_leads_idx]
-        Psi = Psi[eqs_and_leads_idx, :]
-        Pi = Pi[eqs_and_leads_idx, :][:, lead_var_idx]
-
-        # Is this necessary?
-        g0 = -np.ascontiguousarray(Gamma_0)  # NOTE THE IMPORTANT MINUS SIGN LURKING
-        g1 = np.ascontiguousarray(Gamma_1)
-        c = np.ascontiguousarray(np.zeros(shape=(n_vars + n_leads, 1)))
-        psi = np.ascontiguousarray(Psi)
-        pi = np.ascontiguousarray(Pi)
-
-        G_1, constant, impact, f_mat, f_wt, y_wt, gev, eu, loose = gensys(
-            g0, g1, c, psi, pi
-        )
-
-        return G_1, constant, impact, f_mat, f_wt, y_wt, gev, eu, loose
-
-    @staticmethod
-    def solve_policy_function_with_cycle_reduction(
-        A: ArrayLike,
-        B: ArrayLike,
-        C: ArrayLike,
-        D: ArrayLike,
-        max_iter: int = 1000,
-        tol: float = 1e-8,
-        verbose: bool = True,
-    ) -> tuple[ArrayLike, ArrayLike, str, float]:
-        """
-        Solve quadratic matrix equation of the form $A0x^2 + A1x + A2 = 0$ via cycle reduction algorithm of [1] to
-        obtain the first-order linear approxiate policy matrices T and R.
-
-        Parameters
-        ----------
-        A: Arraylike
-            Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to past variables
-            values that are known when decision-making: those with t-1 subscripts.
-        B: ArrayLike
-            Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to variables that
-            are observed when decision-making: those with t subscripts.
-        C: ArrayLike
-            Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to variables that
-            enter in expectation when decision-making: those with t+1 subscripts.
-        D: ArrayLike
-            Jacobian matrix of the DSGE system, evaluated at the steady state, taken with respect to exogenous shocks.
-        max_iter: int, default: 1000
-            Maximum number of iterations to perform before giving up.
-        tol: float, default: 1e-7
-            Floating point tolerance used to detect algorithmic convergence
-        verbose: bool, default: True
-            If true, prints the sum of squared residuals that result when the system is computed used the solution.
-
-        Returns
-        -------
-        T: ArrayLike
-            Transition matrix T in state space jargon. Gives the effect of variable values at time t on the
-            values of the variables at time t+1.
-        R: ArrayLike
-            Selection matrix R in state space jargon. Gives the effect of exogenous shocks at the t on the values of
-            variables at time t+1.
-        result: str
-            String describing result of the cycle reduction algorithm
-        log_norm: float
-            Log L1 matrix norm of the first matrix (A2 -> A1 -> A0) that did not converge.
-        """
-
-        # Sympy gives back integers in the case of x/dx = 1, which can screw up the dtypes when passing to numba if
-        # a Jacobian matrix is all constants (i.e. dF/d_shocks) -- cast everything to float64 here to avoid
-        # a numba warning.
-        T, R = None, None
-
-        # A, B, C, D = A.astype('float64'), B.astype('float64'), C.astype('float64'), D.astype('float64')
-
-        T, result, log_norm = cycle_reduction(A, B, C, max_iter, tol, verbose)
-
-        if T is not None:
-            R = solve_shock_matrix(B, C, D, T)
-
-        return T, R, result, log_norm
-
-    def statespace_to_gEcon_representation(self, A, T, R, variables, tol):
-        n_vars = len(variables)
-
-        state_var_idx = np.where(
-            np.abs(T[np.argmax(np.abs(T), axis=0), np.arange(n_vars)]) >= tol
-        )[0]
-        state_var_mask = np.isin(np.arange(n_vars), state_var_idx)
-
-        n_shocks = self.n_shocks
-        shock_idx = np.arange(n_shocks)
-
-        # variables = np.atleast_1d(variables).squeeze()
-
-        # state_vars = variables[state_var_mask]
-        # L1_state_vars = np.array([x.step_backward() for x in state_vars])
-        # jumpers = np.atleast_1d(variables)[~state_var_mask]
-
-        PP = T.copy()
-        PP[np.where(np.abs(PP) < tol)] = 0
-        QQ = R.copy()
-        QQ = QQ[:n_vars, :]
-        QQ[np.where(np.abs(QQ) < tol)] = 0
-
-        P = PP[state_var_mask, :][:, state_var_mask]
-        Q = QQ[state_var_mask, :][:, shock_idx]
-        R = PP[~state_var_mask, :][:, state_var_idx]
-        S = QQ[~state_var_mask, :][:, shock_idx]
-
-        A_prime = A[:, state_var_mask]
-        R_prime = PP[:, state_var_mask]
-        S_prime = QQ[:, shock_idx]
-
-        return P, Q, R, S, A_prime, R_prime, S_prime
-
-    @staticmethod
-    def residual_norms(B, C, D, Q, P, A_prime, R_prime, S_prime):
-        norm_deterministic = linalg.norm(A_prime + B @ R_prime + C @ R_prime @ P)
-
-        norm_stochastic = linalg.norm(B @ S_prime + C @ R_prime @ Q + D)
-
-        return norm_deterministic, norm_stochastic
-
-    def log_linearize_model(self, not_loglin_variables=None) -> list[sp.Matrix]:
-        """
-        :return: List, a list of Sympy matrices comprised of parameters and steady-state values, see docstring.
-
-        Convert the non-linear model to its log-linear approximation using a first-order Taylor expansion around the
-        deterministic steady state. The specific method of log-linearization is taken from the gEcon User's Guide,
-        page 54, equation 9.9.
-
-            F1 @ T @ y_{t-1} + F2 @ T @ y_t + F3 @ T @ y_{t+1} + F4 @ epsilon_t = 0
-
-        Where T is a diagonal matrix containing steady-state values on the diagonal. Evaluating the matrix
-        multiplications in the expression above obtains:
-
-            A @ y_{t-1} + B @ y_t + C @ y_{t+1} + D @ epsilon = 0
-
-        Matrices A, B, C, and D are returned by this function.
-
-        TODO: Presently, everything is done using sympy, which is extremely slow. This should all be re-written in a
-            way that is Numba and/or CUDA compatible.
-        """
-
-        Fs = []
-        lags, now, leads = self.make_all_variable_time_combinations()
-        shocks = self.shocks
-        for var_group in [lags, now, leads, shocks]:
-            F = []
-
-            # If the user selects a variable to not be log linearized, we need to set the value in T to be one, but
-            # still replace all SS values in A, B, C, D as usual. These dummies facilitate that.
-            # T = sp.diag(*[TimeAwareSymbol(x.base_name + '_T', 'ss') for x in var_group])
-
-            for eq in self.system_equations:
-                F_row = []
-                for var in var_group:
-                    dydx = sp.powsimp(eq_to_ss(eq.diff(var)))
-                    dydx *= (
-                        1.0 if var.base_name in not_loglin_variables else var.to_ss()
-                    )
-                    atoms = dydx.atoms()
-                    if len(atoms) == 1:
-                        x = list(atoms)[0]
-                        if isinstance(x, sp.core.numbers.Number) and x != 0:
-                            dydx = sp.Float(x)
-                    F_row.append(dydx)
-
-                F.append(F_row)
-            F = sp.Matrix(F)
-            # Fs.append(sp.MatMul(F, T, evaluate=False))
-            Fs.append(F)
-
-        return Fs
-
-    def convert_linear_system_to_matrices(self) -> list[sp.Matrix]:
-        """
-
-        :return: List of sympy Matrices representing the linear system
-
-        If the model has already been log-linearized by hand, this method is used to simplify the construction of the
-        solution matrices. Following the gEcon user's guide, page 54, equation 9.10, the solution should be of the form:
-
-            A @ y_{t-1} + B @ y_t + C @ y_{t+1} + D @ epsilon = 0
-
-        This function organizes the model equations and returns matrices A, B, C, and D.
-        """
-
-        lags, now, leads = self.make_all_variable_time_combinations()
-        shocks = self.shocks
-        model = self.system_equations
-        n = len(lags)
-
-        all_y = lags + now + leads + shocks
-
-        try:
-            A, b = sp.linear_eq_to_matrix(model, all_y)
-        except NonlinearError as sympy_msg:
-            raise ValueError(
-                f"Model does not appear to be linear, check your GCN file. Sympy error: {sympy_msg}"
-            )
-
-        offsets = np.array([0, n, n, n, 1])
-        slices = [
-            slice(i, i + offset)
-            for i, offset in zip(offsets.cumsum()[:-1], offsets[1:])
-        ]
-
-        Fs = [A[:, idx] for idx in slices]
-
-        return Fs
-
-    def make_all_variable_time_combinations(
-        self,
-    ) -> tuple[list[TimeAwareSymbol], list[TimeAwareSymbol], list[TimeAwareSymbol]]:
-        """
-        :return: Tuple of three lists, containing all model variables at time steps t-1, t, and t+1, respectively.
-        """
-
-        now = sorted(self.variables, key=lambda x: x.base_name)
-        lags = [x.step_backward() for x in now]
-        leads = [x.step_forward() for x in now]
-
-        return lags, now, leads
diff --git a/gEconpy/solvers/shared.py b/gEconpy/solvers/shared.py
new file mode 100644
index 0000000..2a1254d
--- /dev/null
+++ b/gEconpy/solvers/shared.py
@@ -0,0 +1,74 @@
+import pytensor.tensor as pt
+
+from pytensor.tensor import TensorVariable
+
+
+def stabilize(x, jitter=1e-16):
+    return x + jitter * pt.eye(x.shape[0])
+
+
+def o1_policy_function_adjoints(
+    A: TensorVariable,
+    B: TensorVariable,
+    C: TensorVariable,
+    T: TensorVariable,
+    T_bar: TensorVariable,
+) -> list[TensorVariable, TensorVariable, TensorVariable]:
+    """
+    Compute the adjoints of the inputs to the equation:
+
+    ..math::
+
+        A + BT + CTT = 0
+
+    Which is the matrix quadratic equation associated with the first order approximation to a DSGE policy function.
+
+    Parameters
+    ----------
+    A: TensorVariable
+        Matrix of partial derivatives with respect to variables at t-1, evaluated at the steady-state
+    B: TensorVariable
+        Matrix of partial derivatives with respect to variables at t, evaluated at the steady-state
+    C: TensorVariable
+        Matrix of partial derivatives with respect to variables at t+1, evaluated at the steady-state
+    T: TensorVariable
+    T_bar: TensorVariable
+        Backward sensitivity of a scalar loss function with respect to the solved policy function T
+
+    Returns
+    -------
+    adjoints: list of TensorVariable
+        A_bar: TensorVariable
+            Adjoint of A
+        B_bar: TensorVariable
+            Adjoint of B
+        C_bar: TensorVariable
+            Adjoint of C
+    """
+    vec_T_bar = T_bar.T.ravel()
+
+    n = A.shape[0]
+
+    # Compute matrix of lagrange multipliers S
+    eye = pt.eye(n)
+    M1 = pt.linalg.kron(T, C.T)
+    M2 = pt.linalg.kron(eye, T.T @ C.T)
+    M3 = pt.linalg.kron(eye, B.T)
+
+    vec_S = pt.linalg.solve(
+        stabilize(M1 + M2 + M3), -vec_T_bar, assume_a="gen", check_finite=False
+    )
+    S = vec_S.reshape((n, n)).T
+
+    # With S, compute adjoints of the inputs
+    A_bar = S
+    B_bar = S @ T.T
+    C_bar = S @ T.T @ T.T
+
+    return [A_bar, B_bar, C_bar]
+
+
+def pt_compute_selection_matrix(B, C, D, T):
+    return -pt.linalg.solve(
+        C @ T + B, D.astype(T.dtype), assume_a="gen", check_finite=False
+    )
diff --git a/gEconpy/solvers/steady_state.py b/gEconpy/solvers/steady_state.py
deleted file mode 100644
index 41f0d4a..0000000
--- a/gEconpy/solvers/steady_state.py
+++ /dev/null
@@ -1,1556 +0,0 @@
-from itertools import product
-from typing import Any
-from collections.abc import Callable
-from warnings import catch_warnings, simplefilter
-
-import numpy as np
-import sympy as sp
-from joblib import Parallel, delayed
-from scipy import optimize
-
-from gEconpy.classes.containers import SymbolDictionary
-from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.numba_tools.utilities import numba_lambdify
-from gEconpy.shared.utilities import eq_to_ss, substitute_all_equations
-
-
-class SteadyStateSolver:
-    def __init__(self, model):
-        self.variables: list[TimeAwareSymbol] = model.variables
-        self.shocks: list[sp.Add] = model.shocks
-
-        self.n_variables: int = model.n_variables
-
-        self.free_param_dict: SymbolDictionary[str, float] = model.free_param_dict
-        self.params_to_calibrate: list[sp.Symbol] = model.params_to_calibrate
-        self.calibrating_equations: list[sp.Add] = model.calibrating_equations
-        self.shock_dict: SymbolDictionary[str, float] | None = None
-
-        self.system_equations: list[sp.Add] = model.system_equations
-        self.steady_state_relationships: SymbolDictionary[str, float | sp.Add] = (
-            model.steady_state_relationships
-        )
-
-        self.steady_state_system: list[sp.Add] = []
-        self.steady_state_dict: SymbolDictionary[str, float] = SymbolDictionary()
-        self.steady_state_solved: bool = False
-
-        self.build_steady_state_system()
-
-    def build_steady_state_system(self):
-        ss_vars = map(lambda x: x.to_ss(), self.variables)
-        self.steady_state_dict = (
-            SymbolDictionary.fromkeys(ss_vars, None).to_string().sort_keys()
-        )
-
-        self.shock_dict = SymbolDictionary.fromkeys(self.shocks, 0.0).to_ss()
-        self.steady_state_system = [
-            eq_to_ss(eq).subs(self.shock_dict).simplify()
-            for eq in self.system_equations
-        ]
-
-    def _validate_optimizer_kwargs(
-        self,
-        optimizer_kwargs: dict,
-        n_eq: int,
-        method: str,
-        use_jac: bool,
-        use_hess: bool,
-    ) -> dict:
-        """
-        Validate user-provided keyword arguments to either scipy.optimize.root or scipy.optimize.minimize, and insert
-        good defaults where not provided.
-
-        Note: This function never overwrites user arguments.
-
-        Parameters
-        ----------
-        optimizer_kwargs: dict
-            User-provided arguments for the optimizer
-        n_eq: int
-            Number of remaining steady-state equations after reduction
-        method: str
-            Which family of solution algorithms, minimization or root-finding, to be used.
-        use_jac: bool
-            Whether computation of the jacobian has been requested
-        use_hess: bool
-            Whether computation of the hessian has been requested
-
-        Returns
-        -------
-        optimizer_kwargs: dict
-            Keyword arguments for the scipy function, with "reasonable" defaults inserted where not provided
-        """
-
-        optimizer_kwargs = {} if optimizer_kwargs is None else optimizer_kwargs
-        method_given = "method" in optimizer_kwargs.keys()
-
-        if method == "root" and not method_given:
-            if use_jac:
-                optimizer_kwargs["method"] = "hybr"
-            else:
-                optimizer_kwargs["method"] = "broyden1"
-
-            if n_eq == 1:
-                optimizer_kwargs["method"] = "lm"
-
-        elif method == "minimize" and not method_given:
-            # Set optimizer_kwargs for minimization
-            if use_hess and use_jac:
-                optimizer_kwargs["method"] = "trust-exact"
-            elif use_jac:
-                optimizer_kwargs["method"] = "BFGS"
-            else:
-                optimizer_kwargs["method"] = "Nelder-Mead"
-
-        if "tol" not in optimizer_kwargs.keys():
-            optimizer_kwargs["tol"] = 1e-9
-
-        return optimizer_kwargs
-
-    def apply_user_simplifications(self) -> list[sp.Add]:
-        """
-        Check if the system is analytically solvable without resorting to an optimizer. Currently, this is true only
-        if it is a linear model, or if the user has provided the complete steady state.
-
-        Returns
-        -------
-        is_presolved: bool
-        """
-        param_dict = self.free_param_dict.copy().to_sympy()
-        user_provided = (
-            self.steady_state_relationships.copy().to_sympy().float_to_values()
-        )
-        ss_eqs = self.steady_state_system.copy()
-        calib_eqs = self.calibrating_equations.copy()
-        all_eqs = ss_eqs + calib_eqs
-
-        all_vars_sym = list(self.steady_state_dict.to_sympy().keys())
-        all_vars_and_calib_sym = all_vars_sym + self.params_to_calibrate
-
-        zeros = np.full_like(all_eqs, False)
-        simplified_eqs = substitute_all_equations(all_eqs, user_provided)
-
-        for i, eq in enumerate(simplified_eqs):
-            subbed_eq = eq.subs(param_dict)
-
-            # Janky, but many expressions won't reduce to zero even if they ought to -> test numerically
-            atoms = [x for x in subbed_eq.atoms() if x in all_vars_and_calib_sym]
-            test_values = {x: np.random.uniform(1e-2, 0.99) for x in atoms}
-            eq_is_zero = sp.Abs(subbed_eq.subs(test_values)) < 1e-8
-            zeros[i] = eq_is_zero
-
-            if isinstance(subbed_eq, sp.Float) and not eq_is_zero:
-                raise ValueError(
-                    f"Applying user steady state definitions to equation {i}:\n"
-                    f"\t{all_eqs[i]}\n"
-                    f"resulted in non-zero residuals: {subbed_eq}.\n"
-                    f"Please verify the provided steady state relationships are correct."
-                )
-
-        try:
-            eqs_to_solve = [eq for i, eq in enumerate(simplified_eqs) if not zeros[i]]
-        except TypeError:
-            msg = "Found the following loose symbols during simplification:\n"
-            # Something didn't reduce, figure out what and show the user
-            for i, eq in enumerate(zeros):
-                loose_symbols = [
-                    x for x in eq.atoms() if isinstance(x, (sp.Symbol, TimeAwareSymbol))
-                ]
-                if len(loose_symbols) > 0:
-                    msg += (
-                        f"Equation {i}: "
-                        + ", ".join([x.name for x in loose_symbols])
-                        + "\n"
-                    )
-
-            raise ValueError(msg)
-
-        return eqs_to_solve
-
-    def solve_steady_state(
-        self,
-        apply_user_simplifications: bool | None = True,
-        model_is_linear: bool | None = True,
-        optimizer_kwargs: dict[str, Any] | None = None,
-        method: str | None = "root",
-        use_jac: bool | None = True,
-        use_hess: bool | None = True,
-    ) -> Callable:
-        """
-        Solving of the steady state proceeds in three steps: solve calibrating equations (if any), gather user provided
-        equations into a function, then solve the remaining equations.
-
-        Calibrating equations are handled first because if the user passed a complete steady state solution, it is
-        unlikely to include solutions for calibrating equations. Calibrating equations are then combined with
-        user supplied equations, and we check if everything necessary to solve the model is now present. If not,
-        a final optimizer step runs to solve for the remaining variables.
-
-        Note that no checks are done in this function to validate the steady state solution. If a user supplies an
-        incorrect steady state, this function will not catch it. It will, however, still fail if an optimizer fails
-        to find a solution.
-
-        Parameters
-        ----------
-        apply_user_simplifications: bool
-            If true, substitute all equations using the steady-state equations provided in the steady_state block
-            of the GCN file.
-        model_is_linear: bool
-            A flag indicating that the model has already been linearized by the user. In this case, the steady state
-            can be obtained simply by forming an augmented matrix and finding its reduced row-echelon form. If True,
-            all other arguments to this function have no effect. Default is False.
-        optimizer_kwargs: dict
-            A dictionary of keyword arguments to pass to the scipy optimizer, either root or minimize. See the docstring
-            for scipy.optimize.root or scipy.optimize.minimize for more information.
-        method: str, default: "root"
-            Whether to seek the steady state via root finding algorithm or via minimization of squared errors. "root"
-            requires that the number of unknowns be equal to the number of equations; this assumption can be violated
-            if the user provides only a subset of steady-state relationship (and this subset does not result in
-            elimination of model equations via substitution).
-            One of "root" or "minimize".
-        use_jac: bool
-            A flag indicating whether to use the Jacobian of the steady-state system when solving. Can help the
-            solver on complex problems, but symbolic computation may be slow on large problems. Default is True.
-        use_hess: bool
-            A flag indicating whether to use the Hessian of the loss function of the steady-state system when solving.
-            Ignored if method is "root", as these routines do not use Hessian information.
-
-        Returns
-        -------
-        f_ss: Callable
-            A function that maps a dictionary of parameters to steady state values for all system variables and
-            calibrated parameters.
-        """
-
-        param_dict = self.free_param_dict.copy().to_sympy()
-        params = list(param_dict.keys())
-        calib_params = self.params_to_calibrate
-        user_provided = (
-            self.steady_state_relationships.copy().to_sympy().float_to_values()
-        )
-        ss_eqs = self.steady_state_system.copy()
-        calib_eqs = self.calibrating_equations.copy()
-        all_eqs = ss_eqs + calib_eqs
-
-        all_vars_sym = list(self.steady_state_dict.to_sympy().keys())
-        all_vars_and_calib_sym = all_vars_sym + self.params_to_calibrate
-
-        # This can be skipped if we're working on a linear model (there should be no user simplifications)
-        if apply_user_simplifications and not model_is_linear:
-            eqs_to_solve = self.apply_user_simplifications()
-        else:
-            eqs_to_solve = all_eqs
-
-        vars_sym = sorted(
-            list(
-                {
-                    x
-                    for eq in eqs_to_solve
-                    for x in eq.atoms()
-                    if isinstance(x, TimeAwareSymbol)
-                }
-            ),
-            key=lambda x: x.name,
-        )
-
-        vars_and_calib_sym = vars_sym + calib_params
-
-        k_vars = len(vars_sym)
-        k_calib = len(calib_params)
-        n_eq = len(eqs_to_solve)
-        n_loose = len(vars_and_calib_sym)
-
-        if (n_eq != n_loose) and (n_eq > 0) and (method == "root"):
-            raise ValueError(
-                'method = "root" is only possible when the number of equations (after substitution of '
-                "user-provided steady-state relationships) is equal to the number of (remaining) "
-                f"variables.\nFound {n_eq} equations and {k_vars + k_calib} variables. This can happen if "
-                f"user-provided steady-state relationships do not result in elimination of model "
-                f"equations after substitution. \nCheck the provided steady state relationships, or "
-                f'use method = "minimize" to attempt to solve via minimization of squared errors.'
-            )
-
-        # Get residuals for all equations, regardless of how much simplification was done
-        f_ss_resid = numba_lambdify(
-            exog_vars=all_vars_and_calib_sym, endog_vars=params, expr=[all_eqs]
-        )
-
-        if model_is_linear:
-            steady_state_values = self._solve_linear_steady_state()
-            f_ss = numba_lambdify(exog_vars=params, expr=steady_state_values)
-
-            def ss_func(param_dict):
-                success = True
-                params = np.array(list(param_dict.values()))
-
-                # Need to ravel because the result of Ab.rref() is a column vector
-                ss_values = f_ss(params).ravel()
-                result_dict = SymbolDictionary(
-                    dict(zip(all_vars_and_calib_sym, ss_values))
-                )
-
-                ss_dict = self.steady_state_dict.float_to_values().to_sympy().copy()
-                calib_dict = SymbolDictionary(
-                    dict(zip(self.params_to_calibrate, [np.inf] * k_calib))
-                )
-
-                for k in ss_dict.keys():
-                    ss_dict[k] = result_dict[k]
-                for k in calib_dict.keys():
-                    calib_dict[k] = result_dict[k]
-
-                return {
-                    "ss_dict": ss_dict.to_string(),
-                    "calib_dict": calib_dict.to_string(),
-                    "resids": np.array(
-                        f_ss_resid(
-                            np.array(
-                                list(ss_dict.values()) + list(calib_dict.values())
-                            ),
-                            params,
-                        )
-                    ),
-                    "success": success,
-                }
-
-            return ss_func
-
-        f_user = numba_lambdify(
-            exog_vars=vars_and_calib_sym,
-            endog_vars=params,
-            expr=[list(user_provided.values())],
-        )
-
-        optimizer_required = True
-        f_jac_ss = None
-        f_hess_ss = None
-
-        if n_eq == 0:
-            optimizer_required = False
-
-        elif method == "root":
-            f_ss = numba_lambdify(
-                exog_vars=vars_and_calib_sym, endog_vars=params, expr=[eqs_to_solve]
-            )
-
-            if use_jac:
-                jac = sp.Matrix(
-                    [[eq.diff(x) for x in vars_and_calib_sym] for eq in eqs_to_solve]
-                )
-                f_jac_ss = numba_lambdify(
-                    exog_vars=vars_and_calib_sym, endog_vars=params, expr=jac
-                )
-
-        elif method == "minimize":
-            # For minimization, need to form a loss function (use L2 norm -- better options?).
-            loss = sum([eq**2 for eq in eqs_to_solve])
-            f_loss = numba_lambdify(
-                exog_vars=vars_and_calib_sym, endog_vars=params, expr=[loss]
-            )
-            if use_jac:
-                jac = [loss.diff(x) for x in vars_and_calib_sym]
-
-                f_jac_ss = numba_lambdify(
-                    exog_vars=vars_and_calib_sym, endog_vars=params, expr=[jac]
-                )
-
-            if use_hess:
-                hess = sp.hessian(loss, vars_and_calib_sym)
-                f_hess_ss = numba_lambdify(
-                    exog_vars=vars_and_calib_sym, endog_vars=params, expr=hess
-                )
-
-        optimizer_kwargs = self._validate_optimizer_kwargs(
-            optimizer_kwargs, n_eq, method, use_jac, use_hess
-        )
-
-        def ss_func(param_dict):
-            params = np.array(list(param_dict.values()))
-
-            if optimizer_required:
-                if "x0" not in optimizer_kwargs.keys():
-                    optimizer_kwargs["x0"] = np.full(k_vars + k_calib, 0.8)
-                with catch_warnings():
-                    simplefilter("ignore")
-                    if method == "root":
-                        optim = optimize.root(
-                            f_ss, jac=f_jac_ss, args=params, **optimizer_kwargs
-                        )
-                    elif method == "minimize":
-                        optim = optimize.minimize(
-                            f_loss,
-                            jac=f_jac_ss,
-                            hess=f_hess_ss,
-                            args=params,
-                            **optimizer_kwargs,
-                        )
-
-                optim_dict = SymbolDictionary(dict(zip(vars_and_calib_sym, optim.x)))
-                success = optim.success
-            else:
-                optim_dict = SymbolDictionary()
-                success = True
-
-            ss_dict = self.steady_state_dict.float_to_values().to_sympy().copy()
-            calib_dict = SymbolDictionary(
-                dict(zip(self.params_to_calibrate, [np.inf] * k_calib))
-            )
-            user_dict = SymbolDictionary(
-                dict(
-                    zip(
-                        user_provided.keys(),
-                        f_user(np.array(list(optim_dict.values())), params),
-                    )
-                )
-            )
-
-            for k in all_vars_sym:
-                if k in optim_dict.keys():
-                    ss_dict[k] = optim_dict[k]
-                elif k in user_provided.keys():
-                    ss_dict[k] = user_dict[k]
-                else:
-                    raise ValueError(
-                        f"Could not find {k} among either optimizer or user provided solutions"
-                    )
-
-            for k in calib_params:
-                if k in optim_dict.keys():
-                    calib_dict[k] = optim_dict[k]
-                elif k in user_provided.keys():
-                    calib_dict[k] = user_dict[k]
-                else:
-                    raise ValueError(
-                        f"Could not find {k} among either optimizer or user provided solutions"
-                    )
-
-            ss_dict.sort_keys(inplace=True)
-            calib_dict.sort_keys(inplace=True)
-
-            return {
-                "ss_dict": ss_dict.to_string(),
-                "calib_dict": calib_dict.to_string(),
-                "resids": np.array(
-                    f_ss_resid(
-                        np.array(list(ss_dict.values()) + list(calib_dict.values())),
-                        params,
-                    )
-                ),
-                "success": success,
-            }
-
-        return ss_func
-
-    def _solve_linear_steady_state(self) -> list[sp.Add]:
-        """
-        If the model is linear, we can quickly solve for the steady state by putting everything into a matrix and
-        getting the reduced row-echelon form.
-
-        # TODO: Potentially save a "reverse deterministic sub" dict for use here.
-
-        Returns
-        -------
-        steady_state_values, list
-            A list of closed-form solutions to the steady-state, one per
-        """
-
-        shock_subs = {shock.to_ss(): 0 for shock in self.shocks}
-
-        all_vars_sym = list(self.steady_state_dict.to_sympy().keys())
-
-        all_vars_and_calib_sym = self.variables + self.params_to_calibrate
-        all_vars_and_calib_sym_ss = all_vars_sym + self.params_to_calibrate
-        all_eqs = self.system_equations + self.calibrating_equations
-
-        # simplifications make the next few steps a lot faster
-        sub_dict, simplified_system = sp.cse(all_eqs, ignore=all_vars_and_calib_sym)
-        A, b = sp.linear_eq_to_matrix(
-            [eq_to_ss(eq).subs(shock_subs) for eq in simplified_system],
-            all_vars_and_calib_sym_ss,
-        )
-        Ab = sp.Matrix([[A, b]])
-        A_rref, _ = Ab.rref()
-
-        # Recursive substitution to undo any simplifications
-        steady_state_values = A_rref[:, -1].subs(sub_dict * len(sub_dict))
-
-        return steady_state_values
-
-    def _steady_state_fast(self, model_is_linear: bool | None = True):
-        param_dict = self.free_param_dict.copy().to_sympy()
-        params = list(param_dict.keys())
-
-        if model_is_linear:
-            steady_state_values = self._solve_linear_steady_state()
-        elif self._system_is_presolved():
-            steady_state_values = self.get_presolved_system()
-        else:
-            raise ValueError(
-                "Cannot get a fast steady state solution unless the model is linear or a full closed-form"
-                "solution is provided"
-            )
-
-        f_ss = numba_lambdify(exog_vars=params, expr=steady_state_values)
-        return f_ss
-
-
-class SymbolicSteadyStateSolver:
-    def __init__(self):
-        pass
-
-    @staticmethod
-    def score_eq(
-        eq: sp.Expr,
-        var_list: list[sp.Symbol],
-        state_vars: list[sp.Symbol],
-        var_penalty_factor: float = 25,
-        state_var_penalty_factor: float = 5,
-        length_penalty_factor: float = 1,
-    ) -> float:
-        """
-        Compute an "unfitness" score for an equation using three simple heuristics:
-            1. The number of jumper variables in the expression
-            2. The number of state variables in the expression
-            3. The total length of the expression
-
-        Expressions with the lowest unfitness will be selected. Setting a lower penalty for state variables will
-        push the system towards finding solutions expressed in state variables if a steady state is parameters only
-        cannot be found.
-
-        Parameters
-        ----------
-        eq: sp.Expr
-            A sympy expression representing a steady-state equation
-        var_list: list of sp.Symbol
-            A list of sympy symbols representing all variables in the model (state and jumper)
-        state_vars: list of sp.Symbol
-            A list of symbol symbols representing all state variables in the model
-        var_penalty_factor: float, default: 25
-            A penalty factor applied to unfitness for each jumper variable in the expression.
-        state_var_penalty_factor: float, default: 5
-            A penalty factor applied to unfitness for each control variable in the expression.
-        length_penalty_factor: float, default: 1
-            A penalty factor applied to each term in the expression
-
-        Returns
-        -------
-        unfitness: float
-            An unfitness score used to select potential substitutions between system equations
-        """
-
-        # If the equation is length zero, it's been reduced away and should never be selected.
-        if eq == 0:
-            return 10000
-
-        var_list = list(set(var_list) - set(state_vars))
-
-        # The equation with the LOWEST score will be chosen to substitute, so punishing state variables less
-        # ensures that equations that have only state variables will be chosen more often.
-        var_penalty = len([x for x in eq.atoms() if x in var_list]) * var_penalty_factor
-        state_var_penalty = (
-            len([x for x in eq.atoms() if x in state_vars]) * state_var_penalty_factor
-        )
-
-        # Prefer shorter equations
-        length_penalty = eq.count_ops() * length_penalty_factor
-
-        return var_penalty + state_var_penalty + length_penalty
-
-    @staticmethod
-    def solve_and_return(eq: sp.Expr, v: sp.Symbol) -> sp.Expr:
-        """
-        Attempt to solve an expression for a given variable. Returns 0 if the expression is not solvable or if the
-        given variable does not appear in the expression. If multiple solutions are found, only the first one is
-        returned.
-
-        Parameters
-        ----------
-        eq: sp.Expr
-            A sympy expression
-        v: sp.Symbol
-            A sympy symbol
-
-        Returns
-        -------
-        solution: sp.Expr
-            Given f(x, ...) =  0, returns x = g(...) if possible, or 0 if not.
-        """
-
-        if v not in eq.atoms():
-            return sp.Float(0)
-        try:
-            solution = sp.solve(eq, v)
-        except Exception:
-            return sp.Float(0)
-
-        if len(solution) > 0:
-            return solution[0]
-
-        return sp.Float(0)
-
-    @staticmethod
-    def clean_substitutions(
-        sub_dict: dict[sp.Symbol, sp.Expr],
-    ) -> dict[sp.Symbol, sp.Expr]:
-        """
-        "Cleans" a dictionary of substitutions by:
-            1. Delete substitutions in the form of x=x or x=0 (x=0 implies the substitution is redundant with other
-                substitutions in sub_dict)
-            2. If a substitution is of the form x = f(x, ...), attempts to solve the expression x - f(x, ...) = 0 for x,
-                and deletes the substitution if no solution exists.
-            3. Apply all substitutions in sub_dict to expressions in sub_dict to ensure older solutions remain up to
-                date with newly found solutions.
-
-        Parameters
-        ----------
-        sub_dict: dict
-            Dictionary of sp.Symbol keys and sp.Expr values to be passed to the subs method of sympy expressions.
-
-        Returns
-        -------
-        sub_dict: dict
-            Cleaned dictionary of sympy substitutions
-        """
-        result = sub_dict.copy()
-
-        for k, eq in sub_dict.items():
-            # Remove invalid or useless substitutions
-            if eq == 0 or k == eq:
-                del result[k]
-                continue
-
-            # Solve for the sub variable if necessary
-            elif k in eq.atoms():
-                try:
-                    eq = sp.solve(k - eq, k)[0]
-                except Exception:
-                    del result[k]
-                    continue
-            result[k] = eq
-
-        # Substitute subs into the sub dict
-        result = {k: v.subs(result) for k, v in result.items()}
-        return result
-
-    def get_candidates(
-        self,
-        system: list[sp.Expr],
-        variables: list[sp.Symbol],
-        state_variables: list[sp.Symbol],
-        var_penalty_factor: float = 25,
-        state_var_penalty_factor: float = 5,
-        length_penalty_factor: float = 1,
-        cores: int = -1,
-    ) -> dict[sp.Symbol, tuple[sp.Expr, float]]:
-        """
-        Attempt to solve every equation in the system for every variable. Scores the results using the score_eq
-        function, and returns (solution, score) pairs with the highest fitness (lowest unfitness).
-
-        Solving equations is parallelized using joblib.
-
-        Parameters
-        ----------
-        system: list of sp.Expr
-            List of steady state equations to be scored
-        variables: list of sp.Symbol
-            List of all variables among all steady state equations
-        state_variables: list of Sp.Symbol
-            List of all state variables among all steady state equations
-        var_penalty_factor: float, default: 25
-            A penalty factor applied to unfitness for each jumper variable in the expression.
-        state_var_penalty_factor: float, default: 5
-            A penalty factor applied to unfitness for each control variable in the expression.
-        length_penalty_factor: float, default: 1
-            A penalty factor applied to each term in the expression
-        cores: int, default -1
-            Number of cores over which to parallelize computation. Passed to joblib.Parallel. -1 for all available
-            cores.
-
-        Returns
-        -------
-        candidates: dict
-            A dictionary of candidate substitutions to simplify the steady state system. One candidate is produced
-            for each variable in the system. Keys are sp.Symbol, and values are (sp.Expr, float) tuples with the
-            candidate substitution and its fitness.
-        """
-        eq_vars = product(system, variables)
-
-        n = len(system)
-        k = len(variables)
-        args = (
-            variables,
-            state_variables,
-            var_penalty_factor,
-            state_var_penalty_factor,
-            length_penalty_factor,
-        )
-
-        with Parallel(cores) as pool:
-            solutions = pool(delayed(self.solve_and_return)(eq, v) for eq, v in eq_vars)
-            scores = np.array(
-                pool(delayed(self.score_eq)(eq, *args) for eq in solutions)
-            )
-
-        score_matrix = scores.reshape(n, k)
-        idx_matrix = np.arange(n * k).reshape(n, k)
-        best_idx = idx_matrix[score_matrix.argmin(axis=0), np.arange(k)]
-
-        return dict(zip(variables, [(solutions[idx], scores[idx]) for idx in best_idx]))
-
-    @staticmethod
-    def make_solved_subs(sub_dict, assumptions):
-        res = {}
-        for k, v in sub_dict.items():
-            if not any([isinstance(x, TimeAwareSymbol) for x in v.atoms()]):
-                if v == 1:
-                    continue
-                res[v] = sp.Symbol(k.name + r"^\star", **assumptions[k.base_name])
-
-        return res
-
-    def solve_symbolic_steady_state(
-        self,
-        mod,
-        top_k=3,
-        var_penalty_factor=25,
-        state_var_penalty_factor=5,
-        length_penalty_factor=1,
-        cores=-1,
-        zero_tol=12,
-    ):
-        ss_vars = [x.to_ss() for x in mod.variables]
-        state_vars = [x for x in mod.variables if x.base_name == "Y"]
-        ss_system = mod.steady_state_system
-
-        system = ss_system.copy()
-        calib_eqs = [
-            var - eq
-            for var, eq in zip(mod.params_to_calibrate, mod.calibrating_equations)
-        ]
-        system.extend(calib_eqs)
-
-        params = list(mod.free_param_dict.to_sympy().keys())
-        sub_dict = {}
-        unsolved_dict = {}
-
-        while True:
-            candidates = self.get_candidates(
-                system,
-                ss_vars,
-                state_vars,
-                var_penalty_factor=var_penalty_factor,
-                state_var_penalty_factor=state_var_penalty_factor,
-                length_penalty_factor=length_penalty_factor,
-                cores=cores,
-            )
-
-            scores = np.array([score for eq, score in candidates.values()])
-            print(scores)
-            top_k_score_idxs = scores.argsort()[:top_k]
-            for idx in top_k_score_idxs:
-                key = list(candidates.keys())[idx]
-                if candidates[key][0] == 0:
-                    continue
-                sub_dict[key] = candidates[key][0]
-
-            sub_dict = self.clean_substitutions(sub_dict)
-
-            system = [eq.subs(sub_dict) for eq in system]
-            system = [
-                eq
-                for eq in system
-                if not self.test_expr_is_zero(
-                    eq.subs(unsolved_dict), params, tol=zero_tol
-                )
-            ]
-            solved_dict = self.make_solved_subs(sub_dict, mod.assumptions)
-            unsolved_dict = {v: k.subs(unsolved_dict) for k, v in solved_dict.items()}
-            system = [eq.subs(solved_dict) for eq in system]
-
-            if len(system) == 0:
-                break
-
-            if min(scores) > 100:
-                break
-
-        to_solve = {
-            x for eq in system for x in eq.atoms() if isinstance(x, TimeAwareSymbol)
-        }
-        system = [eq.simplify() for eq in system]
-        try:
-            final_solutions = sp.solve(system, to_solve, dict=True)
-        except NotImplementedError:
-            final_solutions = [{}]
-
-        return [sub_dict.update(d) for d in final_solutions]
-
-
-# from functools import partial
-# from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-# from warnings import catch_warnings, simplefilter
-#
-# import numpy as np
-# import sympy as sp
-# from numpy.typing import ArrayLike
-# from scipy import optimize
-#
-# from gEconpy.classes.containers import SymbolDictionary
-#
-# from gEconpy.shared.typing import sp.Symbol
-# from gEconpy.shared.utilities import (
-#     float_values_to_sympy_float,
-#     is_variable,
-#     merge_dictionaries,
-#     merge_functions,
-#     safe_string_to_sympy,
-#     sequential,
-#     sort_dictionary,
-#     string_keys_to_sympy,
-#     substitute_all_equations,
-#     symbol_to_string,
-#     sympy_keys_to_strings,
-#     sympy_number_values_to_floats,
-# )
-#
-#
-# class SteadyStateSolver:
-#     def __init__(self, model):
-#
-#         self.variables: List[sp.Symbol] = model.variables
-#         self.shocks: List[sp.Add] = model.shocks
-#
-#         self.n_variables: int = model.n_variables
-#
-#         self.free_param_dict: SymbolDictionary[str, float] = model.free_param_dict
-#         self.params_to_calibrate: List[sp.Symbol] = model.params_to_calibrate
-#         self.calibrating_equations: List[sp.Add] = model.calibrating_equations
-#         self.system_equations: List[sp.Add] = model.system_equations
-#         self.steady_state_relationships: SymbolDictionary[
-#             str, Union[float, sp.Add]
-#         ] = model.steady_state_relationships
-#
-#         self.steady_state_system: List[sp.Add] = []
-#         self.steady_state_dict: SymbolDictionary[str, float] = SymbolDictionary()
-#         self.steady_state_solved: bool = False
-#
-#         self.f_calib_params: Callable = lambda *args, **kwargs: {}
-#         self.f_ss_resid: Callable = lambda *args, **kwargs: np.inf
-#         self.f_ss: Callable = lambda *args, **kwargs: np.inf
-#
-#         self.build_steady_state_system()
-#
-#     def build_steady_state_system(self):
-#         self.steady_state_system = []
-#
-#         all_atoms = [
-#             x for eq in self.system_equations for x in eq.atoms() if is_variable(x)
-#         ]
-#         all_variables = set(all_atoms) - set(self.shocks)
-#         ss_sub_dict = {variable: variable.to_ss() for variable in set(all_variables)}
-#         unique_ss_variables = list(set(list(ss_sub_dict.values())))
-#
-#         steady_state_dict = dict.fromkeys(unique_ss_variables, None)
-#         steady_state_dict = (SymbolDictionary(steady_state_dict)
-#                              .to_string()
-#                              .sort_keys())
-#
-#         self.steady_state_dict = steady_state_dict
-#
-#         for shock in self.shocks:
-#             ss_sub_dict[shock] = 0
-#
-#         for eq in self.system_equations:
-#             self.steady_state_system.append(eq.subs(ss_sub_dict))
-#
-#     def solve_steady_state(
-#         self,
-#         param_bounds: Optional[Dict[str, Tuple[float, float]]] = None,
-#         optimizer_kwargs: Optional[Dict[str, Any]] = None,
-#         use_jac: Optional[bool] = False,
-#     ) -> Callable:
-#         """
-#
-#         Parameters
-#         ----------
-#         param_bounds: dict
-#             A dictionary of string, tuple(float, float) pairs, giving bounds for each variable or parameter to be
-#             solved for. Only used by certain optimizers; check the scipy docs. Pass it here instead of in
-#             optimizer_kwargs to make sure the correct variables have the correct bounds.
-#         optimizer_kwargs: dict
-#             A dictionary of keyword arguments to pass to the scipy optimizer, either root or root_scalar.
-#         use_jac: bool
-#             A flag to symbolically compute the Jacobain function of the model before optimization, can help the solver
-#             on complex problems.
-#
-#         Returns
-#         -------
-#         f_ss: Callable
-#             A function that maps a dictionary of parameters to steady state values for all system variables and
-#             calibrated parameters.
-#
-#         Solving of the steady state proceeds in three steps: solve calibrating equations (if any), gather user provided
-#         equations into a function, then solve the remaining equations.
-#
-#         Calibrating equations are handled first because if the user passed a complete steady state solution, it is
-#         unlikely to include solutions for calibrating equations. Calibrating equations are then combined with
-#         user supplied equations, and we check if everything necessary to solve the model is now present. If not,
-#         a final optimizer step runs to solve for the remaining variables.
-#
-#         Note that no checks are done in this function to validate the steady state solution. If a user supplies an
-#         incorrect steady state, this function will not catch it. It will, however, still fail if an optimizer fails
-#         to find a solution.
-#         """
-#         free_param_dict = self.free_param_dict.copy()
-#         parameters = list(free_param_dict.keys())
-#         variables = list(self.steady_state_dict.keys())
-#
-#         params_to_calibrate = [symbol_to_string(x) for x in self.params_to_calibrate]
-#
-#         n_to_calibrate = len(params_to_calibrate)
-#         has_calibrating_equations = n_to_calibrate > 0
-#
-#         params_and_variables = parameters + params_to_calibrate + variables
-#         steady_state_system = self.steady_state_system
-#
-#         # TODO: Move the creation of this residual function somewhere more logical
-#         self.f_ss_resid = sp.lambdify(params_and_variables, steady_state_system)
-#
-#         # Solve calibrating equations, if any.
-#         if has_calibrating_equations:
-#             f_calib, additional_solutions = self._solve_calibrating_equations(
-#                 param_bounds=param_bounds,
-#                 optimizer_kwargs=optimizer_kwargs,
-#                 use_jac=use_jac,
-#             )
-#         else:
-#             f_calib = lambda *args, **kwargs: {}
-#             additional_solutions = {}
-#
-#         solved_calib_params = list(f_calib(free_param_dict).keys())
-#
-#         # Gather user provided steady state solutions
-#         f_provided = self._gather_provided_solutions(solved_calib_params)
-#
-#         calib_dict = f_calib(free_param_dict)
-#         var_dict = f_provided(free_param_dict, calib_dict)
-#
-#         # If we have everything we're done. We don't need to use final_f, set it to return an empty dictionary.
-#         if (
-#             set(params_and_variables) - set(var_dict.keys()).union(calib_dict.keys())
-#         ) == set(free_param_dict.keys()):
-#             f_ss = self._create_final_function(
-#                 final_f=lambda x: {}, f_calib=f_calib, f_provided=f_provided
-#             )
-#
-#         else:
-#             final_f = self._solve_remaining_equations(
-#                 calib_dict=calib_dict,
-#                 var_dict=var_dict,
-#                 additional_solutions=additional_solutions,
-#                 param_bounds=param_bounds,
-#                 optimizer_kwargs=optimizer_kwargs,
-#                 use_jac=use_jac,
-#             )
-#             f_ss = self._create_final_function(
-#                 final_f=final_f, f_calib=f_calib, f_provided=f_provided
-#             )
-#
-#         return f_ss
-#
-#
-#     def _solve_calibrating_equations(
-#         self,
-#         param_bounds: Optional[Dict[str, Tuple[float, float]]],
-#         optimizer_kwargs: Optional[Dict[str, Any]],
-#         use_jac: bool = False,
-#     ) -> Tuple[Callable, Dict]:
-#         """
-#         Parameters
-#         ----------
-#         param_bounds: dict
-#             See docstring of solve_steady_state for details
-#         optimizer_kwargs: dict
-#             See docstring of solve_steady_state for details
-#         use_jac: bool
-#             See docstring of solve_steady_state for details
-#
-#         Returns
-#         -------
-#         f_calib: callable
-#             A function that maps param_dict to values of calibrated parameteres
-#         additional_solutions: dict
-#             A dictionary of symbolic solutions to non-calibrating parameters that were solved en passant and can be
-#             reused later
-#         """
-#         calibrating_equations = self.calibrating_equations
-#         symbolic_solutions = self.steady_state_relationships.copy()
-#         free_param_dict = self.free_param_dict.copy()
-#         steady_state_system = self.steady_state_system
-#
-#         parameters = list(free_param_dict.keys())
-#         variables = list(self.steady_state_dict.keys())
-#         params_to_calibrate = [symbol_to_string(x) for x in self.params_to_calibrate]
-#         params_and_variables = parameters + params_to_calibrate + variables
-#
-#         unknown_variables = set(variables).union(set(params_to_calibrate)) - set(
-#             symbolic_solutions.keys()
-#         )
-#
-#         n_to_calibrate = len(params_to_calibrate)
-#
-#         additional_solutions = {}
-#
-#         # Make substitutions
-#         calib_with_user_solutions = substitute_all_equations(
-#             calibrating_equations, symbolic_solutions
-#         )
-#
-#         # Try the heuristic solver
-#         calib_solutions, solved_mask = self.heuristic_solver(
-#             {},
-#             calib_with_user_solutions,
-#             calib_with_user_solutions,
-#             [safe_string_to_sympy(x) for x in params_and_variables],
-#         )
-#
-#         # Case 1: We found something! Refine the solution.
-#         if solved_mask.sum() > 0:
-#             # If the heuristic solver worked, we got solutions for variables that will allow us to go back and solve for
-#             # the calibrating parameters.
-#
-#             sub_dict = merge_dictionaries(free_param_dict, calib_solutions)
-#             more_solutions, solved_mask = self.heuristic_solver(
-#                 sub_dict,
-#                 substitute_all_equations(steady_state_system, sub_dict),
-#                 steady_state_system,
-#                 [safe_string_to_sympy(x) for x in params_and_variables],
-#             )
-#
-#             calib_solutions = {
-#                 key: value
-#                 for key, value in more_solutions.items()
-#                 if (key in params_to_calibrate)
-#             }
-#
-#             # We potentially pick up additional solutions from this heuristic pass, we can save them and use them later
-#             # to help the heuristic solver later.
-#             additional_solutions = {
-#                 key: value
-#                 for key, value in more_solutions.items()
-#                 if (key not in params_to_calibrate) and (key not in free_param_dict)
-#             }
-#
-#             calib_solutions = SymbolDictionary(calib_solutions).to_string().sort_keys().values_to_float()
-#             f_calib = lambda *args, **kwargs: calib_solutions
-#
-#         # Case 2: Found nothing, try to use an optimizer
-#         else:
-#             # Here we check how many equations are remaining to solve after accounting for the user's SS info.
-#             # We're looking for the case when all information is given EXCEPT the calibrating parameters.
-#             # If there is more than that, we handle it in the final pass.
-#             calib_remaining_to_solve = list(
-#                 set(unknown_variables) - set(symbolic_solutions.keys())
-#             )
-#             calib_n_eqs = len(calib_remaining_to_solve)
-#             if calib_n_eqs > len(calibrating_equations):
-#
-#                 def f_calib(*args, **kwargs):
-#                     return SymbolDictionary()
-#
-#                 return f_calib, SymbolDictionary()
-#
-#             # TODO: Is there a more elegant way to handle one equation vs many equations here?
-#             if calib_n_eqs == 1:
-#                 calib_with_user_solutions = calib_with_user_solutions[0]
-#
-#                 _f_calib = sp.lambdify(
-#                     calib_remaining_to_solve + parameters, calib_with_user_solutions
-#                 )
-#
-#                 def f_calib(x, kwargs):
-#                     return _f_calib(x, **kwargs)
-#
-#             else:
-#                 _f_calib = sp.lambdify(
-#                     calib_remaining_to_solve + parameters, calib_with_user_solutions
-#                 )
-#
-#                 def f_calib(args, kwargs):
-#                     return _f_calib(*args, **kwargs)
-#
-#             f_jac = None
-#             if use_jac:
-#                 f_jac = self._build_jacobian(
-#                     diff_variables=calib_remaining_to_solve,
-#                     additional_inputs=parameters,
-#                     equations=calib_with_user_solutions,
-#                 )
-#
-#             f_calib = self._bundle_symbolic_solutions_with_optimizer_solutions(
-#                 unknowns=calib_remaining_to_solve,
-#                 f=f_calib,
-#                 f_jac=f_jac,
-#                 param_dict=free_param_dict,
-#                 symbolic_solutions=calib_solutions,
-#                 n_eqs=calib_n_eqs,
-#                 output_names=calib_remaining_to_solve,
-#                 param_bounds=param_bounds,
-#                 optimizer_kwargs=optimizer_kwargs,
-#             )
-#
-#         return f_calib, additional_solutions
-#
-#     def _gather_provided_solutions(self, solved_calib_params) -> Callable:
-#         """
-#         Returns
-#         -------
-#         f_provided: Callable
-#             A function that takes model parameters, both calibrated and otherwise, as keywork arguments, and returns
-#             a dictionary of variable values according to steady state equations supplied by the user
-#         """
-#
-#         free_param_dict = self.free_param_dict.copy()
-#         symbolic_solutions = self.steady_state_relationships.copy()
-#         parameters = list(free_param_dict.keys())
-#
-#         _provided_lambda = sp.lambdify(
-#             parameters + solved_calib_params, [eq for eq in symbolic_solutions.values()]
-#         )
-#
-#         def f_provided(param_dict, calib_dict):
-#             return SymbolDictionary(dict(
-#                 zip(
-#                     symbolic_solutions.keys(),
-#                     _provided_lambda(**param_dict, **calib_dict),
-#                 )
-#             ))
-#
-#         return f_provided
-#
-#     def _solve_remaining_equations(
-#         self,
-#         calib_dict: Dict[str, float],
-#         var_dict: Dict[str, float],
-#         additional_solutions: Dict[str, float],
-#         param_bounds: Optional[Dict[str, Tuple[float, float]]],
-#         optimizer_kwargs: Optional[Dict[str, Any]],
-#         use_jac: bool,
-#     ) -> Callable:
-#         """
-#         Parameters
-#         ----------
-#         calib_dict: Dict
-#             A dictionary of solved calibrating parameters, if any.
-#         var_dict: Dict
-#             A dictionary of user-provided steady-state relationships, if any.
-#         additional_solutions:
-#             A dictionary of variable solutions found en passant by the heuristic solver while solving for the
-#             calibrated parameters, if any.
-#         param_bounds:
-#             See docstring of solve_steady_state for details
-#         optimizer_kwargs:
-#             See docstring of solve_steady_state for details
-#         use_jac:
-#             See docstring of solve_steady_state for details
-#
-#         Returns
-#         -------
-#         f_final: Callable
-#             A function that takes model parameters as keyword arguments and returns steady-state values for each
-#             model variable without an explicit symbolic solution.
-#         """
-#         free_param_dict = self.free_param_dict
-#         steady_state_system = self.steady_state_system
-#         calibrating_equations = self.calibrating_equations
-#
-#         parameters = list(free_param_dict.keys())
-#         variables = list(self.steady_state_dict.keys())
-#         params_to_calibrate = [symbol_to_string(x) for x in self.params_to_calibrate]
-#
-#         sub_dict = merge_dictionaries(calib_dict, var_dict, additional_solutions)
-#         params_and_variables = parameters + params_to_calibrate + variables
-#
-#         ss_solutions, solved_mask = self.heuristic_solver(
-#             sub_dict,
-#             substitute_all_equations(
-#                 steady_state_system + calibrating_equations, sub_dict, free_param_dict
-#             ),
-#             steady_state_system + calibrating_equations,
-#             [safe_string_to_sympy(x) for x in params_and_variables],
-#         )
-#
-#         ss_solutions = {
-#             key: value
-#             for key, value in ss_solutions.items()
-#             if key not in calib_dict.keys()
-#         }
-#         sub_dict.update(ss_solutions)
-#
-#         ss_remaining_to_solve = sorted(
-#             list(
-#                 set(variables + params_to_calibrate)
-#                 - set(ss_solutions.keys())
-#                 - set(calib_dict.keys())
-#             )
-#         )
-#
-#         unsolved_eqs = substitute_all_equations(
-#             [
-#                 eq
-#                 for idx, eq in enumerate(steady_state_system + calibrating_equations)
-#                 if not solved_mask[idx]
-#             ],
-#             sub_dict,
-#         )
-#
-#         n_eqs = len(unsolved_eqs)
-#
-#         _f_unsolved_ss = sp.lambdify(ss_remaining_to_solve + parameters, unsolved_eqs)
-#
-#         def f_unsolved_ss(args, kwargs):
-#             return _f_unsolved_ss(*args, **kwargs)
-#
-#         f_jac = None
-#         if use_jac:
-#             f_jac = self._build_jacobian(
-#                 diff_variables=ss_remaining_to_solve,
-#                 additional_inputs=parameters,
-#                 equations=unsolved_eqs,
-#             )
-#
-#         f_final = self._bundle_symbolic_solutions_with_optimizer_solutions(
-#             unknowns=ss_remaining_to_solve,
-#             f=f_unsolved_ss,
-#             f_jac=f_jac,
-#             param_dict=free_param_dict,
-#             symbolic_solutions=ss_solutions,
-#             n_eqs=n_eqs,
-#             output_names=ss_remaining_to_solve,
-#             param_bounds=param_bounds,
-#             optimizer_kwargs=optimizer_kwargs,
-#         )
-#
-#         return f_final
-#
-#     def _create_final_function(self, final_f, f_calib, f_provided):
-#         """
-#
-#         Parameters
-#         ----------
-#         final_f: Callable
-#             Function generated by solve_remaining_equations
-#         f_calib: Callable
-#             Function generated by _solve_calibrating_equations
-#         f_provided: Callable
-#             Function generated by _gather_provided_solutions
-#
-#         Returns
-#         -------
-#         f_ss: Callable
-#             A single function wrapping the three steady state functions, that returns a complete solution to the
-#             model's steady state as two dictionaries: one with variable values, and one with calibrated parameter
-#             values.
-#         """
-#         calib_params = [x.name for x in self.params_to_calibrate]
-#         ss_vars = [x.to_ss().name for x in self.variables]
-#
-#         def combined_function(param_dict):
-#             ss_out = SymbolDictionary()
-#
-#             calib_dict = f_calib(param_dict).copy()
-#             var_dict = f_provided(param_dict, calib_dict).copy()
-#             final_dict = final_f(param_dict).copy()
-#
-#             for param in calib_params:
-#                 if param in final_dict.keys():
-#                     calib_dict[param] = final_dict[param]
-#                     del final_dict[param]
-#
-#             var_dict_final = {}
-#             for key in var_dict:
-#                 if key in ss_vars:
-#                     var_dict_final[key] = var_dict[key]
-#
-#             ss_out = ss_out | var_dict_final | final_dict
-#
-#             return ss_out.sort_keys(), calib_dict.sort_keys()
-#
-#         return combined_function
-#
-#     def _bundle_symbolic_solutions_with_optimizer_solutions(
-#         self,
-#         unknowns: List[str],
-#         f: Callable,
-#         f_jac: Optional[Callable],
-#         param_dict: Dict[str, float],
-#         symbolic_solutions: Optional[Dict[str, float]],
-#         n_eqs: int,
-#         output_names: List[str],
-#         param_bounds: Optional[Dict[str, Tuple[float, float]]],
-#         optimizer_kwargs: Optional[Dict[str, Any]],
-#     ) -> Callable:
-#
-#         parameters = list(param_dict.keys())
-#
-#         optimize_wrapper = partial(
-#             self._optimize_dispatcher,
-#             unknowns=unknowns,
-#             f=f,
-#             f_jac=f_jac,
-#             n_eqs=n_eqs,
-#             param_bounds=param_bounds,
-#             optimizer_kwargs=optimizer_kwargs,
-#         )
-#         _symbolic_lambda = sp.lambdify(parameters, list(symbolic_solutions.values()))
-#
-#         def solve_optimizer_variables(param_dict):
-#             return SymbolDictionary(dict(zip(output_names, optimize_wrapper(param_dict))))
-#
-#         def solve_symbolic_variables(param_dict):
-#             return SymbolDictionary(dict(zip(symbolic_solutions.keys(), _symbolic_lambda(**param_dict))))
-#
-#         wrapped_f = merge_functions(
-#             [solve_optimizer_variables, solve_symbolic_variables], param_dict
-#         )
-#
-#         return wrapped_f
-#
-#     def _optimize_dispatcher(
-#         self, param_dict, unknowns, f, f_jac, n_eqs, param_bounds, optimizer_kwargs
-#     ):
-#         if n_eqs == 1:
-#             optimize_fun = optimize.root_scalar
-#             if param_bounds is None:
-#                 param_bounds = self._prepare_param_bounds(None, 1)[0]
-#             optimizer_kwargs = self._prepare_optimizer_kwargs(optimizer_kwargs, n_eqs)
-#             optimizer_kwargs.update(
-#                 dict(args=param_dict, method="brentq", bracket=param_bounds)
-#             )
-#
-#         else:
-#             optimize_fun = optimize.root
-#
-#             optimizer_kwargs = self._prepare_optimizer_kwargs(optimizer_kwargs, n_eqs)
-#             optimizer_kwargs.update(dict(args=param_dict, jac=f_jac))
-#
-#         with catch_warnings():
-#             simplefilter("ignore")
-#             result = optimize_fun(f, **optimizer_kwargs)
-#
-#         if hasattr(result, "converged") and result.converged:
-#             return np.atleast_1d(result.root)
-#         elif hasattr(result, "converged") and not result.converged:
-#             raise ValueError(
-#                 f"Optimization failed while solving for steady state solution of the following "
-#                 f'variables: {", ".join([symbol_to_string(x) for x in unknowns])}\n\n {result}'
-#             )
-#
-#         if hasattr(result, "success") and result.success:
-#             return result.x
-#
-#         elif hasattr(result, "success") and not result.success:
-#             raise ValueError(
-#                 f"Optimization failed while solving for steady state solution of the following "
-#                 f'variables: {", ".join([symbol_to_string(x) for x in unknowns])}\n\n {result}'
-#             )
-#
-#     @staticmethod
-#     def _build_jacobian(
-#         diff_variables: List[Union[str, sp.Symbol]],
-#         additional_inputs: List[Union[str, sp.Symbol]],
-#         equations: List[sp.Add],
-#     ) -> Callable:
-#         """
-#         Parameters
-#         ----------
-#         diff_variables: list
-#             A list of variables, as either TimeAwareSymbols or strings that the equations will be differentiated with
-#             respect to.
-#         additional_inputs: list
-#             A list of variables or parameters that will be arguments to the Jacobian function, but that will NOT
-#             be used in differentiation (i.e. the model parameters)
-#         equations: list
-#             A list of equations to be differentiated
-#
-#         Returns
-#         -------
-#         f_jac: Callable
-#             A function that takes diff_variables + additional_inputs as keyword arguments and returns an
-#             len(equations) x len(diff_variables) matrix of derivatives.
-#         """
-#         equations = np.atleast_1d(equations)
-#         sp_variables = [safe_string_to_sympy(x) for x in diff_variables]
-#         _f_jac = sp.lambdify(
-#             diff_variables + additional_inputs,
-#             [[eq.diff(x) for x in sp_variables] for eq in equations],
-#         )
-#
-#         def f_jac(args, kwargs):
-#             return np.array(_f_jac(*args, **kwargs))
-#
-#         return f_jac
-#
-#     @staticmethod
-#     def _prepare_optimizer_kwargs(
-#         optimizer_kwargs: Optional[Dict[str, Any]], n_unknowns: int
-#     ) -> Dict[str, Any]:
-#         if optimizer_kwargs is None:
-#             optimizer_kwargs = {}
-#
-#         arg_names = list(optimizer_kwargs.keys())
-#         if "x0" not in arg_names:
-#             optimizer_kwargs["x0"] = np.full(n_unknowns, 0.8)
-#         if "method" not in arg_names:
-#             optimizer_kwargs["method"] = "hybr"
-#
-#         return optimizer_kwargs
-#
-#     @staticmethod
-#     def _prepare_param_bounds(
-#         param_bounds: Optional[List[Tuple[float, float]]], n_params
-#     ) -> List[Tuple[float, float]]:
-#         if param_bounds is None:
-#             bounds = [(1e-4, 0.999) for _ in range(n_params)]
-#         else:
-#             bounds = [(lower + 1e-4, upper - 1e-4) for lower, upper in param_bounds]
-#
-#         return bounds
-#
-#     def _get_n_unknowns_in_eq(self, eq: sp.Add) -> int:
-#         params_to_calibrate = (
-#             [] if self.params_to_calibrate is None else self.params_to_calibrate
-#         )
-#         unknown_atoms = [
-#             x for x in eq.atoms() if is_variable(x) or x in params_to_calibrate
-#         ]
-#         n_unknowns = len(list(set(unknown_atoms)))
-#
-#         return n_unknowns
-#
-#     def heuristic_solver(
-#         self,
-#         solution_dict: Dict[str, float],
-#         subbed_ss_system: List[Any],
-#         steady_state_system: List[Any],
-#         unknowns: List[str],
-#     ) -> Tuple[Dict[str, float], ArrayLike]:
-#         """
-#         Parameters
-#         ----------
-#         solution_dict: dict
-#             A dictionary of TimeAwareSymbol: float pairs, giving steady-state values that have already been determined
-#
-#         subbed_ss_system: list
-#             A list containing all unsolved steady state equations, pre-substituted with parameter values and known
-#             steady-state values.
-#
-#         steady_state_system: list
-#             A list containing all steady state equations, without substitution
-#
-#         unknowns: list
-#             A list of sympy variables containing unknown values to solve for; variables plus any unsolved calibrated
-#             parameters.
-#
-#         Returns
-#         -------
-#         It is likely that the GCN model will contain simple equations that amount to little more than parameters, for
-#         example declaring that P = 1 in a perfect competition setup. These types of simple expressions can be "solved"
-#         and removed from the system to reduce the dimensionality of the problem given to the numerical solver.
-#
-#         This function performs this simplification in a heuristic way in the following manner. We first look for
-#         "simple" equations, defined as those with only a single unknown variable. Solutions are then substituted back
-#         into the system, equations that have reduced to 0=0 as a result of substitution are removed, then we repeat
-#         the procedure to see if any additional equations have become heuristically solvable as a result of substitution.
-#
-#         The process terminates when no "simple" equations remain.
-#         """
-#
-#         solved_mask = np.array([eq == 0 for eq in subbed_ss_system])
-#         eq_to_var_dict = {}
-#         check_again_mask = np.full_like(solved_mask, True)
-#         solution_dict = sequential(
-#             solution_dict, [float_values_to_sympy_float, string_keys_to_sympy]
-#         )
-#
-#         numeric_solutions = solution_dict.copy()
-#
-#         while True:
-#             solution_dict = {
-#                 key: eq.subs(solution_dict) for key, eq in solution_dict.items()
-#             }
-#             subbed_ss_system = [
-#                 eq.subs(numeric_solutions).simplify() for eq in subbed_ss_system
-#             ]
-#
-#             n_unknowns = np.array(
-#                 [self._get_n_unknowns_in_eq(eq) for eq in subbed_ss_system]
-#             )
-#             eq_len = np.array([len(eq.atoms()) for eq in subbed_ss_system])
-#
-#             solvable_mask = (n_unknowns < 2) & (~solved_mask) & check_again_mask
-#
-#             # Sympy struggles with solving complicated functions inside powers, just avoid them. 5 is a magic number
-#             # for the maximum number of variable in a function to be considered "complicated", needs tuning.
-#             has_power_argument = np.array(
-#                 [
-#                     any([isinstance(arg, sp.core.power.Pow)] for arg in eq.args)
-#                     for eq in subbed_ss_system
-#                 ]
-#             )
-#             solvable_mask &= ~(has_power_argument & (eq_len > 5))
-#
-#             if sum(solvable_mask) == 0:
-#                 break
-#
-#             for idx in np.flatnonzero(solvable_mask):
-#                 # Putting the solved = True flag here is ugly, but it catches equations
-#                 # that are 0 = 0 after substitution
-#                 solved_mask[idx] = True
-#
-#                 eq = subbed_ss_system[idx]
-#
-#                 variables = list({x for x in eq.atoms() if x in unknowns})
-#                 if len(variables) > 0:
-#                     eq_to_var_dict[variables[0]] = idx
-#
-#                     try:
-#                         symbolic_solution = sp.solve(
-#                             steady_state_system[idx], variables[0]
-#                         )
-#                     except NotImplementedError:
-#                         # There are functional forms sympy can't handle;  mark the equation as unsolvable and continue.
-#                         check_again_mask[idx] = False
-#                         solved_mask[idx] = False
-#                         continue
-#
-#                     # The solution should only ever be length 0 or 1, if it's more than 1 something went wrong. Haven't
-#                     # hit this case yet in testing.
-#                     if len(symbolic_solution) == 1:
-#                         solution_dict[variables[0]] = symbolic_solution[0]
-#                         numeric_solutions[variables[0]] = (
-#                             symbolic_solution[0]
-#                             .subs(self.free_param_dict)
-#                             .subs(numeric_solutions)
-#                         )
-#                         check_again_mask[:] = True
-#                         solved_mask[idx] = True
-#
-#                     else:
-#                         # Solver failed; something went wrong. Skip this equation.
-#                         solved_mask[idx] = False
-#                         check_again_mask[idx] = False
-#
-#                 else:
-#                     check_again_mask[idx] = False
-#
-#         numeric_solutions = sympy_number_values_to_floats(numeric_solutions)
-#         for key, eq in numeric_solutions.items():
-#             if not isinstance(eq, float):
-#                 del solution_dict[key]
-#                 solved_mask[eq_to_var_dict[key]] = False
-#
-#         solution_dict = sequential(
-#             solution_dict, [sympy_keys_to_strings, sympy_number_values_to_floats]
-#         )
-#
-#         return solution_dict, solved_mask
diff --git a/gEconpy/shared/utilities.py b/gEconpy/utilities.py
similarity index 53%
rename from gEconpy/shared/utilities.py
rename to gEconpy/utilities.py
index 997444a..55d0997 100644
--- a/gEconpy/shared/utilities.py
+++ b/gEconpy/utilities.py
@@ -1,37 +1,22 @@
+import logging
+
+from collections.abc import Callable
 from copy import copy
-from enum import EnumMeta
 from typing import Any
-from collections.abc import Callable
 
-import numba as nb
 import numpy as np
 import sympy as sp
 
-from gEconpy.classes.containers import SymbolDictionary, string_keys_to_sympy
-from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-
-
-class IterEnum(EnumMeta):
-    def __init__(self, *args, **kwargs):
-        self.__idx = 0
-        super().__init__(*args, **kwargs)
-
-    def __contains__(self, item):
-        return item in {v.value for v in self.__members__.values()}
+from scipy.optimize import OptimizeResult
 
-    def __len__(self):
-        return len(self.__members__)
-
-    def __iter__(self):
-        return self
+from gEconpy.classes.containers import (
+    SteadyStateResults,
+    SymbolDictionary,
+    string_keys_to_sympy,
+)
+from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
 
-    def __next__(self):
-        self.__idx += 1
-        try:
-            return list(self.__members__)[self.__idx - 1]
-        except IndexError:
-            self.__idx = 0
-            raise StopIteration
+_log = logging.getLogger(__name__)
 
 
 def flatten_list(items, result_list=None):
@@ -57,10 +42,26 @@ def set_equality_equals_zero(eq):
     return eq.rhs - eq.lhs
 
 
-def eq_to_ss(eq):
+def eq_to_ss(eq: sp.Expr, shocks: list[TimeAwareSymbol] | None = None):
+    if shocks is None:
+        shock_subs = {}
+    else:
+        shock_subs = {x.to_ss(): 0.0 for x in shocks}
+
     var_list = [x for x in eq.atoms() if isinstance(x, TimeAwareSymbol)]
-    sub_dict = dict(zip(var_list, [x.to_ss() for x in var_list]))
-    return eq.subs(sub_dict)
+    to_ss_subs = dict(zip(var_list, [x.to_ss() for x in var_list]))
+
+    return eq.subs(to_ss_subs).subs(shock_subs)
+
+
+def safe_to_ss(x: sp.Symbol):
+    """
+    Convert ``x`` to steady-state if it is TimeAware, or return it unchanged otherwise.
+    """
+
+    if isinstance(x, TimeAwareSymbol):
+        return x.to_ss()
+    return x
 
 
 def expand_subs_for_all_times(sub_dict: dict[TimeAwareSymbol, TimeAwareSymbol]):
@@ -108,6 +109,7 @@ def diff_through_time(eq, dx, discount_factor=1):
     while next_dydx != 0:
         next_dydx = eq.diff(dx)
         eq = step_equation_forward(eq) * discount_factor
+        discount_factor = step_equation_forward(discount_factor)
         total_dydx += next_dydx
 
     return total_dydx
@@ -127,7 +129,7 @@ def substitute_all_equations(eqs, *sub_dicts):
         for key in eqs:
             result[key] = (
                 eqs[key]
-                if isinstance(eqs[key], (int, float))
+                if isinstance(eqs[key], int | float)
                 else eqs[key].subs(sub_dict)
             )
         return result
@@ -151,8 +153,12 @@ def is_number(x: str):
 
     A small extension to the .isnumeric() string built-in method, to allow float values with "." to pass.
     """
-
-    return all([c in set("0123456789.") for c in x])
+    if isinstance(x, float | int):
+        return True
+    elif isinstance(x, str):
+        return all([c in set("0123456789.") for c in x])
+    else:
+        return False
 
 
 def sequential(x: Any, funcs: list[Callable]) -> Any:
@@ -190,7 +196,7 @@ def reduce_system_via_substitution(system, sub_dict):
 
 
 def merge_dictionaries(*dicts):
-    if not isinstance(dicts, (list, tuple)):
+    if not isinstance(dicts, list | tuple):
         return dicts
 
     result = {}
@@ -199,6 +205,18 @@ def merge_dictionaries(*dicts):
     return result
 
 
+def recursively_self_substitute_dict(sub_dict, max_iter=5):
+    eqs = list(sub_dict.values())
+    for i in range(max_iter):
+        new_eqs = substitute_all_equations(eqs, sub_dict)
+        sub_dict = substitute_all_equations(sub_dict, sub_dict)
+        no_changes = all([new_eq == old_eq for new_eq, old_eq in zip(new_eqs, eqs)])
+        eqs = new_eqs
+        if no_changes:
+            break
+    return {var: eq for var, eq in zip(sub_dict.keys(), eqs)}
+
+
 def make_all_var_time_combos(var_list):
     result = []
     for x in var_list:
@@ -207,123 +225,12 @@ def make_all_var_time_combos(var_list):
     return result
 
 
-def build_Q_matrix(
-    model_shocks: list[str],
-    shock_dict: dict[str, float] | None = None,
-    shock_cov_matrix: np.ndarray | None = None,
-    shock_std_priors: dict[str, Any] | None = None,
-    default_value: float | None = 0.01,
-) -> np.array:
-    """
-    Take different options for user input and reconcile them into a covariance matrix. Exactly one or zero of shock_dict
-    or shock_cov_matrix should be provided. Then, proceed according to the following logic:
-
-    - If `shock_cov_matrix` is provided, it is Q. Return it.
-    - If `shock_dict` is provided, insert these into a diagonal matrix at locations according to `model_shocks`.
-
-    For values missing from `shock_dict`, or if neither `shock_dict` nor `shock_cov_matrix` are provided:
-
-    - Fill missing values using the mean of the prior defined in `shock_priors`
-    - If no prior is set, fill the value with `default_value`.
-
-    Note that the only way to get off-diagonal elements is to explicitly pass the entire covariance matrix.
-
-    Parameters
-    ----------
-    model_shocks: list of str
-        List of model shock names, used to infer positions in the covariance matrix
-    shock_dict: dict of str, float
-        Dictionary of shock names and standard deviations to be used to build Q
-    shock_cov_matrix: ndarray
-        The shock covariance matrix. If provided, check that it is positive semi-definite, then return it.
-    shock_std_priors: dict of str, frozendist
-        Dictionary of model priors over shock standard deviations
-    default_value: float
-        A default value of fall back on if no other information is available about a shock's standard deviation
-
-    Raises
-    ---------
-    LinalgError
-        If the provided Q is not positive semi-definite
-    ValueError
-        If both model_shocks and shock_dict are provided
-
-    Returns
-    -------
-    Q: ndarray
-        Shock variance-covariance matrix
-    """
-    n = len(model_shocks)
-    if shock_dict is not None and shock_cov_matrix is not None:
-        raise ValueError("Both shock_dict and shock_cov_matrix cannot be provided.")
-
-    if shock_dict is None:
-        shock_dict = {}
-
-    if shock_cov_matrix is not None:
-        if not all([x == n for x in shock_cov_matrix.shape]):
-            raise ValueError(
-                f"Provided covariance matrix has shape {shock_cov_matrix.shape}, expected ({n}, {n})"
-            )
-        try:
-            # check that the result is PSD
-            np.linalg.cholesky(shock_cov_matrix)
-            return shock_cov_matrix
-        except np.linalg.LinAlgError:
-            raise np.linalg.LinAlgError("The provided Q is not positive semi-definite.")
-
-    Q = np.eye(len(model_shocks)) * default_value
-
-    if shock_dict is not None:
-        for i, shock in enumerate(model_shocks):
-            if shock in shock_dict:
-                Q[i, i] = shock_dict[shock]
-
-    if shock_std_priors is not None:
-        for i, shock in enumerate(model_shocks):
-            if shock not in shock_dict and shock in shock_std_priors:
-                Q[i, i] = shock_std_priors[shock].mean()
-
-    return Q
-
-
-@nb.njit(cache=True)
-def compute_autocorrelation_matrix(A, sigma, n_lags=5):
-    """Compute the autocorrelation matrix for the given state-space model.
-
-    Parameters
-    ----------
-    A : ndarray
-        An array of shape (n_endog, n_endog, n_lags) representing the transition matrix of the
-        state-space system.
-    sigma : ndarray
-        An array of shape (n_endog, n_endog) representing the variance-covariance matrix of the errors of
-        the transition equation.
-    n_lags : int, optional
-        The number of lags for which to compute the autocorrelation matrix.
-
-    Returns
-    -------
-    acov : ndarray
-        An array of shape (n_endog, n_lags) representing the autocorrelation matrix of the state-space process.
-    """
-
-    acov = np.zeros((A.shape[0], n_lags))
-    acov_factor = np.eye(A.shape[0])
-    for i in range(n_lags):
-        cov = acov_factor @ sigma
-        acov[:, i] = np.diag(cov) / np.diag(sigma)
-        acov_factor = A @ acov_factor
-
-    return acov
-
-
 def get_shock_std_priors_from_hyperpriors(shocks, priors, out_keys="parent"):
     """
     Extract a single key, value pair from the model hyper_priors.
 
     Parameters
-    -------
+    ----------
     shocks: list of sympy Symbols
         Model shocks
     priors: dict of key, tuple
@@ -362,18 +269,21 @@ def split_random_variables(param_dict, shock_names, obs_names):
 
     Parameters
     ----------
-    param_dict : Dict[str, float]
+    param_dict : dict
         A dictionary of parameters and their values.
-    shock_names : List[str]
+    shock_names : list of str
         A list of the names of shock variables.
-    obs_names : List[str]
+    obs_names : list of str
         A list of the names of observable variables.
 
     Returns
     -------
-    Tuple[Dict[str, float], Dict[str, float], Dict[str, float]]
-        A tuple containing three dictionaries: the first has parameters, the second has
-        all shock variances parameters, and the third has observation noise variances.
+    out_param_dict: dict
+        Dictionary mapping parameter names to values
+    shock_dict: dict
+        Dictionary mapping shock names to values
+    obs_dict: dict
+        Dictionary mapping names of observed variables to observation noise values
     """
 
     out_param_dict = SymbolDictionary()
@@ -389,3 +299,139 @@ def split_random_variables(param_dict, shock_names, obs_names):
             out_param_dict[k] = v
 
     return out_param_dict, shock_dict, obs_dict
+
+
+def postprocess_optimizer_res(
+    res: OptimizeResult,
+    res_dict: SteadyStateResults,
+    f_resid: Callable[..., np.ndarray],
+    f_jac: Callable[..., np.ndarray],
+    tol: float = 1e-6,
+    verbose: bool = True,
+) -> SteadyStateResults:
+    success = res.success
+
+    f_x = np.r_[[x.ravel() for x in f_resid(**res_dict)]]
+    df_dx = f_jac(**res_dict)
+
+    sse = (f_x**2).sum()
+    max_abs_error = np.max(np.abs(f_x))
+    grad_norm = np.linalg.norm(df_dx, ord=2)
+    abs_max_grad = np.max(np.abs(df_dx))
+
+    # Sometimes the optimizer is way too strict and returns success of False even if the point is pretty clearly
+    # minimum.
+    numeric_success = all(
+        condition < tol for condition in [sse, max_abs_error, grad_norm, abs_max_grad]
+    )
+
+    if numeric_success and not success:
+        word = " IS "
+    elif not numeric_success and not success:
+        word = " NOT "
+    else:
+        word = " "
+
+    line_1 = f"Steady state{word}found"
+    if numeric_success and not success:
+        line_1 += (
+            ", although optimizer returned success = False.\n"
+            "This can be ignored, but to silence this message, try reducing the solver-specific tolerance, "
+            "or use a different solution algorithm."
+        )
+
+    msg = (
+        f'{line_1}\n'
+        f'{"-"*80}\n'
+        f"{'Optimizer message':<30}{res.message}\n"
+        f"{'Sum of squared residuals':<30}{sse}\n"
+        f"{'Maximum absoluate error':<30}{max_abs_error}\n"
+        f"{'Gradient L2-norm at solution':<30}{grad_norm}\n"
+        f"{'Max abs gradient at solution':<30}{abs_max_grad}"
+    )
+
+    if verbose:
+        _log.info(msg)
+    res_dict.success = success | numeric_success
+    return res_dict
+
+
+def get_name(x: str | sp.Symbol, base_name=False) -> str:
+    """
+    Return the name of a string, TimeAwareSymbol, or sp.Symbol object.
+
+    Parameters
+    ----------
+    x : str, or sp.Symbol
+        The object whose name is to be returned. If str, x is directly returned.
+    base_name: bool
+        If True, return TimeAwareSymbol base name (the name without any time suffix)
+
+    Returns
+    -------
+    name: str
+        The name of the object.
+    """
+
+    if isinstance(x, str):
+        return x
+
+    elif isinstance(x, TimeAwareSymbol):
+        return x.safe_name if not base_name else x.base_name
+
+    elif isinstance(x, sp.Symbol):
+        return x.name
+
+
+def substitute_repeatedly(
+    expr: sp.Expr, sub_dict: dict[sp.Expr, sp.Expr], max_subs: int = 10
+) -> sp.Expr:
+    """
+    Repeatedly call ``expr = expr.sub(sub_dict)``. Useful when substitutions in ``sub_dict`` themselves require
+    substitution.
+
+    Parameters
+    ----------
+    expr: sp.Expr
+        Expression to substitute into
+    sub_dict: dict of sp.Expr, sp.Expr
+        Dictionary of substitutions
+    max_subs: int
+        Maximum number of substitutions to make. If the number of substitutions exceeds this number, the function
+        will return the expression as is.
+
+    Returns
+    -------
+    substituted_expr: sp.Expr
+        The expression with all substitutions made.
+    """
+    for i in range(max_subs):
+        new_expr = expr.subs(sub_dict)
+        if not any([new_expr.has(x) for x in sub_dict.keys()]):
+            return new_expr
+        expr = new_expr
+
+    return expr
+
+
+def simplify_matrix(A: sp.MutableMatrix):
+    """
+    Call ``sp.simplify`` on all cells of a matrix.
+
+    Parameters
+    ----------
+    A: sp.MutableMatrix
+        Matrix to simplify
+
+    Returns
+    -------
+    A: sp.MutableMatrix
+        Simplified matrix
+    """
+
+    for i in range(A.rows):
+        for j in range(A.cols):
+            expr = A[i, j]
+            A[i, j] = sp.simplify(expr)
+
+    return A
diff --git a/pyproject.toml b/pyproject.toml
index c4d8f72..9626e81 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,24 +1,143 @@
+[build-system]
+requires = ["setuptools", "versioneer[toml]"]
+build-backend = "setuptools.build_meta"
+
+
+[project]
+name = "gEconpy"
+dynamic = ['version']
+requires-python = ">=3.10, <3.13"
+authors = [{name="Jesse Grabowski", email='jessegrabowski@gmail.com'}]
+description = "A package for solving, estimating, and analyzing DSGE models"
+readme = 'README.md'
+license = { file = 'LICENSE.txt'}
+classifiers = [
+  "Development Status :: 2 - Pre-Alpha",
+  "Intended Audience :: Science/Research",
+  "Programming Language :: Python",
+  "Topic :: Scientific/Engineering",
+  "Operating System :: Microsoft :: Windows",
+  "Operating System :: POSIX",
+  "Operating System :: Unix",
+  "Operating System :: MacOS",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+]
+
+keywords = [
+    "dynamic stochastic general equlibrium",
+    "economics",
+    "macroeconomics",
+    "numerical",
+    "simulation",
+    "autodiff",
+    "bayesian statistics"
+]
+
+
+dependencies = [
+    "matplotlib",
+    "numba",
+    "numpy",
+    "pandas",
+    "pymc",
+    "preliz",
+    "pyparsing",
+    "pytensor",
+    "scipy",
+    "setuptools",
+    "sympy<1.13",
+    "sympytensor",
+    "xarray",
+  ]
+
+[project.optional-dependencies]
+dev = [
+    "pre-commit",
+    "pytest",
+    "pytest-cov",
+    "versioneer",
+    "numdifftools"
+]
+
+docs = [
+  "ipython",
+  "jupyter-sphinx",
+  "myst-nb",
+  "numpydoc",
+  "pre-commit",
+  "sphinx>=5",
+  "sphinx-copybutton",
+  "sphinx-design",
+  "sphinx-notfound-page",
+  "sphinx-sitemap",
+  "sphinx-codeautolink",
+  "sphinxcontrib-bibtex",
+  "pydata-sphinx-theme",
+  "watermark",
+]
+
+
+[tool.versioneer]
+VCS = "git"
+style = "pep440"
+versionfile_source = "gEconpy/_version.py"
+versionfile_build = "gEconpy/_version.py"
+tag_prefix = 'v'
+
+
 [tool.pytest.ini_options]
 minversion = "6.0"
 xfail_strict=true
+log_cli=true
+log_cli_level="INFO"
 filterwarnings = [
     "error",
-    "ignore::DeprecationWarning"]
+    "ignore::DeprecationWarning",
+    "ignore::RuntimeWarning"]
 env = ["NUMBA_DISABLE_JIT = 1"]
 
 [tool.ruff.lint]
-ignore = ["E741"]
-
-[tool.bumpver]
-current_version = "1.2.1"
-version_pattern = "MAJOR.MINOR.PATCH"
-commit_message  = "Bump version {old_version} -> {new_version}"
-commit          = true
-tag             = true
-push            = false
-
-[tool.bumpver.file_patterns]
-"pyproject.toml" = ['current_version = "{version}"']
-"setup.cfg" = ['version = {version}']
-"gEconpy/__init__.py" = ["{version}"]
-"docs/source/conf.py" = ["release = {version}"]
+select = ["D", "E", "F", "I", "UP", "W", "RUF"]
+ignore = [
+  "E501",   # Line length
+  "E741",   # Ambiguous variable name
+  "RUF001", # String contains ambiguous character (such as Greek letters)
+  "RUF002", # Docstring contains ambiguous character (such as Greek letters)
+  "RUF012", # Mutable class attributes should be annotated with `typing.ClassVar`
+  "D100",
+  "D101",
+  "D102",
+  "D103",
+  "D104",
+  "D105",
+  "D107",
+  "D200",
+  "D202",
+  "D203",
+  "D204",
+  "D205",
+  "D209",
+  "D212",
+  "D213",
+  "D301",
+  "D400",
+  "D401",
+  "D403",
+  "D413",
+  "D415",
+  "D417",
+]
+
+[tool.ruff.lint.isort]
+lines-between-types = 1
+
+[tool.ruff.lint.per-file-ignores]
+'tests/*.py' = [
+  'F401', # Unused import warning for test files -- this check removes imports of fixtures
+  'F811',  # Redefine while unused -- this check fails on imported fixtures
+  'F841', # Unused variable warning for test files -- common in pymc model declarations
+  'D106'  # Missing docstring for public method -- unittest test subclasses don't need docstrings
+]
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index d3058bf..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-arviz
-emcee
-joblib
-matplotlib
-numba
-numpy
-pandas
-pyparsing
-scipy
-setuptools
-statsmodels
-sympy
-xarray
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index f739d34..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,25 +0,0 @@
-[metadata]
-name = gEconpy
-version = 1.2.1
-description = A package for solving, estimating, and analyzing DSGE models
-long_description = file: README.md
-long_description_content_type = text/markdown
-url = https://github.com/jessegrabowski/gEcon.py
-author = Jesse Grabowski
-author_email = jessegrabowski@gmail.com
-
-[options]
-packages = find:
-install_requires =
-    emcee
-    arviz
-    matplotlib
-    numba
-    numpy
-    pyparsing
-    scipy
-    statsmodels
-    sympy
-
-[options.packages.find]
-exclude = tests*
diff --git a/setup.py b/setup.py
index 6068493..451f259 100644
--- a/setup.py
+++ b/setup.py
@@ -1,3 +1,19 @@
-from setuptools import setup
+import versioneer
 
-setup()
+from setuptools import find_packages, setup
+from setuptools.dist import Distribution
+
+dist = Distribution()
+dist.parse_config_files()
+
+
+NAME: str = dist.get_name()  # type: ignore
+
+
+if __name__ == "__main__":
+    setup(
+        name=NAME,
+        version=versioneer.get_version(),
+        cmdclass=versioneer.get_cmdclass(),
+        packages=find_packages(exclude=["tests*"]),
+    )
diff --git a/sphinxext/generate_gallery.py b/sphinxext/generate_gallery.py
index 6c8b798..a24fa66 100644
--- a/sphinxext/generate_gallery.py
+++ b/sphinxext/generate_gallery.py
@@ -8,17 +8,17 @@
 import json
 import os
 import shutil
+
 from glob import glob
 
 import matplotlib
 
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
+import sphinx
 
 from matplotlib import image
 
-import sphinx
-
 logger = sphinx.util.logging.getLogger(__name__)
 
 DOC_SRC = os.path.dirname(os.path.abspath(__file__))
@@ -112,7 +112,6 @@ def extract_preview_pic(self):
     def gen_previews(self):
         preview = self.extract_preview_pic()
         if preview is not None:
-            print(self.png_path)
             with open(self.png_path, "wb") as buff:
                 buff.write(preview)
         else:
diff --git a/tests/.DS_Store b/tests/.DS_Store
index 45b844a..4e6049a 100644
Binary files a/tests/.DS_Store and b/tests/.DS_Store differ
diff --git a/tests/Test GCNs/basic_rbc.gcn b/tests/Test GCNs/basic_rbc.gcn
new file mode 100644
index 0000000..543190b
--- /dev/null
+++ b/tests/Test GCNs/basic_rbc.gcn	
@@ -0,0 +1,89 @@
+options
+{
+	output logfile = FALSE;
+	output LaTeX = FALSE;
+};
+
+tryreduce
+{
+	U[], TC[];
+};
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = C[] ^ (1 - sigma_C) / (1 - sigma_C) - L[] ^ (1 + sigma_L) / (1 + sigma_L);
+	};
+
+	controls
+	{
+		C[], L[], I[], K[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	constraints
+	{
+		C[] + I[] = r[] * K[-1] + w[] * L[] : lambda[];
+		K[] = (1 - delta) * K[-1] + I[];
+	};
+
+	calibration
+	{
+		beta = 0.99;
+		delta = 0.02;
+		sigma_C = 1.5;
+		sigma_L = 2.0;
+	};
+};
+
+block FIRM
+{
+    controls
+    {
+        K[-1], L[];
+    };
+
+    objective
+    {
+        TC[] = -(r[] * K[-1] + w[] * L[]);
+    };
+
+    constraints
+    {
+        Y[] = A[] * K[-1] ^ alpha * L[] ^ (1 - alpha) : mc[];
+    };
+
+    identities
+    {
+        # Perfect competition
+        mc[] = 1;
+    };
+
+    calibration
+    {
+        alpha = 0.35;
+    };
+};
+
+block TECHNOLOGY_SHOCKS
+{
+    identities
+    {
+        log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+    };
+
+    shocks
+    {
+        epsilon_A[];
+    };
+
+    calibration
+    {
+        rho_A = 0.95;
+    };
+};
diff --git a/tests/Test GCNs/full_nk.gcn b/tests/Test GCNs/full_nk.gcn
new file mode 100644
index 0000000..c4be075
--- /dev/null
+++ b/tests/Test GCNs/full_nk.gcn	
@@ -0,0 +1,313 @@
+options
+{
+    output logfile = TRUE;
+    output LaTeX = TRUE;
+    output LaTeX landscape = TRUE;
+};
+
+assumptions
+{
+	negative
+	{
+		TC[];
+	};
+
+	positive
+	{
+		shock_technology[], shock_preference[], pi[], pi_star[], pi_obj[], r[], r_G[], mc[], w[], w_star[],
+		Y[], C[], I[], K[], L[],
+		delta, beta, sigma_C, sigma_L, gamma_I, phi_H;
+	};
+};
+
+block STEADY_STATE
+{
+    identities
+    {
+        # Steady state values
+        shock_technology[ss] = 1;
+        shock_preference[ss] = 1;
+        pi[ss] = 1;
+        pi_star[ss] = 1;
+        pi_obj[ss] = 1;
+        B[ss] = 0;
+
+        r[ss] = 1 / beta - (1 - delta);
+        r_G[ss] = 1 / beta;
+
+        mc[ss] = 1 / (1 + psi_p);
+        w[ss] = (1 - alpha) * mc[ss] ** (1 / (1 - alpha)) * (alpha / r[ss]) ** (alpha / (1 - alpha));
+
+        w_star[ss] = w[ss];
+
+        Y[ss] = (
+            w[ss] ** ((sigma_L + 1) / (sigma_C + sigma_L))
+            * ((-beta * phi_H + 1) / (psi_w + 1)) ** (1 / (sigma_C + sigma_L))
+            * (r[ss] / ((1 - phi_H) * (-alpha * delta * mc[ss] + r[ss])))
+            ** (sigma_C / (sigma_C + sigma_L))
+            / (mc[ss] * (1 - alpha)) ** (sigma_L / (sigma_C + sigma_L))
+        );
+
+        C[ss] = (
+            w[ss] ** ((1 + sigma_L) / sigma_C)
+            * (1 / (1 - phi_H))
+            * ((1 - beta * phi_H) / (1 + psi_w)) ** (1 / sigma_C)
+            * ((1 - alpha) * mc[ss]) ** (-sigma_L / sigma_C)
+            * Y[ss] ** (-sigma_L / sigma_C)
+        );
+
+        lambda[ss] = (1 - beta * phi_H) * ((1 - phi_H) * C[ss]) ** (-sigma_C);
+        q[ss] = lambda[ss];
+        I[ss] = delta * alpha * mc[ss] * Y[ss] / r[ss];
+        K[ss] = alpha * mc[ss] * Y[ss] / r[ss];
+        L[ss] = (1 - alpha) * Y[ss] * mc[ss] / w[ss];
+
+        U[ss] = (
+            1
+            / (1 - beta)
+            * (
+                ((1 - phi_H) * C[ss]) ** (1 - sigma_C) / (1 - sigma_C)
+                - L[ss] ** (1 + sigma_L) / (1 + sigma_L)
+            )
+        );
+
+        TC[ss] = -(r[ss] * K[ss] + w[ss] * L[ss]);
+        Div[ss] = Y[ss] + TC[ss];
+
+        LHS[ss] = 1 / (1 - beta * eta_p * pi[ss] ** (1 / psi_p)) * lambda[ss] * Y[ss] * pi_star[ss];
+
+        RHS[ss] = 1 / (1 + psi_p) * LHS[ss];
+
+        LHS_w[ss] = 1 / (1 - beta * eta_w) * 1 / (1 + psi_w) * w_star[ss] * lambda[ss] * L[ss];
+
+        RHS_w[ss] = LHS_w[ss];
+    };
+
+};
+
+
+block HOUSEHOLD
+{
+    definitions
+    {
+        u[] = shock_preference[] * (
+				(C[] - phi_H * C[-1]) ^ (1 - sigma_C) / (1 - sigma_C) -
+			    L[] ^ (1 + sigma_L) / (1 + sigma_L));
+    };
+    controls
+    {
+		C[], I[], K[], B[];
+    };
+
+    objective
+    {
+        U[] = u[] + beta * E[][U[1]];
+    };
+
+    constraints
+    {
+        C[] + I[] + B[] / r_G[]  =
+			  r[] * K[-1] +
+			  w[] * L[] +
+			  B[-1] / pi[] +
+			  Div[] : lambda[];
+
+		K[] = (1 - delta) * K[-1] +
+			I[] * (1 - gamma_I / 2 * (I[] / I[-1] - 1) ^ 2) : q[];
+    };
+
+    calibration
+    {
+        delta = 0.025;
+        beta = 0.99;
+
+        sigma_C = 2;
+        sigma_L = 1.5;
+
+		gamma_I = 10;
+		phi_H = 0.5;
+    };
+};
+
+block WAGE_SETTING
+{
+	definitions
+	{
+		L_d_star[] = (w[] / w_star[]) ^ ((1 + psi_w) / psi_w) * L[];
+	};
+
+	identities
+	{
+		LHS_w[] = RHS_w[];
+
+		LHS_w[] =  1 / (1 + psi_w) * w_star[] * lambda[] * L_d_star[] +
+			beta * eta_w * E[][
+				pi[1] * (w_star[1] / w_star[]) ^ (1 / psi_w) * LHS_w[1]
+			];
+
+		RHS_w[] = shock_preference[] * L_d_star[] ^ (1 + sigma_L) +
+			beta * eta_w * E[][
+				(pi[1] * w_star[1] / w_star[]) ^ ((1 + psi_w) * (1 + sigma_L) / psi_w) *
+				RHS_w[1]
+			];
+
+	};
+
+	calibration
+	{
+		psi_w	= 0.782;	# Elasticity of substitution between forms of labor
+		eta_w	= 0.75;		# Probability of not receiving the update signal
+	};
+};
+
+block WAGE_EVOLUTION
+{
+	identities
+	{
+		1 = eta_w * (pi[] * w[] / w[-1]) ^ (1 / psi_w) +
+			(1 - eta_w) * (w[] / w_star[]) ^ (1 / psi_w);
+	};
+};
+
+
+block PREFERENCE_SHOCKS
+{
+	identities
+	{
+		log(shock_preference[]) = rho_preference * log(shock_preference[-1]) + epsilon_preference[];
+	};
+
+	shocks
+	{
+		epsilon_preference[];
+	};
+
+	calibration
+	{
+		rho_preference = 0.95;
+	};
+};
+
+
+block FIRM
+{
+    controls
+    {
+        K[-1], L[];
+    };
+
+    objective
+    {
+        TC[] = -(L[] * w[] + K[-1] * r[]);
+    };
+
+    constraints
+    {
+        Y[] = shock_technology[] * K[-1] ^ alpha *
+					L[] ^ (1 - alpha) :  mc[];
+    };
+
+	identities
+	{
+		Div[] = Y[] + TC[];
+	};
+
+    calibration
+    {
+        alpha  = 0.35;
+    };
+};
+
+
+block TECHNOLOGY_SHOCKS
+{
+    identities
+    {
+        log(shock_technology[]) = rho_technology * log(shock_technology[-1]) + epsilon_Y[];
+    };
+    shocks
+    {
+        epsilon_Y[];
+    };
+    calibration
+    {
+        rho_technology = 0.95;
+    };
+};
+
+
+block FIRM_PRICE_SETTING_PROBLEM
+{
+	identities
+	{
+		LHS[] = (1 + psi_p) * RHS[];
+
+		LHS[] = lambda[] * Y[] * pi_star[] +
+			beta * eta_p * E[][
+				pi_star[] / pi_star[1] * pi[1] ^ (1 / psi_p) * LHS[1]];
+
+		RHS[] = lambda[] * mc[] * Y[] +
+			beta * eta_p * E[][
+				pi[1] ^ ((1 + psi_p) / psi_p) * RHS[1]];
+	};
+
+	calibration
+	{
+		psi_p   = 0.6;
+		eta_p   = 0.75;
+	};
+};
+
+
+block PRICE_EVOLUTION
+{
+	identities
+	{
+		1 = eta_p * pi[] ^ (1 / psi_p) +
+				(1 - eta_p) * pi_star[] ^ (-1 / psi_p);
+	};
+};
+
+
+block MONETARY_POLICY
+{
+	identities
+	{
+		log(r_G[] / r_G[ss]) = gamma_R * log(r_G[-1] / r_G[ss]) +
+			(1 - gamma_R) * log(pi_obj[]) +
+			(1 - gamma_R) * gamma_pi * log(pi[] / pi[ss] - log(pi_obj[])) +
+			(1 - gamma_R) * gamma_Y * log(Y[] / Y[-1]) +
+			epsilon_R[];
+
+		log(pi_obj[]) = (1 - rho_pi_dot) * log(phi_pi_obj) +
+			rho_pi_dot * log(pi_obj[-1]) + epsilon_pi[];
+	};
+
+	shocks
+	{
+		epsilon_R[], epsilon_pi[];
+	};
+
+
+	calibration
+	{
+		gamma_R = 0.9;
+		gamma_pi = 1.5;
+		gamma_Y = 0.05;
+#		pi_obj[ss]	= 1	-> phi_pi_obj;
+#		pi[ss]		= pi_obj[ss]-> phi_pi;
+        phi_pi_obj = 1;
+#        phi_pi = 1;
+		rho_pi_dot	= 0.924;
+	};
+};
+
+
+
+block EQUILIBRIUM
+{
+    identities
+    {
+		B[] = 0;
+    };
+};
diff --git a/tests/Test GCNs/full_nk_linear_phillips_curve.gcn b/tests/Test GCNs/full_nk_linear_phillips_curve.gcn
new file mode 100644
index 0000000..b51ac9c
--- /dev/null
+++ b/tests/Test GCNs/full_nk_linear_phillips_curve.gcn	
@@ -0,0 +1,251 @@
+tryreduce
+{
+    U[], TC[];
+};
+
+assumptions
+{
+	positive
+	{
+		shock_technology[], shock_preference[], pi[], pi_star[], pi_obj[], r[], r_G[], mc[], w[], w_star[],
+		Y[], C[], I[], K[], L[],
+		delta, beta, sigma_C, sigma_L, gamma_I, phi_H;
+	};
+};
+
+block STEADY_STATE
+{
+    identities
+    {
+        # Steady state values
+        shock_technology[ss] = 1;
+        shock_preference[ss] = 1;
+        pi[ss] = 1;
+        pi_w[ss] = 1;
+        pi_obj[ss] = 1;
+        B[ss] = 0;
+
+        r[ss] = 1 / beta - (1 - delta);
+        r_G[ss] = 1 / beta;
+
+        mc[ss] = 1 / (1 + psi_p);
+        w[ss] = (1 - alpha) * mc[ss] ** (1 / (1 - alpha)) * (alpha / r[ss]) ** (alpha / (1 - alpha));
+
+        Y[ss] = (
+            w[ss] ** ((sigma_L + 1) / (sigma_C + sigma_L))
+            * ((-beta * phi_H + 1) / (psi_w + 1)) ** (1 / (sigma_C + sigma_L))
+            * (r[ss] / ((1 - phi_H) * (-alpha * delta * mc[ss] + r[ss])))
+            ** (sigma_C / (sigma_C + sigma_L))
+            / (mc[ss] * (1 - alpha)) ** (sigma_L / (sigma_C + sigma_L))
+        );
+
+        C[ss] = (
+            w[ss] ** ((1 + sigma_L) / sigma_C)
+            * (1 / (1 - phi_H))
+            * ((1 - beta * phi_H) / (1 + psi_w)) ** (1 / sigma_C)
+            * ((1 - alpha) * mc[ss]) ** (-sigma_L / sigma_C)
+            * Y[ss] ** (-sigma_L / sigma_C)
+        );
+
+        lambda[ss] = (1 - beta * phi_H) * ((1 - phi_H) * C[ss]) ** (-sigma_C);
+        q[ss] = lambda[ss];
+        I[ss] = delta * alpha * mc[ss] * Y[ss] / r[ss];
+        K[ss] = alpha * mc[ss] * Y[ss] / r[ss];
+        L[ss] = (1 - alpha) * Y[ss] * mc[ss] / w[ss];
+
+        U[ss] = (
+            1
+            / (1 - beta)
+            * (
+                ((1 - phi_H) * C[ss]) ** (1 - sigma_C) / (1 - sigma_C)
+                - L[ss] ** (1 + sigma_L) / (1 + sigma_L)
+            )
+        );
+
+        TC[ss] = -(r[ss] * K[ss] + w[ss] * L[ss]);
+        Div[ss] = Y[ss] + TC[ss];
+    };
+
+};
+
+
+block HOUSEHOLD
+{
+    definitions
+    {
+        u[] = shock_preference[] * (
+				(C[] - phi_H * C[-1]) ^ (1 - sigma_C) / (1 - sigma_C) -
+			    L[] ^ (1 + sigma_L) / (1 + sigma_L));
+    };
+    controls
+    {
+		C[], I[], K[], B[];
+    };
+
+    objective
+    {
+        U[] = u[] + beta * E[][U[1]];
+    };
+
+    constraints
+    {
+        C[] + I[] + B[] / r_G[]  =
+			  r[] * K[-1] +
+			  w[] * L[] +
+			  B[-1] / pi[] +
+			  Div[] : lambda[];
+
+		K[] = (1 - delta) * K[-1] +
+			I[] * (1 - gamma_I / 2 * (I[] / I[-1] - 1) ^ 2) : q[];
+    };
+
+    calibration
+    {
+        delta   ~ Beta(alpha=2, beta=42) = 0.025;
+        beta    ~ Beta(alpha=70, beta=4) = 0.99;
+
+        sigma_C ~ Gamma(alpha=7, beta=3) = 2;
+        sigma_L ~ Gamma(alpha=7, beta=3) = 1.5;
+
+		gamma_I ~ Gamma(alpha=9, beta=1.4) = 10;
+		phi_H ~ Beta(alpha=5, beta=2) = 0.5;
+    };
+};
+
+block WAGE_SETTING
+{
+	identities
+	{
+	    pi_w[] = w[] / w[-1] * pi[];
+	    log(pi_w[]) = (1 - eta_w) * (1 - eta_w * beta) / (eta_w * (1 + psi_w * sigma_L))
+	        * (sigma_L * log(L[] / L[ss]) - log(w[] / w[ss]) - log(lambda[] / lambda[ss]))
+	        + beta * E[][log(pi_w[1])];
+	};
+
+	calibration
+	{
+		psi_w ~ Exponential(lambda=1) 	= 0.782;	# Markup parameter -> psi_w = 1 / (elasticity - 1)
+		                                            # 0 -> perfect substitutes, oo -> Cobb Douglas
+		eta_w ~ Beta(alpha=4, beta=1)  	= 0.75;		# Probability of not receiving the update signal
+	};
+};
+
+block PREFERENCE_SHOCKS
+{
+	identities
+	{
+		log(shock_preference[]) = rho_preference * log(shock_preference[-1]) + epsilon_preference[];
+	};
+
+	shocks
+	{
+		epsilon_preference[];
+	};
+
+	calibration
+	{
+		rho_preference ~ Beta(alpha=25, beta=3) = 0.95;
+	};
+};
+
+
+block FIRM
+{
+    controls
+    {
+        K[-1], L[];
+    };
+
+    objective
+    {
+        TC[] = -(L[] * w[] + K[-1] * r[]);
+    };
+
+    constraints
+    {
+        Y[] = shock_technology[] * K[-1] ^ alpha *
+					L[] ^ (1 - alpha) :  mc[];
+    };
+
+	identities
+	{
+		Div[] = Y[] + TC[];
+	};
+
+    calibration
+    {
+        alpha ~ Beta(alpha=5.65, beta=7) = 0.35;
+    };
+};
+
+
+block TECHNOLOGY_SHOCKS
+{
+    identities
+    {
+        log(shock_technology[]) = rho_technology * log(shock_technology[-1]) + epsilon_Y[];
+    };
+    shocks
+    {
+        epsilon_Y[];
+    };
+    calibration
+    {
+        rho_technology ~ Beta(alpha=25, beta=3) = 0.95;
+    };
+};
+
+
+block FIRM_PRICE_SETTING_PROBLEM
+{
+	identities
+	{
+        log(pi[]) = (1 - eta_p) * (1 - eta_p * beta) / eta_p * log(mc[] / mc[ss]) + beta * E[][log(pi[1])];
+	};
+
+	calibration
+	{
+		psi_p ~ Exponential(lambda=1)  = 0.6; #Markup parameter: 0 -> perfect substitutes, oo -> Cobb Douglas
+		eta_p ~ Beta(alpha=13, beta=2)  = 0.75;
+	};
+};
+
+block MONETARY_POLICY
+{
+	identities
+	{
+		log(r_G[] / r_G[ss]) = rho_r_G * log(r_G[-1] / r_G[ss]) +
+			(1 - rho_r_G) * log(pi_obj[]) +
+			(1 - rho_r_G) * phi_pi * log(pi[] / pi[ss] - log(pi_obj[])) +
+			(1 - rho_r_G) * phi_Y * log(Y[] / Y[-1]) +
+			epsilon_R[];
+
+		log(pi_obj[]) = (1 - rho_pi_dot) * log(phi_pi_obj) +
+			rho_pi_dot * log(pi_obj[-1]) + epsilon_pi[];
+	};
+
+	shocks
+	{
+		epsilon_R[], epsilon_pi[];f
+	};
+
+
+	calibration
+	{
+		rho_r_G ~ Beta(alpha=25, beta=3)  = 0.9;
+		phi_pi ~ Gamma(alpha=30, beta=20) = 1.5;
+		phi_Y ~ Gamma(alpha=3, beta=30) = 0.05;
+        phi_pi_obj = 1;
+		rho_pi_dot ~ Beta(alpha=25, beta=3)	= 0.95;
+	};
+};
+
+
+
+block EQUILIBRIUM
+{
+    identities
+    {
+		B[] = 0;
+    };
+};
diff --git a/tests/Test GCNs/Full_New_Keyensian.gcn b/tests/Test GCNs/full_nk_no_ss.gcn
similarity index 92%
rename from tests/Test GCNs/Full_New_Keyensian.gcn
rename to tests/Test GCNs/full_nk_no_ss.gcn
index 072f699..0e411d9 100644
--- a/tests/Test GCNs/Full_New_Keyensian.gcn	
+++ b/tests/Test GCNs/full_nk_no_ss.gcn	
@@ -5,11 +5,6 @@ options
     output LaTeX landscape = TRUE;
 };
 
-tryreduce
-{
-	Div[], TC[];
-};
-
 assumptions
 {
 	negative
@@ -19,11 +14,12 @@ assumptions
 
 	positive
 	{
+		shock_technology[], shock_preference[], pi[], pi_star[], pi_obj[], r[], r_G[], mc[], w[], w_star[],
+		Y[], C[], I[], K[], L[],
 		delta, beta, sigma_C, sigma_L, gamma_I, phi_H;
 	};
 };
 
-
 block HOUSEHOLD
 {
     definitions
@@ -78,7 +74,6 @@ block WAGE_SETTING
 	{
 		LHS_w[] = RHS_w[];
 
-		#Equation 23
 		LHS_w[] =  1 / (1 + psi_w) * w_star[] * lambda[] * L_d_star[] +
 			beta * eta_w * E[][
 				pi[1] * (w_star[1] / w_star[]) ^ (1 / psi_w) * LHS_w[1]
@@ -212,7 +207,7 @@ block MONETARY_POLICY
 {
 	identities
 	{
-		log(r_G[] / r_G[ss]) + phi_pi = gamma_R * log(r_G[-1] / r_G[ss]) +
+		log(r_G[] / r_G[ss]) = gamma_R * log(r_G[-1] / r_G[ss]) +
 			(1 - gamma_R) * log(pi_obj[]) +
 			(1 - gamma_R) * gamma_pi * log(pi[] / pi[ss] - log(pi_obj[])) +
 			(1 - gamma_R) * gamma_Y * log(Y[] / Y[-1]) +
@@ -233,8 +228,10 @@ block MONETARY_POLICY
 		gamma_R = 0.9;
 		gamma_pi = 1.5;
 		gamma_Y = 0.05;
-		pi_obj[ss]	= 1	-> phi_pi_obj;
-		pi[ss]		= pi_obj[ss]-> phi_pi;
+#		pi_obj[ss]	= 1	-> phi_pi_obj;
+#		pi[ss]		= pi_obj[ss]-> phi_pi;
+        phi_pi_obj = 1;
+#        phi_pi = 1;
 		rho_pi_dot	= 0.924;
 	};
 };
diff --git a/tests/Test GCNs/full_nk_partial_ss.gcn b/tests/Test GCNs/full_nk_partial_ss.gcn
new file mode 100644
index 0000000..e0815db
--- /dev/null
+++ b/tests/Test GCNs/full_nk_partial_ss.gcn	
@@ -0,0 +1,268 @@
+options
+{
+    output logfile = TRUE;
+    output LaTeX = TRUE;
+    output LaTeX landscape = TRUE;
+};
+
+assumptions
+{
+	negative
+	{
+		TC[];
+	};
+
+	positive
+	{
+		shock_technology[], shock_preference[], pi[], pi_star[], pi_obj[], r[], r_G[], mc[], w[], w_star[],
+		Y[], C[], I[], K[], L[],
+		delta, beta, sigma_C, sigma_L, gamma_I, phi_H;
+	};
+};
+
+block STEADY_STATE
+{
+    identities
+    {
+        # Steady state values
+        shock_technology[ss] = 1;
+        shock_preference[ss] = 1;
+        pi[ss] = 1;
+        pi_star[ss] = 1;
+        pi_obj[ss] = 1;
+        B[ss] = 0;
+
+        r[ss] = 1 / beta - (1 - delta);
+        r_G[ss] = 1 / beta;
+
+        mc[ss] = 1 / (1 + psi_p);
+    };
+
+};
+
+
+block HOUSEHOLD
+{
+    definitions
+    {
+        u[] = shock_preference[] * (
+				(C[] - phi_H * C[-1]) ^ (1 - sigma_C) / (1 - sigma_C) -
+			    L[] ^ (1 + sigma_L) / (1 + sigma_L));
+    };
+    controls
+    {
+		C[], I[], K[], B[];
+    };
+
+    objective
+    {
+        U[] = u[] + beta * E[][U[1]];
+    };
+
+    constraints
+    {
+        C[] + I[] + B[] / r_G[]  =
+			  r[] * K[-1] +
+			  w[] * L[] +
+			  B[-1] / pi[] +
+			  Div[] : lambda[];
+
+		K[] = (1 - delta) * K[-1] +
+			I[] * (1 - gamma_I / 2 * (I[] / I[-1] - 1) ^ 2) : q[];
+    };
+
+    calibration
+    {
+        delta = 0.025;
+        beta = 0.99;
+
+        sigma_C = 2;
+        sigma_L = 1.5;
+
+		gamma_I = 10;
+		phi_H = 0.5;
+    };
+};
+
+block WAGE_SETTING
+{
+	definitions
+	{
+		L_d_star[] = (w[] / w_star[]) ^ ((1 + psi_w) / psi_w) * L[];
+	};
+
+	identities
+	{
+		LHS_w[] = RHS_w[];
+
+		LHS_w[] =  1 / (1 + psi_w) * w_star[] * lambda[] * L_d_star[] +
+			beta * eta_w * E[][
+				pi[1] * (w_star[1] / w_star[]) ^ (1 / psi_w) * LHS_w[1]
+			];
+
+		RHS_w[] = shock_preference[] * L_d_star[] ^ (1 + sigma_L) +
+			beta * eta_w * E[][
+				(pi[1] * w_star[1] / w_star[]) ^ ((1 + psi_w) * (1 + sigma_L) / psi_w) *
+				RHS_w[1]
+			];
+
+	};
+
+	calibration
+	{
+		psi_w	= 0.782;	# Elasticity of substitution between forms of labor
+		eta_w	= 0.75;		# Probability of not receiving the update signal
+	};
+};
+
+block WAGE_EVOLUTION
+{
+	identities
+	{
+		1 = eta_w * (pi[] * w[] / w[-1]) ^ (1 / psi_w) +
+			(1 - eta_w) * (w[] / w_star[]) ^ (1 / psi_w);
+	};
+};
+
+
+block PREFERENCE_SHOCKS
+{
+	identities
+	{
+		log(shock_preference[]) = rho_preference * log(shock_preference[-1]) + epsilon_preference[];
+	};
+
+	shocks
+	{
+		epsilon_preference[];
+	};
+
+	calibration
+	{
+		rho_preference = 0.95;
+	};
+};
+
+
+block FIRM
+{
+    controls
+    {
+        K[-1], L[];
+    };
+
+    objective
+    {
+        TC[] = -(L[] * w[] + K[-1] * r[]);
+    };
+
+    constraints
+    {
+        Y[] = shock_technology[] * K[-1] ^ alpha *
+					L[] ^ (1 - alpha) :  mc[];
+    };
+
+	identities
+	{
+		Div[] = Y[] + TC[];
+	};
+
+    calibration
+    {
+        alpha  = 0.35;
+    };
+};
+
+
+block TECHNOLOGY_SHOCKS
+{
+    identities
+    {
+        log(shock_technology[]) = rho_technology * log(shock_technology[-1]) + epsilon_Y[];
+    };
+    shocks
+    {
+        epsilon_Y[];
+    };
+    calibration
+    {
+        rho_technology = 0.95;
+    };
+};
+
+
+block FIRM_PRICE_SETTING_PROBLEM
+{
+	identities
+	{
+		LHS[] = (1 + psi_p) * RHS[];
+
+		LHS[] = lambda[] * Y[] * pi_star[] +
+			beta * eta_p * E[][
+				pi_star[] / pi_star[1] * pi[1] ^ (1 / psi_p) * LHS[1]];
+
+		RHS[] = lambda[] * mc[] * Y[] +
+			beta * eta_p * E[][
+				pi[1] ^ ((1 + psi_p) / psi_p) * RHS[1]];
+	};
+
+	calibration
+	{
+		psi_p   = 0.6;
+		eta_p   = 0.75;
+	};
+};
+
+
+block PRICE_EVOLUTION
+{
+	identities
+	{
+		1 = eta_p * pi[] ^ (1 / psi_p) +
+				(1 - eta_p) * pi_star[] ^ (-1 / psi_p);
+	};
+};
+
+
+block MONETARY_POLICY
+{
+	identities
+	{
+		log(r_G[] / r_G[ss]) = gamma_R * log(r_G[-1] / r_G[ss]) +
+			(1 - gamma_R) * log(pi_obj[]) +
+			(1 - gamma_R) * gamma_pi * log(pi[] / pi[ss] - log(pi_obj[])) +
+			(1 - gamma_R) * gamma_Y * log(Y[] / Y[-1]) +
+			epsilon_R[];
+
+		log(pi_obj[]) = (1 - rho_pi_dot) * log(phi_pi_obj) +
+			rho_pi_dot * log(pi_obj[-1]) + epsilon_pi[];
+	};
+
+	shocks
+	{
+		epsilon_R[], epsilon_pi[];
+	};
+
+
+	calibration
+	{
+		gamma_R = 0.9;
+		gamma_pi = 1.5;
+		gamma_Y = 0.05;
+#		pi_obj[ss]	= 1	-> phi_pi_obj;
+#		pi[ss]		= pi_obj[ss]-> phi_pi;
+        phi_pi_obj = 1;
+#        phi_pi = 1;
+		rho_pi_dot	= 0.924;
+	};
+};
+
+
+
+block EQUILIBRIUM
+{
+    identities
+    {
+		B[] = 0;
+    };
+};
diff --git a/tests/Test GCNs/one_block_1.gcn b/tests/Test GCNs/one_block_1.gcn
new file mode 100644
index 0000000..fa6d078
--- /dev/null
+++ b/tests/Test GCNs/one_block_1.gcn	
@@ -0,0 +1,48 @@
+options
+{
+
+};
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = (C[] ^ (1 - gamma) - 1) / (1 - gamma);
+	};
+
+	controls
+	{
+		C[], K[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	constraints
+	{
+		C[] + K[] - (1 - delta) * K[-1] = A[] * K[-1] ^ alpha : lambda[];
+	};
+
+	identities
+	{
+		log(A[]) = rho * log(A[-1]) + epsilon[];
+	};
+
+	shocks
+	{
+		epsilon[];
+	};
+
+	calibration
+	{
+		alpha = 0.4;
+		beta = 0.99;
+		delta = 0.02;
+		rho = 0.95;
+		gamma = 1.5;
+	};
+
+
+};
diff --git a/tests/Test GCNs/One_Block_Simple_1_w_Distributions.gcn b/tests/Test GCNs/one_block_1_dist.gcn
similarity index 100%
rename from tests/Test GCNs/One_Block_Simple_1_w_Distributions.gcn
rename to tests/Test GCNs/one_block_1_dist.gcn
diff --git a/tests/Test GCNs/One_Block_Simple_1.gcn b/tests/Test GCNs/one_block_1_duplicate_params.gcn
similarity index 97%
rename from tests/Test GCNs/One_Block_Simple_1.gcn
rename to tests/Test GCNs/one_block_1_duplicate_params.gcn
index b5f8686..d84af3e 100644
--- a/tests/Test GCNs/One_Block_Simple_1.gcn	
+++ b/tests/Test GCNs/one_block_1_duplicate_params.gcn	
@@ -50,6 +50,7 @@ block HOUSEHOLD
 		delta = 0.02;
 		rho = 0.95;
 		gamma = 1.5;
+		gamma = 2.0;
 	};
 
 
diff --git a/tests/Test GCNs/one_block_1_duplicate_params_2.gcn b/tests/Test GCNs/one_block_1_duplicate_params_2.gcn
new file mode 100644
index 0000000..8da6869
--- /dev/null
+++ b/tests/Test GCNs/one_block_1_duplicate_params_2.gcn	
@@ -0,0 +1,71 @@
+options
+{
+
+};
+
+block STEADY_STATE
+{
+    identities
+    {
+        A[ss] = 1;
+    };
+};
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = (C[] ^ (1 - gamma) - 1) / (1 - gamma);
+	};
+
+	controls
+	{
+		C[], K[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	constraints
+	{
+		C[] + K[] - (1 - delta) * K[-1] = A[] * K[-1] ^ alpha : lambda[];
+	};
+
+	identities
+	{
+		log(A[]) = rho * log(A[-1]) + epsilon[];
+	};
+
+	shocks
+	{
+		epsilon[];
+	};
+
+	calibration
+	{
+		alpha = 0.4;
+		beta = 0.99;
+		delta = 0.02;
+		rho = 0.95;
+		gamma = 1.5;
+	};
+};
+
+block DUPLICATE_PARAMETER
+{
+    calibration
+    {
+        gamma = 3;
+        beta = 2;
+    };
+};
+
+block MORE_DUPLICATE_PARAMETERS
+{
+    calibration
+    {
+        alpha = 0.333;
+    };
+};
diff --git a/tests/Test GCNs/One_Block_Simple_1_w_Steady_State.gcn b/tests/Test GCNs/one_block_1_ss.gcn
similarity index 85%
rename from tests/Test GCNs/One_Block_Simple_1_w_Steady_State.gcn
rename to tests/Test GCNs/one_block_1_ss.gcn
index ab3f490..353ffa3 100644
--- a/tests/Test GCNs/One_Block_Simple_1_w_Steady_State.gcn	
+++ b/tests/Test GCNs/one_block_1_ss.gcn	
@@ -71,12 +71,12 @@ block HOUSEHOLD
 	calibration
 	{
 		# L[ss] / K[ss] = 0.36 -> alpha;
-		alpha = 0.35;
-		theta = 0.357;
-		beta = 0.99;
-		delta = 0.02;
-		tau = 2;
-		rho = 0.95;
+		alpha ~ Beta(alpha=1, beta=1) = 0.35;
+		theta ~ Beta(alpha=1, beta=1) = 0.357;
+		beta  ~ Beta(alpha=1, beta=1) = 0.99;
+		delta ~ Beta(alpha=1, beta=1) = 0.02;
+		tau   ~ Gamma(alpha=2, beta=1) = 2;
+		rho   ~ Beta(alpha=1, beta=1) = 0.95;
 	};
 
 
diff --git a/tests/Test GCNs/one_block_1_ss_2shock.gcn b/tests/Test GCNs/one_block_1_ss_2shock.gcn
new file mode 100644
index 0000000..1b42925
--- /dev/null
+++ b/tests/Test GCNs/one_block_1_ss_2shock.gcn	
@@ -0,0 +1,72 @@
+block STEADY_STATE
+{
+    definitions
+    {
+        L_num = theta * (1 - alpha) * (1 - beta * (1 - delta));
+        L_denom = 1 - alpha * theta - beta * (1 - delta * (1 - alpha) - alpha * theta);
+    };
+
+    identities
+    {
+        L[ss] =  L_num / L_denom;
+        K[ss] = (((1 - alpha * theta) * L[ss] - theta * (1 - alpha)) / (delta * (1 - theta) * L[ss])) ^
+            (1 / (1 - alpha)) * L[ss];
+        A[ss] = 1;
+        B[ss] = 1;
+
+        Y[ss] = K[ss] ^ alpha * L[ss] ^ (1 - alpha);
+        I[ss] = delta * K[ss];
+        C[ss] = Y[ss] - I[ss];
+
+        lambda[ss] = theta * (C[ss] ^ theta * (1 - L[ss]) ^ (1 - theta)) ^ (1 - tau) / C[ss];
+        q[ss] = lambda[ss];
+        U[ss] = (C[ss] ^ theta * (1 - L[ss]) ^ (1 - theta)) ^ (1 - tau) / ((1 - beta) * (1 - tau));
+    };
+};
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = B[] * (C[] ^ theta * (1 - L[]) ^ (1 - theta)) ^ (1 - tau) / (1 - tau);
+	};
+
+	controls
+	{
+		C[], L[], I[], K[], Y[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	constraints
+	{
+		Y[] = A[] * K[-1] ^ alpha * L[] ^ (1 - alpha);
+		C[] + I[] = Y[] : lambda[];
+		K[] = I[] + (1 - delta) * K[-1] : q[];
+	};
+
+	identities
+	{
+		log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+		log(B[]) = rho_B * log(B[-1]) + epsilon_B[];
+	};
+
+	shocks
+	{
+		epsilon_A[], epsilon_B[];
+	};
+
+	calibration
+	{
+		alpha = 0.35;
+		theta = 0.357;
+		beta = 0.99;
+		delta = 0.02;
+		tau = 2;
+		rho_A = 0.95;
+		rho_B = 0.95;
+	};
+};
diff --git a/tests/Test GCNs/One_Block_Simple_1_ss_Error.gcn b/tests/Test GCNs/one_block_1_ss_error.gcn
similarity index 100%
rename from tests/Test GCNs/One_Block_Simple_1_ss_Error.gcn
rename to tests/Test GCNs/one_block_1_ss_error.gcn
diff --git a/tests/Test GCNs/one_block_2.gcn b/tests/Test GCNs/one_block_2.gcn
new file mode 100644
index 0000000..0928584
--- /dev/null
+++ b/tests/Test GCNs/one_block_2.gcn	
@@ -0,0 +1,69 @@
+options
+{
+	output logfile = FALSE;
+	output LaTeX = FALSE;
+};
+
+tryreduce
+{
+    C[];
+};
+
+assumptions
+{
+    positive
+    {
+        Y[], C[], I[], K[], L[], A[], theta, beta, delta, tau, rho, alpha;
+    };
+};
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = (C[] ^ theta * (1 - L[]) ^ (1 - theta)) ^ (1 - tau) / (1 - tau);
+	};
+
+	controls
+	{
+		C[], L[], I[], K[], Y[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	constraints
+	{
+		Y[] = A[] * K[] ^ alpha * L[] ^ (1 - alpha) + Theta + zeta;
+		I[] = Y[] - C[] : lambda[];
+		K[] = I[] + (1 - delta) * K[-1] : q[];
+	};
+
+	identities
+	{
+		log(A[]) = rho * log(A[-1]) + epsilon[];
+	};
+
+	shocks
+	{
+		epsilon[];
+	};
+
+	calibration
+	{
+		L[ss] / K[ss] = 0.36 -> alpha;
+		theta = 0.357;
+		beta = 1 / 1.01;
+		delta = 0.02;
+		tau = 2;
+
+		rho = 0.95;
+
+		Theta = rho * beta + 3;
+		zeta = -log(theta);
+	};
+
+
+};
diff --git a/tests/Test GCNs/One_Block_Simple_2.gcn b/tests/Test GCNs/one_block_2_no_extra.gcn
similarity index 92%
rename from tests/Test GCNs/One_Block_Simple_2.gcn
rename to tests/Test GCNs/one_block_2_no_extra.gcn
index 08bb138..32d37e7 100644
--- a/tests/Test GCNs/One_Block_Simple_2.gcn	
+++ b/tests/Test GCNs/one_block_2_no_extra.gcn	
@@ -55,15 +55,10 @@ block HOUSEHOLD
 	{
 		L[ss] / K[ss] = 0.36 -> alpha;
 		theta = 0.357;
-		beta = 0.99;
+		beta = 1 / 1.01;
 		delta = 0.02;
 		tau = 2;
 
 		rho = 0.95;
-
-		Theta = rho * beta + 3;
-		zeta = -log(theta);
 	};
-
-
 };
diff --git a/tests/Test GCNs/open_rbc.gcn b/tests/Test GCNs/open_rbc.gcn
new file mode 100644
index 0000000..dc6078f
--- /dev/null
+++ b/tests/Test GCNs/open_rbc.gcn	
@@ -0,0 +1,111 @@
+assumptions
+{
+    positive
+    {
+        A[], r[], N[], K[], Y[], I[], C[];
+    };
+};
+
+block STEADY_STATE
+{
+    identities
+    {
+        A[ss] = 1;
+		IIP[ss] = IIPbar;
+        r[ss] = rstar;
+		r_given[ss] = r[ss];
+        KtoN[ss] = (alpha/(r[ss]+delta))^(1/(1-alpha));
+        N[ss] = ((1-alpha)*(KtoN[ss])^alpha)^(1/(omega-1));
+        K[ss] = KtoN[ss]*N[ss];
+        Y[ss] = A[ss] * K[ss] ^ alpha * N[ss] ^ (1 - alpha);
+        I[ss] = delta * K[ss];
+		C[ss] = r[ss]*IIP[ss]+Y[ss]-I[ss];
+        u[ss] = 1/(1-gamma)*((C[ss]-1/omega*N[ss]^omega)^(1-gamma)-1);
+        U[ss] = 1 / (1 - beta) * u[ss];
+        Cadjcost[ss] = 0;
+        TB[ss] = Y[ss] - C[ss] - I[ss] - Cadjcost[ss];
+        TBtoY[ss] = TB[ss] / Y[ss];
+		CA[ss] = TB[ss] + r[ss]*IIP[ss];
+		lambda[ss] = (C[ss] - N[ss] ^ omega / omega) ^ (-gamma);
+    };
+};
+
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = 1/(1-gamma)*((C[] - 1 / omega * N[] ^ omega) ^ (1 - gamma) - 1);
+		I[] = K[] - (1 - delta) * K[-1];
+		Cadjcost[] = psi/2*(K[] - K[-1])^2;
+		Y[] = A[] * K[-1] ^ alpha * N[] ^ (1 - alpha);
+	};
+
+	controls
+	{
+		C[], N[], K[], IIP[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	constraints
+	{
+		C[] + I[] + Cadjcost[] + IIP[] = Y[] + (1+r_given[-1])*IIP[-1] : lambda[];
+	};
+
+	identities
+	{
+		TB[] = Y[] - C[] - I[] - Cadjcost[];
+		KtoN[] = K[] / N[];
+		TBtoY[] = TB[] / Y[];
+		CA[] = TB[] + r[-1]*IIP[-1];
+		r[] = rstar + psi2*(exp(IIPbar-IIP[])-1);
+		r_given[] = r[];
+	};
+
+	calibration
+	{
+		beta = 0.990099;
+		delta = 0.025;
+        gamma_rv ~ HalfNormal(sigma=5) = 1;
+        omega_rv ~ HalfNormal(sigma=5) = 0.455;
+        gamma = 1 + gamma_rv;
+        omega = 1 + omega_rv;
+        psi2 = 0.000742;
+        psi = 0.028;
+        alpha ~ Beta(alpha=5, beta=5) = 0.32;
+        rstar = 1 / beta - 1;
+		IIPbar = 0;
+	};
+};
+
+
+block TECHNOLOGY_SHOCKS
+{
+    identities
+    {
+        log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+    };
+
+	calibration
+	{
+		rho_A ~ Beta(alpha=3, beta=1) = 0.42;
+	};
+
+    shocks
+    {
+        epsilon_A[];
+    };
+};
+
+block EQULIBRIUM
+{
+	identities
+	{
+		I[] = K[] - (1 - delta) * K[-1];
+		Y[] = A[] * K[-1] ^ alpha * N[] ^ (1 - alpha);
+	};
+};
diff --git a/tests/Test GCNs/open_rbc_extra_params.gcn b/tests/Test GCNs/open_rbc_extra_params.gcn
new file mode 100644
index 0000000..aeac850
--- /dev/null
+++ b/tests/Test GCNs/open_rbc_extra_params.gcn	
@@ -0,0 +1,103 @@
+block STEADY_STATE
+{
+    identities
+    {
+        A[ss] = 1;
+		IIP[ss] = IIPbar;
+        r[ss] = rstar;
+		r_given[ss] = r[ss];
+        KtoN[ss] = (alpha/(r[ss]+delta))^(1/(1-alpha));
+        N[ss] = ((1-alpha)*(KtoN[ss])^alpha)^(1/(omega-1));
+        K[ss] = KtoN[ss]*N[ss];
+        Y[ss] = A[ss] * K[ss] ^ alpha * N[ss] ^ (1 - alpha);
+        I[ss] = delta * K[ss];
+		C[ss] = r[ss]*IIP[ss]+Y[ss]-I[ss];
+        u[ss] = 1/(1-gamma)*((C[ss]-1/omega*N[ss]^omega)^(1-gamma)-1);
+        U[ss] = 1 / (1 - beta) * u[ss];
+        Cadjcost[ss] = 0;
+        TB[ss] = Y[ss] - C[ss] - I[ss] - Cadjcost[ss];
+        TBtoY[ss] = TB[ss] / Y[ss];
+		CA[ss] = TB[ss] + r[ss]*IIP[ss];
+		lambda[ss] = (C[ss] - N[ss] ^ omega / omega) ^ (-gamma);
+    };
+};
+
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = 1/(1-gamma)*((C[] - 1 / omega * N[] ^ omega) ^ (1 - gamma) - 1);
+		I[] = K[] - (1 - delta) * K[-1];
+		Cadjcost[] = psi/2*(K[] - K[-1])^2;
+		Y[] = A[] * K[-1] ^ alpha * N[] ^ (1 - alpha);
+	};
+
+	controls
+	{
+		C[], N[], K[], IIP[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	constraints
+	{
+		C[] + I[] + Cadjcost[] + IIP[] = Y[] + (1+r_given[-1])*IIP[-1] : lambda[];
+	};
+
+	identities
+	{
+		TB[] = Y[] - C[] - I[] - Cadjcost[];
+		KtoN[] = K[] / N[];
+		TBtoY[] = TB[] / Y[];
+		CA[] = TB[] + r[-1]*IIP[-1];
+		r[] = rstar + psi2*(exp(IIPbar-IIP[])-1);
+		r_given[] = r[];
+	};
+
+	calibration
+	{
+		beta = 0.990099;
+		delta = 0.025;
+        gamma = 2;
+        omega = 1.455;
+        psi2 = 0.000742;
+        psi = 0.028;
+        alpha = 0.32;
+        rstar = 1 - 1 / beta;
+		IIPbar = 0;
+		extra_param = 123;
+	};
+};
+
+
+block TECHNOLOGY_SHOCKS
+{
+    identities
+    {
+        log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+    };
+
+	calibration
+	{
+		rho_A = 0.42;
+		sigma_epsilon_A = 0.01;
+	};
+
+    shocks
+    {
+        epsilon_A[];
+    };
+};
+
+block EQULIBRIUM
+{
+	identities
+	{
+		I[] = K[] - (1 - delta) * K[-1];
+		Y[] = A[] * K[-1] ^ alpha * N[] ^ (1 - alpha);
+	};
+};
diff --git a/tests/Test GCNs/open_rbc_orphan_params.gcn b/tests/Test GCNs/open_rbc_orphan_params.gcn
new file mode 100644
index 0000000..e5cb46f
--- /dev/null
+++ b/tests/Test GCNs/open_rbc_orphan_params.gcn	
@@ -0,0 +1,102 @@
+block STEADY_STATE
+{
+    identities
+    {
+        A[ss] = 1;
+		IIP[ss] = IIPbar;
+        r[ss] = rstar;
+		r_given[ss] = r[ss];
+        KtoN[ss] = (alpha/(r[ss]+delta))^(1/(1-alpha));
+        N[ss] = ((1-alpha)*(KtoN[ss])^alpha)^(1/(omega-1));
+        K[ss] = KtoN[ss]*N[ss];
+        Y[ss] = A[ss] * K[ss] ^ alpha * N[ss] ^ (1 - alpha);
+        I[ss] = delta * K[ss];
+		C[ss] = r[ss]*IIP[ss]+Y[ss]-I[ss];
+        u[ss] = 1/(1-gamma)*((C[ss]-1/omega*N[ss]^omega)^(1-gamma)-1);
+        U[ss] = 1 / (1 - beta) * u[ss];
+        Cadjcost[ss] = 0;
+        TB[ss] = Y[ss] - C[ss] - I[ss] - Cadjcost[ss];
+        TBtoY[ss] = TB[ss] / Y[ss];
+		CA[ss] = TB[ss] + r[ss]*IIP[ss];
+		lambda[ss] = (C[ss] - N[ss] ^ omega / omega) ^ (-gamma);
+    };
+};
+
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = 1/(1-gamma)*((C[] - 1 / omega * N[] ^ omega) ^ (1 - gamma) - 1);
+		I[] = K[] - (1 - delta) * K[-1];
+		Cadjcost[] = psi/2*(K[] - K[-1])^2;
+		Y[] = A[] * K[-1] ^ alpha * N[] ^ (1 - alpha);
+	};
+
+	controls
+	{
+		C[], N[], K[], IIP[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	constraints
+	{
+		C[] + I[] + Cadjcost[] + IIP[] = Y[] + (1+r_given[-1])*IIP[-1] : lambda[];
+	};
+
+	identities
+	{
+		TB[] = Y[] - C[] - I[] - Cadjcost[];
+		KtoN[] = K[] / N[];
+		TBtoY[] = TB[] / Y[] + orphan;
+		CA[] = TB[] + r[-1]*IIP[-1];
+		r[] = rstar + psi2*(exp(IIPbar-IIP[])-1);
+		r_given[] = r[];
+	};
+
+	calibration
+	{
+		beta = 0.990099;
+		delta = 0.025;
+        gamma = 2;
+        omega = 1.455;
+        psi2 = 0.000742;
+        psi = 0.028;
+        alpha = 0.32;
+        rstar = 1 - 1 / beta;
+		IIPbar = 0;
+	};
+};
+
+
+block TECHNOLOGY_SHOCKS
+{
+    identities
+    {
+        log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+    };
+
+	calibration
+	{
+		rho_A = 0.42;
+		sigma_epsilon_A = 0.01;
+	};
+
+    shocks
+    {
+        epsilon_A[];
+    };
+};
+
+block EQULIBRIUM
+{
+	identities
+	{
+		I[] = K[] - (1 - delta) * K[-1];
+		Y[] = A[] * K[-1] ^ alpha * N[] ^ (1 - alpha);
+	};
+};
diff --git a/tests/Test GCNs/pert_fails.gcn b/tests/Test GCNs/pert_fails.gcn
index 2e28d1a..4c14d99 100644
--- a/tests/Test GCNs/pert_fails.gcn	
+++ b/tests/Test GCNs/pert_fails.gcn	
@@ -1,3 +1,19 @@
+block STEADY_STATE
+{
+    identities
+    {
+		A[ss] = 1;
+		R[ss] = (1 / beta - (1 - delta));
+		W[ss] = (1 - alpha) ^ (1 / (1 - alpha)) * (alpha / R[ss]) ^ (alpha / (1 - alpha));
+		Y[ss] = (R[ss] / (R[ss] - delta * alpha)) ^ (sigma / (sigma + phi)) *
+			   ((1 - alpha) ^ (-phi) * (W[ss]) ^ (1 + phi)) ^ (1 / (sigma + phi));
+		K[ss] = alpha * Y[ss] / R[ss];
+		I[ss] = delta * K[ss];
+		C[ss] = Y[ss] - I[ss];
+		L[ss] = (1 - alpha) * Y[ss] / W[ss];
+    };
+};
+
 block SYSTEM_EQUATIONS
 {
 	identities
@@ -6,7 +22,7 @@ block SYSTEM_EQUATIONS
 		W[] = sigma * C[] + phi * L[];
 
 		#2. Euler Equation
-		sigma / beta * (E[][C[1]] - C[]) = R_ss * E[][R[1]];
+		sigma / beta * (E[][C[1]] - C[]) = R[ss] * E[][R[1]];
 
 		#3. Law of motion of capital -- Timings have been changed to cause Gensys to fail
 		K[] = (1 - delta) * K[] + delta * I[];
@@ -21,7 +37,7 @@ block SYSTEM_EQUATIONS
 		W[] = Y[] - L[];
 
 		#7. Equlibrium Condition
-		Y_ss * Y[] = C_ss * C[] + I_ss * I[];
+		Y[ss] * Y[] = C[ss] * C[] + I[ss] * I[];
 
 		#8. Productivity Shock
 		A[] = rho_A * A[-1] + epsilon_A[];
@@ -41,16 +57,6 @@ block SYSTEM_EQUATIONS
 		beta = 0.985;
 		delta = 0.025;
 		rho_A = 0.95;
-
-		#P_ss = 1;
-		R_ss = (1 / beta - (1 - delta));
-		W_ss = (1 - alpha) ^ (1 / (1 - alpha)) * (alpha / R_ss) ^ (alpha / (1 - alpha));
-		Y_ss = (R_ss / (R_ss - delta * alpha)) ^ (sigma / (sigma + phi)) *
-			   ((1 - alpha) ^ (-phi) * (W_ss) ^ (1 + phi)) ^ (1 / (sigma + phi));
-		K_ss = alpha * Y_ss / R_ss;
-		I_ss = delta * K_ss;
-		C_ss = Y_ss - I_ss;
-		L_ss = (1 - alpha) * Y_ss / W_ss;
 	};
 
 };
diff --git a/tests/Test GCNs/Two_Block_RBC_1.gcn b/tests/Test GCNs/rbc_2_block.gcn
similarity index 100%
rename from tests/Test GCNs/Two_Block_RBC_1.gcn
rename to tests/Test GCNs/rbc_2_block.gcn
diff --git a/tests/Test GCNs/rbc_2_block_partial_ss.gcn b/tests/Test GCNs/rbc_2_block_partial_ss.gcn
new file mode 100644
index 0000000..21b1fa2
--- /dev/null
+++ b/tests/Test GCNs/rbc_2_block_partial_ss.gcn	
@@ -0,0 +1,74 @@
+block STEADY_STATE
+{
+    identities
+    {
+        A[ss] = 1;
+        r[ss] = 1 / beta - (1 - delta);
+    };
+};
+
+block HOUSEHOLD
+{
+    definitions
+    {
+		u[] = C[] ^ (1 - sigma_C) / (1 - sigma_C) -
+			  L[] ^ (1 + sigma_L) / (1 + sigma_L);
+    };
+    controls
+    {
+		K[], C[], L[], I[];
+    };
+    objective
+    {
+		U[] = u[] + beta * E[][U[1]];
+    };
+    constraints
+    {
+		C[] + I[] = w[] * L[] + r[] * K[-1] : lambda[];
+		K[] = (1 - delta) * K[-1] + I[] : q[];
+    };
+
+    calibration
+    {
+		beta = 0.985;
+		delta = 0.025;
+		sigma_C = 2;
+		sigma_L = 1.5;
+    };
+};
+
+
+block FIRM
+{
+	controls
+	{
+		K[-1], L[];
+	};
+
+    objective
+    {
+		TC[] = -(w[] * L[] + r[] * K[-1]);
+    };
+
+    constraints
+    {
+		Y[] = A[] * K[-1] ^ alpha * L[] ^ (1 - alpha) : P[];
+    };
+
+	identities
+	{
+		log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+		P[] = 1;
+	};
+
+	shocks
+	{
+		epsilon_A[];
+	};
+
+    calibration
+    {
+		alpha = 0.35;
+		rho_A = 0.95;
+    };
+};
diff --git a/tests/Test GCNs/rbc_2_block_ss.gcn b/tests/Test GCNs/rbc_2_block_ss.gcn
new file mode 100644
index 0000000..600cfee
--- /dev/null
+++ b/tests/Test GCNs/rbc_2_block_ss.gcn	
@@ -0,0 +1,85 @@
+block STEADY_STATE
+{
+    identities
+    {
+        A[ss] = 1;
+        r[ss] = 1 / beta - (1 - delta);
+        w[ss] = (1 - alpha) * (alpha / r[ss]) ^ (alpha / (1 - alpha));
+        Y[ss] = (r[ss] / (r[ss] - delta * alpha)) ^ (sigma_C / (sigma_C + sigma_L)) *
+                (w[ss] * (w[ss] / (1 - alpha)) ^ sigma_L) ^ (1 / (sigma_C + sigma_L));
+        I[ss] = delta * alpha / r[ss] * Y[ss];
+        C[ss] = ((1 - alpha) ^ (-sigma_L) * w[ss] ^ (1 + sigma_L)) ^ (1 / sigma_C) * Y[ss] ^ (-sigma_L / sigma_C);
+        K[ss] = alpha * Y[ss] / r[ss];
+        L[ss] = (1 - alpha) * Y[ss] / w[ss];
+        U[ss] = 1 / (1 - beta) * (C[ss] ^ (1 - sigma_C) / (1 - sigma_C) - L[ss] ^ (1 + sigma_L) / (1 + sigma_L));
+        lambda[ss] = C[ss] ^ (-sigma_C);
+        q[ss] = lambda[ss];
+        TC[ss] = -(w[ss] * L[ss] + r[ss] * K[ss]);
+    };
+};
+
+block HOUSEHOLD
+{
+    definitions
+    {
+		u[] = C[] ^ (1 - sigma_C) / (1 - sigma_C) -
+			  L[] ^ (1 + sigma_L) / (1 + sigma_L);
+    };
+    controls
+    {
+		K[], C[], L[], I[];
+    };
+    objective
+    {
+		U[] = u[] + beta * E[][U[1]];
+    };
+    constraints
+    {
+		C[] + I[] = w[] * L[] + r[] * K[-1] : lambda[];
+		K[] = (1 - delta) * K[-1] + I[] : q[];
+    };
+
+    calibration
+    {
+		beta = 0.985;
+		delta = 0.025;
+		sigma_C = 2;
+		sigma_L = 1.5;
+    };
+};
+
+
+block FIRM
+{
+	controls
+	{
+		K[-1], L[];
+	};
+
+    objective
+    {
+		TC[] = -(w[] * L[] + r[] * K[-1]);
+    };
+
+    constraints
+    {
+		Y[] = A[] * K[-1] ^ alpha * L[] ^ (1 - alpha) : P[];
+    };
+
+	identities
+	{
+		log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+		P[] = 1;
+	};
+
+	shocks
+	{
+		epsilon_A[];
+	};
+
+    calibration
+    {
+		alpha = 0.35;
+		rho_A = 0.95;
+    };
+};
diff --git a/tests/Test GCNs/rbc_firm_capital.gcn b/tests/Test GCNs/rbc_firm_capital.gcn
new file mode 100644
index 0000000..6724176
--- /dev/null
+++ b/tests/Test GCNs/rbc_firm_capital.gcn	
@@ -0,0 +1,111 @@
+tryreduce
+{
+	Pi[], U[];
+};
+
+block STEADYSTATE
+{
+
+    definitions
+    {
+		# Capital/Labor Ratio
+		N[ss] = (alpha * beta * A[ss] / (1 - beta * (1 - delta)))
+					^ (1 / (1 - alpha));
+    };
+
+    identities
+    {
+        A[ss] = 1.0;
+        Pi[ss] = 0.0;
+        L[ss] = (1 - alpha) / Theta / (1 - delta * N[ss] ^ (1 - alpha));
+		K[ss] = N[ss] * L[ss];
+
+		w[ss] = (1 - alpha) * N[ss] ^ alpha;
+
+		Y[ss] = A[ss] * K[ss] ^ alpha * L[ss] ^ (1 - alpha);
+		I[ss] = delta * K[ss];
+		C[ss] = Y[ss] - I[ss];
+
+		U[ss] = (1 / (1 - beta)) * (log(C[ss]) - Theta * L[ss]);
+        lambda[ss] = 1 / C[ss];
+    };
+};
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = log(C[]) - Theta * L[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	controls
+	{
+		C[], L[];
+	};
+
+	constraints
+	{
+	    @exclude
+		C[] = w[] * L[] + Pi[] : lambda[];
+	};
+
+	calibration
+	{
+		beta = 0.99;
+		Theta = 1;
+	};
+};
+
+block FIRM
+{
+	definitions
+	{
+		pi[] = Y[] - (w[] * L[] + I[]);
+	};
+
+	objective
+	{
+		Pi[] = pi[] + beta * E[][lambda[1] / lambda[] * Pi[1]];
+	};
+
+	controls
+	{
+		Y[], L[], K[], I[];
+	};
+
+	constraints
+	{
+		Y[] = A[] * K[-1] ^ alpha * L[] ^ (1 - alpha);
+		K[] = (1 - delta) * K[-1] + I[];
+	};
+
+	identities
+    {
+        log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+    };
+
+	shocks
+    {
+        epsilon_A[];
+    };
+
+	calibration
+	{
+		delta = 0.035;
+		alpha = 0.35;
+		rho_A = 0.95;
+	};
+};
+
+block EQUILIBRIUM
+{
+    identities
+    {
+        Y[] = C[] + I[];
+    };
+};
diff --git a/tests/Test GCNs/rbc_firm_capital_comparison.gcn b/tests/Test GCNs/rbc_firm_capital_comparison.gcn
new file mode 100644
index 0000000..0693b30
--- /dev/null
+++ b/tests/Test GCNs/rbc_firm_capital_comparison.gcn	
@@ -0,0 +1,103 @@
+tryreduce
+{
+	Pi[], U[], TC[];
+};
+
+block STEADYSTATE
+{
+    definitions
+    {
+		# Capital/Labor Ratio
+		N[ss] = (alpha * beta * A[ss] / (1 - beta * (1 - delta)))
+					^ (1 / (1 - alpha));
+    };
+
+    identities
+    {
+        A[ss] = 1;
+        P[ss] = 1;
+        Pi[ss] = 0;
+
+		L[ss] = (1 - alpha) / Theta / (1 - delta * N[ss] ^ (1 - alpha));
+		K[ss] = N[ss] * L[ss];
+
+		r[ss] = 1 / beta - (1 - delta);
+		w[ss] = (1 - alpha) * N[ss] ^ alpha;
+
+		Y[ss] = A[ss] * K[ss] ^ alpha * L[ss] ^ (1 - alpha);
+		I[ss] = delta * K[ss];
+		C[ss] = Y[ss] - I[ss];
+
+		U[ss] = (1 / (1 - beta)) * (log(C[ss]) - Theta * L[ss]);
+        lambda[ss] = 1 / (C[ss] * P[ss]);
+        TC[ss] = -(r[ss] * K[ss] + w[ss] * L[ss]);
+    };
+};
+
+block HOUSEHOLD
+{
+	definitions
+	{
+		u[] = log(C[]) - Theta * L[];
+	};
+
+	objective
+	{
+		U[] = u[] + beta * E[][U[1]];
+	};
+
+	controls
+	{
+		C[], L[], I[], K[];
+	};
+
+	constraints
+	{
+		C[] + I[] = w[] * L[] + r[] * K[-1] + Pi[] : lambda[];
+		K[] = (1 - delta) * K[-1] + I[];
+	};
+
+	calibration
+	{
+		beta = 0.99;
+		Theta = 1;
+        delta = 0.035;
+	};
+};
+
+
+block FIRM
+{
+	controls
+	{
+		K[-1], L[];
+	};
+
+    objective
+    {
+		TC[] = -(w[] * L[] + r[] * K[-1]);
+    };
+
+    constraints
+    {
+		Y[] = A[] * K[-1] ^ alpha * L[] ^ (1 - alpha) : P[];
+    };
+
+	identities
+	{
+		log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+		P[] = 1;
+		Pi[] = P[] * Y[] + TC[];
+	};
+
+	shocks
+	{
+		epsilon_A[];
+	};
+
+    calibration
+    {
+		alpha = 0.35;
+		rho_A = 0.95;
+    };
+};
diff --git a/tests/Test GCNs/RBC_Linearized.gcn b/tests/Test GCNs/rbc_linearized.gcn
similarity index 60%
rename from tests/Test GCNs/RBC_Linearized.gcn
rename to tests/Test GCNs/rbc_linearized.gcn
index 2e44ab9..da1b027 100644
--- a/tests/Test GCNs/RBC_Linearized.gcn	
+++ b/tests/Test GCNs/rbc_linearized.gcn	
@@ -3,6 +3,22 @@ options
     linear = True;
 };
 
+block STEADY_STATE
+{
+    identities
+    {
+		A[ss] = 1;
+		R[ss] = (1 / beta - (1 - delta));
+		W[ss] = (1 - alpha) ^ (1 / (1 - alpha)) * (alpha / R[ss]) ^ (alpha / (1 - alpha));
+		Y[ss] = (R[ss] / (R[ss] - delta * alpha)) ^ (sigma / (sigma + phi)) *
+			   ((1 - alpha) ^ (-phi) * (W[ss]) ^ (1 + phi)) ^ (1 / (sigma + phi));
+		K[ss] = alpha * Y[ss] / R[ss];
+		I[ss] = delta * K[ss];
+		C[ss] = Y[ss] - I[ss];
+		L[ss] = (1 - alpha) * Y[ss] / W[ss];
+    };
+};
+
 block SYSTEM_EQUATIONS
 {
 	identities
@@ -11,7 +27,7 @@ block SYSTEM_EQUATIONS
 		W[] = sigma * C[] + phi * L[];
 
 		#2. Euler Equation
-		sigma / beta * (E[][C[1]] - C[]) = R_ss * E[][R[1]];
+		sigma / beta * (E[][C[1]] - C[]) = R[ss] * E[][R[1]];
 
 		#3. Law of motion of capital
 		K[] = (1 - delta) * K[-1] + delta * I[];
@@ -26,11 +42,10 @@ block SYSTEM_EQUATIONS
 		W[] = Y[] - L[];
 
 		#7. Equlibrium Condition
-		Y_ss * Y[] = C_ss * C[] + I_ss * I[];
+		Y[ss] * Y[] = C[ss] * C[] + I[ss] * I[];
 
 		#8. Productivity Shock
 		A[] = rho_A * A[-1] + epsilon_A[];
-
 	};
 
 	shocks
@@ -47,16 +62,6 @@ block SYSTEM_EQUATIONS
 		delta   ~ Beta(a=1, b=10) = 0.025;
 		rho_A   ~ Beta(a=1, b=5) = 0.95;
 		sigma_A ~ Gamma(a=2, scale=0.005) = 0.01;
-
-		#P_ss = 1;
-		R_ss = (1 / beta - (1 - delta));
-		W_ss = (1 - alpha) ^ (1 / (1 - alpha)) * (alpha / R_ss) ^ (alpha / (1 - alpha));
-		Y_ss = (R_ss / (R_ss - delta * alpha)) ^ (sigma / (sigma + phi)) *
-			   ((1 - alpha) ^ (-phi) * (W_ss) ^ (1 + phi)) ^ (1 / (sigma + phi));
-		K_ss = alpha * Y_ss / R_ss;
-		I_ss = delta * K_ss;
-		C_ss = Y_ss - I_ss;
-		L_ss = (1 - alpha) * Y_ss / W_ss;
 	};
 
 };
diff --git a/gEconpy/numba_tools/__init__.py b/tests/Test GCNs/rbc_manually_calibrated.gcn
similarity index 100%
rename from gEconpy/numba_tools/__init__.py
rename to tests/Test GCNs/rbc_manually_calibrated.gcn
diff --git a/tests/Test GCNs/rbc_with_excluded.gcn b/tests/Test GCNs/rbc_with_excluded.gcn
new file mode 100644
index 0000000..7895deb
--- /dev/null
+++ b/tests/Test GCNs/rbc_with_excluded.gcn	
@@ -0,0 +1,75 @@
+block HOUSEHOLD
+{
+    definitions
+    {
+		u[] = C[] ^ (1 - sigma_C) / (1 - sigma_C) -
+			  L[] ^ (1 + sigma_L) / (1 + sigma_L);
+    };
+    controls
+    {
+		K[], C[], L[], I[];
+    };
+    objective
+    {
+		U[] = u[] + beta * E[][U[1]];
+    };
+    constraints
+    {
+        @exclude
+		C[] + I[] = w[] * L[] + r[] * K[-1] : lambda[];
+
+		K[] = (1 - delta) * K[-1] + I[] : q[];
+    };
+
+    calibration
+    {
+		beta = 0.985;
+		delta = 0.025;
+		sigma_C = 2;
+		sigma_L = 1.5;
+    };
+};
+
+
+block FIRM
+{
+	controls
+	{
+		K[-1], L[];
+	};
+
+    objective
+    {
+		TC[] = -(w[] * L[] + r[] * K[-1]);
+    };
+
+    constraints
+    {
+		Y[] = A[] * K[-1] ^ alpha * L[] ^ (1 - alpha) : P[];
+    };
+
+	identities
+	{
+		log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+		P[] = 1;
+	};
+
+	shocks
+	{
+		epsilon_A[];
+	};
+
+    calibration
+    {
+		alpha = 0.35;
+		rho_A = 0.95;
+    };
+};
+
+block EQUILIBRIUM
+{
+    constraints
+    {
+        Y[] = C[] + I[];
+    };
+};
diff --git a/tests/distribution_tests/__init__.py b/tests/distribution_tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/distribution_tests/beta_distribution_test.py b/tests/distribution_tests/beta_distribution_test.py
index 674b61c..63c3389 100644
--- a/tests/distribution_tests/beta_distribution_test.py
+++ b/tests/distribution_tests/beta_distribution_test.py
@@ -1,8 +1,8 @@
 import unittest
-from functools import partial
 
+from functools import partial
 
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     IgnoredCloseMatchWarning,
     InvalidParameterException,
     UnusedParameterWarning,
diff --git a/tests/distribution_tests/exponential_test.py b/tests/distribution_tests/exponential_test.py
new file mode 100644
index 0000000..d405ab4
--- /dev/null
+++ b/tests/distribution_tests/exponential_test.py
@@ -0,0 +1,77 @@
+import unittest
+
+from functools import partial
+
+from gEconpy.exceptions import (
+    IgnoredCloseMatchWarning,
+    InvalidParameterException,
+    UnusedParameterWarning,
+)
+from gEconpy.parser.parse_distributions import (
+    EXPONENTIAL_RATE_ALIASES,
+    ExponentialDistributionParser,
+)
+
+
+class TestExponentialDistributionParser(unittest.TestCase):
+    def setUp(self):
+        self.parser = ExponentialDistributionParser("alpha")
+
+        self.parse_loc_parameter = partial(
+            self.parser._parse_parameter,
+            canon_name=self.parser.loc_param_name,
+            aliases=[self.parser.loc_param_name],
+        )
+
+        self.parse_scale_parameter = partial(
+            self.parser._parse_parameter,
+            canon_name=self.parser.scale_param_name,
+            aliases=[self.parser.scale_param_name],
+        )
+        self.parse_rate_parameter = partial(
+            self.parser._parse_parameter,
+            canon_name=self.parser.rate_param_name,
+            aliases=EXPONENTIAL_RATE_ALIASES,
+        )
+
+    def test_parse_loc_parameter(self):
+        parsed_dict = self.parse_loc_parameter({"loc": "1", "sd": "1"})
+        self.assertEqual(parsed_dict, {"loc": 1})
+
+        self.parser._parse_mean_constraint({"mean": "3"})
+        self.assertEqual(self.parser.mean_constraint, 3)
+
+    def test_typo_in_loc_parameter(self):
+        param_dict = {"loocc": "0", "sd": "1"}
+
+        self.assertWarns(IgnoredCloseMatchWarning, self.parse_loc_parameter, param_dict)
+
+    def test_parse_scale_parameter(self):
+        self.parser._parse_std_constraint({"std": "2"})
+        self.assertEqual(self.parser.std_constraint, 2)
+
+        parsed_dict = self.parse_scale_parameter({"scale": "0.5"})
+        self.assertEqual(parsed_dict, {"scale": 0.5})
+
+    def test_typo_in_scale_parameter(self):
+        param_dict = {"mean": "0", "scaale": "2.5"}
+        self.assertWarns(
+            IgnoredCloseMatchWarning, self.parse_scale_parameter, param_dict
+        )
+
+    def test_unused_parameter_warning(self):
+        parser = self.parser
+        param_dict = {"rate": "1", "x": "3", "y": "4", "z": "6"}
+
+        self.assertWarns(UnusedParameterWarning, parser.build_distribution, param_dict)
+
+    def test_distribution_from_moments(self):
+        parser = self.parser
+        d = parser.build_distribution({"sd": "0.1", "mean": "3"})
+
+        self.assertAlmostEqual(d.mean(), 3)
+        self.assertAlmostEqual(d.std(), 10)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/distribution_tests/gamma_distribution_test.py b/tests/distribution_tests/gamma_distribution_test.py
index c2e68b8..536029a 100644
--- a/tests/distribution_tests/gamma_distribution_test.py
+++ b/tests/distribution_tests/gamma_distribution_test.py
@@ -1,9 +1,10 @@
 import unittest
+
 from functools import partial
 
 import numpy as np
 
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     IgnoredCloseMatchWarning,
     UnusedParameterWarning,
 )
@@ -50,7 +51,7 @@ def test_parse_scale_parameter(self):
         self.parser._parse_std_constraint({"std": "2"})
         self.assertEqual(self.parser.std_constraint, 2)
 
-        parsed_dict = self.parse_scale_parameter({"beta": "1"})
+        parsed_dict = self.parse_scale_parameter({"theta": "1"})
         self.assertEqual(parsed_dict, {"scale": 1})
 
     def test_typo_in_scale_parameter(self):
@@ -85,7 +86,7 @@ def test_distribution_from_moments(self):
 
     def test_distribution_from_scale_and_shape(self):
         parser = self.parser
-        d = parser.build_distribution({"alpha": "0.5", "beta": "2"})
+        d = parser.build_distribution({"alpha": "0.5", "theta": "2"})
 
         self.assertAlmostEqual(d.mean(), 0.5 * 2)
         self.assertAlmostEqual(d.std(), np.sqrt(0.5 * 2**2))
diff --git a/tests/distribution_tests/halfnormal_test.py b/tests/distribution_tests/halfnormal_test.py
index 95f59fe..6757218 100644
--- a/tests/distribution_tests/halfnormal_test.py
+++ b/tests/distribution_tests/halfnormal_test.py
@@ -1,9 +1,10 @@
 import unittest
+
 from functools import partial
 
 from scipy.stats import halfnorm
 
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     IgnoredCloseMatchWarning,
     MultipleParameterDefinitionException,
     UnusedParameterWarning,
diff --git a/tests/distribution_tests/inverse_gamma_test.py b/tests/distribution_tests/inverse_gamma_test.py
index 3477af2..8cc81df 100644
--- a/tests/distribution_tests/inverse_gamma_test.py
+++ b/tests/distribution_tests/inverse_gamma_test.py
@@ -1,9 +1,10 @@
 import unittest
+
 from functools import partial
 
 import numpy as np
 
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     IgnoredCloseMatchWarning,
     MultipleParameterDefinitionException,
     UnusedParameterWarning,
diff --git a/tests/distribution_tests/normal_test.py b/tests/distribution_tests/normal_test.py
index 4220928..2f09538 100644
--- a/tests/distribution_tests/normal_test.py
+++ b/tests/distribution_tests/normal_test.py
@@ -1,10 +1,12 @@
 import unittest
+
 from functools import partial
 
 import numpy as np
+
 from scipy.stats import truncnorm
 
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     IgnoredCloseMatchWarning,
     MultipleParameterDefinitionException,
     UnusedParameterWarning,
diff --git a/tests/dynare_outputs/basic_rbc_loglinear_results.mat b/tests/dynare_outputs/basic_rbc_loglinear_results.mat
new file mode 100644
index 0000000..a84588c
Binary files /dev/null and b/tests/dynare_outputs/basic_rbc_loglinear_results.mat differ
diff --git a/tests/dynare_outputs/basic_rbc_results.mat b/tests/dynare_outputs/basic_rbc_results.mat
new file mode 100644
index 0000000..3397d9b
Binary files /dev/null and b/tests/dynare_outputs/basic_rbc_results.mat differ
diff --git a/tests/dynare_outputs/full_nk_results.mat b/tests/dynare_outputs/full_nk_results.mat
new file mode 100644
index 0000000..1a3ea00
Binary files /dev/null and b/tests/dynare_outputs/full_nk_results.mat differ
diff --git a/tests/dynare_outputs/one_block_1_ss_results.mat b/tests/dynare_outputs/one_block_1_ss_results.mat
new file mode 100644
index 0000000..adda0f4
Binary files /dev/null and b/tests/dynare_outputs/one_block_1_ss_results.mat differ
diff --git a/tests/dynare_outputs/rbc_2_block_ss_results.mat b/tests/dynare_outputs/rbc_2_block_ss_results.mat
new file mode 100644
index 0000000..abfbf48
Binary files /dev/null and b/tests/dynare_outputs/rbc_2_block_ss_results.mat differ
diff --git a/tests/test_block.py b/tests/test_block.py
index bb8c861..2a20ee5 100644
--- a/tests/test_block.py
+++ b/tests/test_block.py
@@ -1,20 +1,24 @@
 import os
+import re
 import unittest
+
 from pathlib import Path
 
 import numpy as np
+import pytest
 import sympy as sp
 
-from gEconpy.classes.block import Block
 from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     ControlVariableNotFoundException,
     DynamicCalibratingEquationException,
     MultipleObjectiveFunctionsException,
     OptimizationProblemNotDefinedException,
 )
+from gEconpy.model.block import Block
 from gEconpy.parser import constants, file_loaders, gEcon_parser
-from gEconpy.shared.utilities import set_equality_equals_zero, unpack_keys_and_values
+from gEconpy.parser.file_loaders import block_dict_to_equation_list
+from gEconpy.utilities import set_equality_equals_zero, unpack_keys_and_values
 
 ROOT = Path(__file__).parent.absolute()
 
@@ -32,7 +36,9 @@ def test_raises_if_controls_missing(self):
             """
 
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
 
         self.assertRaises(
@@ -51,7 +57,9 @@ def test_raises_if_objective_missing(self):
             """
 
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
 
         self.assertRaises(
@@ -75,7 +83,9 @@ def test_raises_if_multiple_objective(self):
             """
 
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
 
         self.assertRaises(
@@ -98,7 +108,9 @@ def test_raises_if_controls_not_found(self):
             """
 
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
 
         self.assertRaises(
@@ -120,7 +132,9 @@ def test_block_parser_handles_empty_block(self):
             };
             """
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
 
         block = Block("HOUSEHOLD", block_dict)
@@ -138,7 +152,9 @@ def test_non_ss_var_in_calibration_raises(self):
             """
 
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
 
         self.assertRaises(
@@ -158,43 +174,13 @@ def test_function_of_variables_in_calibration_raises(self):
             """
 
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
 
         self.assertRaises(ValueError, Block, "HOUSEHOLD", block_dict)
 
-    def test_multiple_leads_in_objective_raises(self):
-        test_file = """
-            block HOUSEHOLD
-            {
-                objective
-                {
-                    U[] = u[] + X[] + beta * E[][U[1] + X[1]];
-                };
-
-                controls
-                {
-                    X[];
-                };
-            };
-            """
-
-        parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
-        block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
-        block = Block("HOUSEHOLD", block_dict)
-
-        with self.assertRaises(ValueError) as error:
-            block.solve_optimization()
-        error_msg = error.exception
-        self.assertEqual(
-            str(error_msg),
-            "Block HOUSEHOLD has multiple t+1 variables in the Bellman equation, this is not "
-            "currently supported. Rewrite the equation in the form X[] = a[] + b * E[][X[1]], "
-            "where a[] is the instantaneous value function at time t, defined in the "
-            '"definitions" component of the block.',
-        )
-
     def test_lagrange_multiplier_in_objective(self):
         test_file = """
             block HOUSEHOLD
@@ -231,7 +217,9 @@ def test_lagrange_multiplier_in_objective(self):
             """
 
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
         block = Block("HOUSEHOLD", block_dict)
 
@@ -239,13 +227,47 @@ def test_lagrange_multiplier_in_objective(self):
             block.solve_optimization()
 
 
+def test_invalid_decorator_raises():
+    test_file = """
+        block HOUSEHOLD
+        {
+            objective
+            {
+                @exclude
+                U[] = u[] + beta * E[][U[1]] : lambda[];
+            };
+
+            controls
+            {
+                u[];
+            };
+        };
+        """
+
+    parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
+    block_dict, options, tryreduce, assumptions = (
+        gEcon_parser.split_gcn_into_dictionaries(parser_output)
+    )
+    block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "Equation Eq(U_t, beta*U_t+1 + u_t) in objective block of HOUSEHOLD "
+            "has an invalid decorator: exclude."
+        ),
+    ):
+        Block("HOUSEHOLD", block_dict)
+
+
 class BlockTestCases(unittest.TestCase):
     def setUp(self):
         test_file = file_loaders.load_gcn(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_2.gcn")
+            os.path.join(ROOT, "Test GCNs/one_block_2.gcn")
         )
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
 
         self.block = Block("HOUSEHOLD", block_dict)
@@ -297,7 +319,7 @@ def test_extract_discount_factor_on_static_eq(self):
 
         self.block.objective = {0: sp.Eq(PI, P * Y - r * K - w * L)}
         df = self.block._get_discount_factor()
-        self.assertEqual(df, 1)
+        assert np.allclose(float(df), 1.0)
 
     def test_extract_discount_factor_on_lagged_eq(self):
         PI = TimeAwareSymbol("Pi", 0)
@@ -310,7 +332,7 @@ def test_extract_discount_factor_on_lagged_eq(self):
 
         self.block.objective = {0: sp.Eq(PI, P * Y - r * K - w * L)}
         df = self.block._get_discount_factor()
-        self.assertEqual(df, 1)
+        assert np.allclose(float(df), 1)
 
     def test_household_lagrangian_function(self):
         U = TimeAwareSymbol("U", 1)
@@ -324,13 +346,13 @@ def test_household_lagrangian_function(self):
         lamb_H_1 = TimeAwareSymbol("lambda__H_1", 0)
         q = TimeAwareSymbol("q", 0)
 
-        alpha, beta, delta, theta, tau = sp.symbols(
-            ["alpha", "beta", "delta", "theta", "tau"]
+        alpha, beta, delta, theta, tau, Theta, zeta = sp.symbols(
+            ["alpha", "beta", "delta", "theta", "tau", "Theta", "zeta"]
         )
 
         utility = (C**theta * (1 - L) ** (1 - theta)) ** (1 - tau) / (1 - tau)
         mkt_clearing = C + I - Y
-        production = Y - A * K**alpha * L ** (1 - alpha)
+        production = Y - A * K**alpha * L ** (1 - alpha) - (Theta + zeta)
         law_motion_K = K - (1 - delta) * K.step_backward() - I
 
         answer = (
@@ -342,7 +364,7 @@ def test_household_lagrangian_function(self):
         )
 
         L = self.block._build_lagrangian()
-        self.assertEqual((L - answer).simplify(), 0)
+        assert (L - answer).simplify().evalf() == 0
 
     def test_Household_FOC(self):
         self.block.solve_optimization(try_simplify=False)
@@ -408,6 +430,12 @@ def test_Household_FOC(self):
             zip(all_variables, np.random.uniform(0, 1, size=len(all_variables)))
         )
 
+        # These are extraneous parameters used to test deterministic relationships. We can ignore them for the
+        # purpose of this test.
+        Theta, zeta = sp.symbols("Theta, zeta")
+        sub_dict[Theta] = 0
+        sub_dict[zeta] = 0
+
         dL_dC = (C**theta * (1 - L) ** (1 - theta)) ** (-tau) * C ** (theta - 1) * (
             1 - L
         ) ** (1 - theta) * theta - lamb
@@ -431,10 +459,12 @@ def test_Household_FOC(self):
 
     def test_firm_block_lagrange_parsing(self):
         test_file = file_loaders.load_gcn(
-            os.path.join(ROOT, "Test GCNs/Two_Block_RBC_1.gcn")
+            os.path.join(ROOT, "Test GCNs/rbc_2_block.gcn")
         )
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         block_dict = gEcon_parser.parsed_block_to_dict(block_dict["FIRM"])
 
         block = Block("FIRM", block_dict)
@@ -456,10 +486,12 @@ def test_firm_block_lagrange_parsing(self):
 
     def test_firm_FOC(self):
         test_file = file_loaders.load_gcn(
-            os.path.join(ROOT, "Test GCNs/Two_Block_RBC_1.gcn")
+            os.path.join(ROOT, "Test GCNs/rbc_2_block.gcn")
         )
         parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
+        block_dict, options, tryreduce, assumptions = (
+            gEcon_parser.split_gcn_into_dictionaries(parser_output)
+        )
         firm_dict = gEcon_parser.parsed_block_to_dict(block_dict["FIRM"])
 
         firm_block = Block("FIRM", firm_dict)
@@ -512,13 +544,15 @@ def test_get_param_dict_and_calibrating_equations(self):
         K = TimeAwareSymbol("K", 0).to_ss()
         L = TimeAwareSymbol("L", 0).to_ss()
 
-        answer = {theta: 0.357, beta: 0.99, delta: 0.02, tau: 2, rho: 0.95}
+        answer = {theta: 0.357, beta: 1 / 1.01, delta: 0.02, tau: 2, rho: 0.95}
         self.assertEqual(
             all([key in self.block.param_dict.keys() for key in answer.keys()]), True
         )
 
         for key in self.block.param_dict:
-            self.assertEqual((answer[key] - self.block.param_dict[key]).simplify(), 0)
+            np.testing.assert_allclose(
+                answer[key], self.block.param_dict.values_to_float()[key]
+            )
 
         assert self.block.params_to_calibrate == [alpha]
 
@@ -540,9 +574,38 @@ def test_deterministic_relationships(self):
         self.assertEqual(
             [x.name for x in self.block.deterministic_params], ["Theta", "zeta"]
         )
-        answers = [3 + 0.99 * 0.95, -np.log(0.357)]
+        answers = [3 + 1 / 1.01 * 0.95, -np.log(0.357)]
         for eq, answer in zip(self.block.deterministic_relationships, answers):
-            self.assertEqual(eq.subs(self.block.param_dict), answer)
+            np.testing.assert_allclose(
+                float(eq.subs(self.block.param_dict).evalf()), answer
+            )
+
+    def test_variable_list(self):
+        self.block.solve_optimization(try_simplify=False)
+        self.assertEqual(
+            {x.base_name for x in self.block.variables},
+            {"A", "C", "I", "K", "L", "U", "Y", "lambda", "q", "lambda__H_1"},
+        )
+        self.assertEqual({x.base_name for x in self.block.shocks}, {"epsilon"})
+
+
+def test_block_with_exlcuded_equation():
+    test_file = file_loaders.load_gcn(
+        os.path.join(ROOT, "Test GCNs/rbc_with_excluded.gcn")
+    )
+
+    parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
+    block_dict, options, tryreduce, assumptions = (
+        gEcon_parser.split_gcn_into_dictionaries(parser_output)
+    )
+
+    block_dict = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
+
+    block = Block("HOUSEHOLD", block_dict)
+    block.solve_optimization()
+
+    # 6 equations are 4 controls, 1 objective, 1 constraint (excluding the excluded equation)
+    assert len(block.system_equations) == 6
 
 
 if __name__ == "__main__":
diff --git a/tests/test_compile.py b/tests/test_compile.py
new file mode 100644
index 0000000..5046886
--- /dev/null
+++ b/tests/test_compile.py
@@ -0,0 +1,89 @@
+from typing import Literal
+
+import numpy as np
+import pytest
+import sympy as sp
+
+from gEconpy.model.compile import BACKENDS, compile_function
+
+
+@pytest.mark.parametrize("backend", ["numpy", "numba", "pytensor"])
+def test_scalar_function(backend: Literal["numpy", "numba", "pytensor"]):
+    x = sp.symbols("x")
+    f = x**2
+    f_func, _ = compile_function(
+        [x], f, backend=backend, mode="FAST_COMPILE", pop_return=backend == "pytensor"
+    )
+    assert f_func(x=2) == 4
+
+
+@pytest.mark.parametrize("backend", ["numpy", "numba", "pytensor"])
+@pytest.mark.parametrize("stack_return", [True, False])
+def test_multiple_outputs(
+    backend: Literal["numpy", "numba", "pytensor"], stack_return: bool
+):
+    x, y, z = sp.symbols("x y z ")
+    x2 = x**2
+    y2 = y**2
+    z2 = z**2
+    f_func, _ = compile_function(
+        [x, y, z],
+        [x2, y2, z2],
+        backend=backend,
+        stack_return=stack_return,
+        mode="FAST_COMPILE",
+    )
+    res = f_func(x=2, y=3, z=4)
+    assert (
+        isinstance(res, np.ndarray) if stack_return else isinstance(res, list | tuple)
+    )
+    assert res.shape == (3,) if stack_return else len(res) == 3
+    np.testing.assert_allclose(
+        res if stack_return else np.stack(res), np.array([4.0, 9.0, 16.0])
+    )
+
+
+@pytest.mark.parametrize("backend", ["numpy", "numba", "pytensor"])
+def test_matrix_function(backend: Literal["numpy", "numba", "pytensor"]):
+    x, y, z = sp.symbols("x y z")
+    f = sp.Matrix([x, y, z]).reshape(1, 3)
+
+    f_func, _ = compile_function(
+        [x, y, z],
+        f,
+        backend=backend,
+        mode="FAST_COMPILE",
+        pop_return=backend == "pytensor",
+    )
+    res = f_func(x=2, y=3, z=4)
+
+    assert isinstance(res, np.ndarray)
+    assert res.shape == (1, 3)
+    np.testing.assert_allclose(res, np.array([[2.0, 3.0, 4.0]]))
+
+
+@pytest.mark.parametrize("backend", ["numpy", "numba", "pytensor"])
+def test_compile_gradient(backend: BACKENDS):
+    x, y, z = sp.symbols("x y z")
+    f = x**2 + y**2 + z**2
+    grad = sp.Matrix([f.diff(x), f.diff(y), f.diff(z)]).reshape(3, 1)
+    grad_func, _ = compile_function(
+        [x, y, z],
+        grad,
+        backend=backend,
+        mode="FAST_COMPILE",
+        pop_return=backend == "pytensor",
+    )
+    res = grad_func(x=2.0, y=3.0, z=4.0)
+    np.testing.assert_allclose(res, np.array([4.0, 6.0, 8.0])[:, None])
+
+    hess = grad.jacobian([x, y, z])
+    hess_func, _ = compile_function(
+        [x, y, z],
+        hess,
+        backend=backend,
+        mode="FAST_COMPILE",
+        pop_return=backend == "pytensor",
+    )
+    res = hess_func(x=2.0, y=3.0, z=4.0)
+    np.testing.assert_allclose(res, np.eye(3) * 2.0)
diff --git a/tests/test_containers.py b/tests/test_containers.py
index e6a7872..a770481 100644
--- a/tests/test_containers.py
+++ b/tests/test_containers.py
@@ -1,6 +1,7 @@
 import unittest
 
 import sympy as sp
+
 from sympy.polys.domains.mpelements import ComplexElement
 
 from gEconpy.classes.containers import SymbolDictionary
@@ -48,6 +49,13 @@ def setUp(self) -> None:
 
         self.d = SymbolDictionary({C: 1, A: -1, r: 2j, alpha: 0.3})
 
+    def test_is_variable(self):
+        assert list(self.d._is_variable.keys()) == ["C", "A", "r", "alpha"]
+        assert self.d._is_variable["C"]
+        assert self.d._is_variable["A"]
+        assert self.d._is_variable["r"]
+        assert not self.d._is_variable["alpha"]
+
     def test_convert_to_string(self):
         d = self.d.to_string()
         self.assertEqual(list(d.keys()), ["C_t", "A_tp1", "r_tm1", "alpha"])
@@ -63,6 +71,24 @@ def test_convert_to_sympy(self):
         self.assertEqual(list(d.keys()), [sp.Symbol("a"), sp.Symbol("b")])
         self.assertTrue(d.is_sympy)
 
+    def test_ambiguous_new_key(self):
+        # Test that when we add something in string mode, it gets "duck typed"
+        d = self.d.to_string()
+        d["F_ss"] = 3
+
+        d.to_sympy(inplace=True)
+        F_ss = TimeAwareSymbol("F", "ss")
+        assert F_ss in d.keys()
+
+        # But when we add in symbol mode, the original type (Symbol vs TimeAwareSymbol) is preserved
+        d = self.d.copy()
+        F_ss2 = sp.Symbol("F_ss")
+        d[F_ss2] = 3
+        d.to_string(inplace=True)
+        assert "F_ss" in d.keys()
+        d.to_sympy(inplace=True)
+        assert F_ss2 in d.keys()
+
     def test_copy(self):
         d_copy = self.d.copy()
         d_ref = self.d
@@ -91,7 +117,11 @@ def test_join_with_pipe(self):
         new_d.sort_keys(inplace=True)
 
         self.assertEqual(list(new_d.keys()), [self.A, self.C, F, self.alpha, self.r])
-        self.assertEqual(self.d._assumptions, d1._assumptions | d2._assumptions)
+        self.assertEqual(
+            self.d._assumptions,
+            d1._assumptions | d2._assumptions,
+            d1._is_variable | d2._is_variable,
+        )
 
     def test_step_forward(self):
         d_tp1 = self.d.step_forward().to_string()
@@ -156,24 +186,24 @@ def test_convert_values(self):
         d_sp = d.float_to_values()
         values = list(d_sp.values())
         self.assertTrue(
-            all([isinstance(x, (sp.core.Number, ComplexElement)) for x in values])
+            all([isinstance(x, sp.core.Number | ComplexElement) for x in values])
         )
 
         d_np = d_sp.values_to_float()
         values = list(d_np.values())
-        self.assertTrue(all([isinstance(x, (int, float, complex)) for x in values]))
+        self.assertTrue(all([isinstance(x, int | float | complex) for x in values]))
 
     def test_convert_values_inplace(self):
         d = self.d.copy()
         d.float_to_values(inplace=True)
         values = list(d.values())
         self.assertTrue(
-            all([isinstance(x, (sp.core.Number, ComplexElement)) for x in values])
+            all([isinstance(x, sp.core.Number | ComplexElement) for x in values])
         )
 
         d.values_to_float(inplace=True)
         values = list(d.values())
-        self.assertTrue(all([isinstance(x, (int, float, complex)) for x in values]))
+        self.assertTrue(all([isinstance(x, int | float | complex) for x in values]))
 
     def test_not_inplace_update_is_not_persistent(self):
         d = self.d
diff --git a/tests/test_distribution_parser.py b/tests/test_distribution_parser.py
index 7c87df4..fbb2bc9 100644
--- a/tests/test_distribution_parser.py
+++ b/tests/test_distribution_parser.py
@@ -1,6 +1,7 @@
-import unittest
+import numpy as np
+import pytest
 
-from gEconpy.exceptions.exceptions import (
+from gEconpy.exceptions import (
     DistributionParsingError,
     InvalidDistributionException,
     MissingParameterValueException,
@@ -14,253 +15,250 @@
 )
 
 
-class BasicParerFunctionalityTests(unittest.TestCase):
-    def setUp(self):
-        self.file = """
-                Block TEST
+@pytest.fixture
+def file():
+    return """
+            Block TEST
+            {
+                shocks
                 {
-                    shocks
-                    {
-                        epsilon[] ~ norm(mu = 0, sd = 1);
-                    };
-
-                    calibration
-                    {
-                        alpha ~ N(mean = 0, sd = 1) = 0.5;
-                    };
+                    epsilon[] ~ norm(mu = 0, sd = 1);
                 };
-        """
 
-    def test_extract_param_dist_simple(self):
-        model, prior_dict = preprocess_gcn(self.file)
-        self.assertEqual(list(prior_dict.keys()), ["epsilon[]", "alpha"])
-        self.assertEqual(
-            list(prior_dict.values()), ["norm(mu = 0, sd = 1)", "N(mean = 0, sd = 1)"]
-        )
-
-    def test_catch_no_initial_value(self):
-        no_initial_value = """
-                Block TEST
+                calibration
                 {
-                    calibration
-                    {
-                        alpha ~ N(mean = 0, sd = 1);
-                    };
+                    alpha ~ N(mean = 0, sd = 1) = 0.5;
                 };
-        """
+            };
+    """
 
-        self.assertRaises(
-            MissingParameterValueException, preprocess_gcn, no_initial_value
-        )
 
-    def test_catch_typo_in_param_dist_definition(self):
-        squiggle_is_equal = """
-                Block TEST
-                {
-                    calibration
-                    {
-                        alpha = N((mean = 0, sd = 1) = 0.5;
-                    };
-                };
-        """
+def test_extract_param_dist_simple(file):
+    model, prior_dict = preprocess_gcn(file)
+    assert list(prior_dict.keys()) == ["epsilon[]", "alpha"]
+    assert list(prior_dict.values()) == [
+        "norm(mu = 0, sd = 1)",
+        "N(mean = 0, sd = 1) = 0.5",
+    ]
 
-        self.assertRaises(DistributionParsingError, preprocess_gcn, squiggle_is_equal)
 
-    def test_catch_distribution_typos(self):
-        extra_parenthesis_start = """
-                Block TEST
+def test_catch_no_initial_value(file):
+    no_initial_value = """
+            Block TEST
+            {
+                calibration
                 {
-                    calibration
-                    {
-                        alpha ~ N((mean = 0, sd = 1) = 0.5;
-                    };
+                    alpha ~ N(mean = 0, sd = 1);
                 };
-        """
+            };
+    """
 
-        extra_parenthesis_end = """
-                Block TEST
-                {
-                    calibration
-                    {
-                        alpha ~ N(mean = 0, sd = 1)) = 0.5;
-                    };
-                };
-        """
+    with pytest.raises(MissingParameterValueException):
+        preprocess_gcn(no_initial_value)
 
-        extra_equals = """
-                Block TEST
-                {
-                    calibration
-                    {
-                        alpha ~ N(mean == 0, sd = 1) = 0.5;
-                    };
-                };
-        """
 
-        missing_common = """
-                Block TEST
-                {
-                    calibration
-                    {
-                        alpha ~ N(mean = 0 sd = 1) = 0.5;
-                    };
-                };
-        """
+extra_parenthesis_start = """
+        Block TEST
+        {
+            calibration
+            {
+                alpha ~ N((mean = 0, sd = 1) = 0.5;
+            };
+        };
+"""
 
-        shock_with_starting_value = """
-                Block TEST
-                {
-                    shocks
-                    {
-                        epsilon[] ~ N(mean = 0, sd = 1) = 0.5;
-                    };
-                };
-        """
-
-        test_files = [
-            extra_parenthesis_start,
-            extra_parenthesis_end,
-            extra_equals,
-            missing_common,
-            shock_with_starting_value,
-        ]
-
-        for file in test_files:
-            model, prior_dict = preprocess_gcn(file)
-            for param_name, distribution_string in prior_dict.items():
-                self.assertRaises(
-                    InvalidDistributionException,
-                    preprocess_distribution_string,
-                    variable_name=param_name,
-                    d_string=distribution_string,
-                )
-
-    def test_catch_repeated_parameter_definition(self):
-        repeated_parameter = """
-                Block TEST
+extra_parenthesis_end = """
+        Block TEST
+        {
+            calibration
+            {
+                alpha ~ N(mean = 0, sd = 1)) = 0.5;
+            };
+        };
+"""
+
+extra_equals = """
+        Block TEST
+        {
+            calibration
+            {
+                alpha ~ N(mean == 0, sd = 1) = 0.5;
+            };
+        };
+"""
+
+missing_common = """
+        Block TEST
+        {
+            calibration
+            {
+                alpha ~ N(mean = 0 sd = 1) = 0.5;
+            };
+        };
+"""
+
+shock_with_starting_value = """
+        Block TEST
+        {
+            shocks
+            {
+                epsilon[] ~ N(mean = 0, sd = 1) = 0.5;
+            };
+        };
+"""
+
+typo_cases = [
+    extra_parenthesis_start,
+    extra_parenthesis_end,
+    extra_equals,
+    missing_common,
+    shock_with_starting_value,
+]
+
+case_names = [
+    "extra_parenthesis_start",
+    "extra_parenthesis_end",
+    "extra_equals",
+    "missing_common",
+    "shock_with_starting_value",
+]
+
+
+@pytest.mark.parametrize("case", typo_cases, ids=case_names)
+def test_catch_distribution_typos(case):
+    model, prior_dict = preprocess_gcn(case)
+    for param_name, distribution_string in prior_dict.items():
+        with pytest.raises(InvalidDistributionException):
+            preprocess_distribution_string(
+                variable_name=param_name, d_string=distribution_string
+            )
+
+
+def test_catch_repeated_parameter_definition(file):
+    repeated_parameter = """
+            Block TEST
+            {
+                calibration
                 {
-                    calibration
-                    {
-                        alpha ~ N(mean = 0, mean = 1) = 0.5;
-                    };
+                    alpha ~ N(mean = 0, mean = 1) = 0.5;
                 };
-        """
-        model, prior_dict = preprocess_gcn(repeated_parameter)
-
-        for param_name, distribution_string in prior_dict.items():
-            self.assertRaises(
-                RepeatedParameterException,
-                preprocess_distribution_string,
-                variable_name=param_name,
-                d_string=distribution_string,
+            };
+    """
+    model, prior_dict = preprocess_gcn(repeated_parameter)
+
+    for param_name, distribution_string in prior_dict.items():
+        with pytest.raises(RepeatedParameterException):
+            preprocess_distribution_string(
+                variable_name=param_name, d_string=distribution_string
             )
 
-    def test_parameter_parsing_simple(self):
-        model, prior_dict = preprocess_gcn(self.file)
-        dicts = [{"mu": "0", "sd": "1"}, {"mean": "0", "sd": "1"}]
 
-        for i, (param_name, distribution_string) in enumerate(prior_dict.items()):
-            dist_name, param_dict = preprocess_distribution_string(
-                param_name, distribution_string
-            )
+def test_parameter_parsing_simple(file):
+    model, prior_dict = preprocess_gcn(file)
+    dicts = [
+        {"mu": 0.0, "sd": 1.0, "initial_value": None},
+        {"mean": 0.0, "sd": 1.0, "initial_value": 0.5},
+    ]
+
+    for i, (param_name, distribution_string) in enumerate(prior_dict.items()):
+        dist_name, param_dict = preprocess_distribution_string(
+            param_name, distribution_string
+        )
 
-            self.assertEqual(dist_name, "normal")
-            self.assertEqual(param_dict, dicts[i])
+        assert dist_name == "normal"
+        assert param_dict == dicts[i]
 
-    def test_parse_compound_distributions(self):
-        compound_distribution = """Block TEST
-                {
-                    calibration
-                    {
-                        sigma_alpha ~ inv_gamma(a=20, scale=1) = 0.01;
-                        mu_alpha ~ N(mean = 1, scale=1) = 0.01;
-                        alpha ~ N(mean = mu_alpha, sd = sigma_alpha) = 0.5;
-                    };
-                };"""
-
-        model, raw_prior_dict = preprocess_gcn(compound_distribution)
-        prior_dict, _ = create_prior_distribution_dictionary(raw_prior_dict)
-
-        d = prior_dict["alpha"]
-
-        self.assertEqual(d.rv_params["loc"].mean(), 1)
-        self.assertEqual(d.rv_params["loc"].std(), 1)
-        self.assertEqual(d.rv_params["scale"].mean(), 1 / (20 - 1))
-        self.assertEqual(d.rv_params["scale"].var(), 1**2 / (20 - 1) ** 2 / (20 - 2))
-
-    def test_multiple_shocks(self):
-        compound_distribution = """Block TEST
+
+def test_parse_compound_distributions(file):
+    compound_distribution = """Block TEST
+            {
+                calibration
                 {
-                    identities
-                    {
-                        log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
-                        log(B[]) = rho_B * log(B[-1]) + epsilon_B[];
-                    };
-
-                    shocks
-                    {
-                        epsilon_A[] ~ N(mean=0, sd=sigma_epsilon_A);
-                        epsilon_B[] ~ N(mean=0, sd=sigma_epsilon_B);
-                    };
-
-                    calibration
-                    {
-                        rho_A ~ Beta(mean=0.95, sd=0.04) = 0.95;
-                        rho_B ~ Beta(mean=0.95, sd=0.04) = 0.95;
-
-                        sigma_epsilon_A ~ Gamma(alpha=1, beta=0.1) = 0.01;
-                        sigma_epsilon_B ~ Gamma(alpha=1, beta=0.1) = 0.01;
-                    };
-                };"""
-
-        model, raw_prior_dict = preprocess_gcn(compound_distribution)
-        prior_dict, _ = create_prior_distribution_dictionary(raw_prior_dict)
-
-        epsilon_A = prior_dict["epsilon_A[]"]
-        epsilon_B = prior_dict["epsilon_B[]"]
-
-        self.assertEqual(len(epsilon_A.rv_params), 1)
-        self.assertEqual(len(epsilon_B.rv_params), 1)
-
-        # self.assertEqual(d.rv_params['loc'].mean(), 1)
-        # self.assertEqual(d.rv_params['loc'].std(), 1)
-        # self.assertEqual(d.rv_params['scale'].mean(), 1 / (20 - 1))
-        # self.assertEqual(d.rv_params['scale'].var(), 1 ** 2 / (20 - 1) ** 2 / (20 - 2))
-
-
-class TestDistributionFactory(unittest.TestCase):
-    def test_parse_distributions(self):
-        file = """
-            TEST_BLOCK
+                    sigma_alpha ~ inv_gamma(a=20, scale=1) = 0.01;
+                    mu_alpha ~ N(mean = 1, scale=1) = 0.01;
+                    alpha ~ N(mean = mu_alpha, sd = sigma_alpha) = 0.5;
+                };
+            };"""
+
+    model, raw_prior_dict = preprocess_gcn(compound_distribution)
+    prior_dict, _ = create_prior_distribution_dictionary(raw_prior_dict)
+
+    d = prior_dict["alpha"]
+
+    assert d.rv_params["loc"].mean() == 1
+    assert d.rv_params["loc"].std() == 1
+    assert d.rv_params["scale"].mean() == 1 / (20 - 1)
+    assert d.rv_params["scale"].var() == 1**2 / (20 - 1) ** 2 / (20 - 2)
+
+
+def test_multiple_shocks():
+    compound_distribution = """Block TEST
             {
+                identities
+                {
+                    log(A[]) = rho_A * log(A[-1]) + epsilon_A[];
+                    log(B[]) = rho_B * log(B[-1]) + epsilon_B[];
+                };
+
                 shocks
                 {
-                    epsilon[] ~ N(mean=0, std=0.1);
+                    epsilon_A[] ~ N(mean=0, sd=sigma_epsilon_A);
+                    epsilon_B[] ~ N(mean=0, sd=sigma_epsilon_B);
                 };
 
                 calibration
                 {
-                    alpha ~ beta(a=1, b=1) = 0.5;
-                    rho ~ gamma(mean=0.95, sd=1) = 0.95;
-                    sigma ~ inv_gamma(mean=0.01, sd=0.1) = 0.01;
-                    tau ~ halfnorm(MEAN=0.5, sd=1) = 1;
-                    psi ~ norm(mean=1.5, Sd=1.5, min=0) = 1;
+                    rho_A ~ Beta(mean=0.95, sd=0.04) = 0.95;
+                    rho_B ~ Beta(mean=0.95, sd=0.04) = 0.95;
+
+                    sigma_epsilon_A ~ Gamma(alpha=1, beta=0.1) = 0.01;
+                    sigma_epsilon_B ~ Gamma(alpha=1, beta=0.1) = 0.01;
                 };
-            };
-        """
+            };"""
 
-        model, prior_dict = preprocess_gcn(file)
-        means = [0, 0.5, 0.95, 0.01, 0.5, 1.5]
-        stds = [0.1, 0.28867513459481287, 1, 0.1, 1, 1.5]
+    model, raw_prior_dict = preprocess_gcn(compound_distribution)
+    prior_dict, _ = create_prior_distribution_dictionary(raw_prior_dict)
 
-        for i, (variable_name, d_string) in enumerate(prior_dict.items()):
-            d_name, param_dict = preprocess_distribution_string(variable_name, d_string)
-            d = distribution_factory(
-                variable_name=variable_name, d_name=d_name, param_dict=param_dict
-            )
-            self.assertAlmostEqual(d.mean(), means[i], places=3)
-            self.assertAlmostEqual(d.std(), stds[i], places=3)
+    epsilon_A = prior_dict["epsilon_A[]"]
+    epsilon_B = prior_dict["epsilon_B[]"]
+
+    assert len(epsilon_A.rv_params) == 1
+    assert len(epsilon_B.rv_params) == 1
+
+    # self.assertEqual(d.rv_params['loc'].mean(), 1)
+    # self.assertEqual(d.rv_params['loc'].std(), 1)
+    # self.assertEqual(d.rv_params['scale'].mean(), 1 / (20 - 1))
+    # self.assertEqual(d.rv_params['scale'].var(), 1 ** 2 / (20 - 1) ** 2 / (20 - 2))
 
 
-if __name__ == "__main__":
-    unittest.main()
+def test_parse_distributions():
+    file = """
+        TEST_BLOCK
+        {
+            shocks
+            {
+                epsilon[] ~ N(mean=0, std=0.1);
+            };
+
+            calibration
+            {
+                alpha ~ beta(a=1, b=1) = 0.5;
+                rho ~ gamma(mean=0.95, sd=1) = 0.95;
+                sigma ~ inv_gamma(mean=0.01, sd=0.1) = 0.01;
+                tau ~ halfnorm(MEAN=0.5, sd=1) = 1;
+                psi ~ norm(mean=1.5, Sd=1.5, min=0) = 1;
+            };
+        };
+    """
+
+    model, prior_dict = preprocess_gcn(file)
+    means = [0, 0.5, 0.95, 0.01, 0.5, 1.5]
+    stds = [0.1, 0.28867513459481287, 1, 0.1, 1, 1.5]
+
+    for i, (variable_name, d_string) in enumerate(prior_dict.items()):
+        d_name, param_dict = preprocess_distribution_string(variable_name, d_string)
+        d = distribution_factory(
+            variable_name=variable_name, d_name=d_name, param_dict=param_dict
+        )
+        np.testing.assert_allclose(d.mean(), means[i], atol=1e-3)
+        np.testing.assert_allclose(d.std(), stds[i], atol=1e-3)
diff --git a/tests/test_dynare_convert.py b/tests/test_dynare_convert.py
index 2a609f9..7636c71 100644
--- a/tests/test_dynare_convert.py
+++ b/tests/test_dynare_convert.py
@@ -1,110 +1,245 @@
-import os
-import unittest
-from pathlib import Path
+import re
 
 import numpy as np
+import pytest
 import sympy as sp
 
-from gEconpy import gEconModel
+from gEconpy import model_from_gcn
 from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.shared.dynare_convert import (
-    build_hash_table,
-    convert_var_timings_to_matlab,
-    get_name,
+from gEconpy.dynare_convert import (
+    DynareCodePrinter,
+    find_ss_variables,
     make_mod_file,
-    make_var_to_matlab_sub_dict,
-    substitute_equation_from_dict,
-    write_lines_from_list,
+    write_model_equations,
+    write_param_names,
+    write_parameter_declarations,
+    write_shock_declarations,
+    write_shock_std,
+    write_steady_state,
+    write_variable_declarations,
 )
+from gEconpy.parser.constants import LOCAL_DICT
 
-ROOT = Path(__file__).parent.absolute()
 
+@pytest.mark.parametrize("op", ["*", "/"], ids=["multiplication", "division"])
+def test_print_multiplication(op):
+    printer = DynareCodePrinter()
+    expr = sp.parse_expr(f"a {op} x - 4", transformations="all")
+    out = printer.doprint(expr)
 
-class TestDynareConvert(unittest.TestCase):
-    def test_get_name(self):
-        cases = [sp.Symbol("x"), TimeAwareSymbol("y", 1), "test"]
-        responses = ["x", "y_tp1", "test"]
-        for case, answer in zip(cases, responses):
-            self.assertEqual(answer, get_name(case))
+    assert out == f"a {op} x - 4"
 
-    def test_build_hash_table_from_strings(self):
-        test_list = ["4", "*", "y", "+", "x", "=", "-4"]
-        var_to_hash, hash_to_var = build_hash_table(test_list)
-        self.assertTrue(all([x in var_to_hash for x in test_list]))
+    expr = sp.parse_expr(
+        f"alpha {op} (beta {op} gamma + 1) - sigma",
+        transformations="all",
+        local_dict=LOCAL_DICT,
+    )
+    out = printer.doprint(expr)
+    assert out == f"alpha {op} (beta {op} gamma + 1) - sigma"
 
-        hashed_string = "_".join([var_to_hash.get(x) for x in test_list])
-        unhashed_string = "_".join(
-            [hash_to_var.get(x) for x in hashed_string.split("_")]
-        )
 
-        self.assertEqual("_".join(test_list), unhashed_string)
+def test_print_power():
+    printer = DynareCodePrinter()
+    expr = sp.parse_expr("a ** 2 - 4", transformations="all")
+    out = printer.doprint(expr)
 
-    def test_build_hash_table_from_symbols(self):
-        test_list = ["4", "*", sp.Symbol("y"), "+", sp.Symbol("x"), "=", "-4"]
-        var_to_hash, hash_to_var = build_hash_table(test_list)
-        self.assertTrue(all([get_name(x) in var_to_hash for x in test_list]))
+    assert out == "a ^ 2 - 4"
 
-        hashed_list = [var_to_hash.get(get_name(x)) for x in test_list]
-        unhashed_list = [hash_to_var.get(x) for x in hashed_list]
+    expr = sp.parse_expr(
+        "alpha ** (beta ** gamma) - sigma", transformations="all", local_dict=LOCAL_DICT
+    )
+    out = printer.doprint(expr)
+    assert out == "alpha ^ (beta ^ gamma) - sigma"
 
-        self.assertEqual([get_name(x) for x in test_list], unhashed_list)
+    expr = sp.parse_expr("x ** 0.5")
+    out = printer.doprint(expr)
+    assert out == "sqrt(x)"
 
-    def test_replace_equations_with_hashes(self):
-        eq_str = "2 * y + 3 * x ^ 2 = -23"
-        tokens = ["y", "x"]
-        var_to_hash, hash_to_var = build_hash_table(tokens)
+    expr = sp.parse_expr("zeta ** (-1)", local_dict=LOCAL_DICT)
+    out = printer.doprint(expr)
+    assert out == "1 / zeta"
 
-        hashed_eq = substitute_equation_from_dict(eq_str, var_to_hash)
-        unhashed_eq = substitute_equation_from_dict(hashed_eq, hash_to_var)
+    expr = sp.parse_expr("(omega * eta) ** (-0.5)", local_dict=LOCAL_DICT)
+    out = printer.doprint(expr)
 
-        self.assertEqual(unhashed_eq, eq_str)
+    # It alphabetizes?
+    assert out == "1 / sqrt(eta * omega)"
 
-    def test_make_var_to_matlab_sub_dict(self):
-        variables = [sp.Symbol("beta"), TimeAwareSymbol("gamma", 0), "lambda"]
 
-        clash_sub_dict = make_var_to_matlab_sub_dict(variables, clash_prefix="param_")
+@pytest.mark.parametrize("name", ["a", "alpha", "x", "beta", "a_name_with_underscores"])
+@pytest.mark.parametrize("time_index", [0, 1, -1, "ss"])
+def test_print_time_aware_symbol(name, time_index):
+    printer = DynareCodePrinter()
+    expr = TimeAwareSymbol(name, time_index)
+    out = printer.doprint(expr)
 
-        self.assertTrue(all([x in clash_sub_dict for x in variables]))
-        self.assertTrue(
-            all([get_name(x).startswith("param_") for x in clash_sub_dict.values()])
-        )
+    if time_index == 0:
+        assert out == name
+    elif time_index == -1:
+        assert out == f"{name}({time_index})"
+    elif time_index == 1:
+        assert out == f"{name}(+{time_index})"
+    elif time_index == "ss":
+        assert out == f"{name}_ss"
 
-        valid_variables = [sp.Symbol("Y"), TimeAwareSymbol("C", 1), "shocks"]
-        clash_sub_dict = make_var_to_matlab_sub_dict(
-            valid_variables, clash_prefix="param_"
-        )
 
-        self.assertTrue(all([get_name(k) == v for k, v in clash_sub_dict.items()]))
+@pytest.fixture()
+def model():
+    return model_from_gcn("tests/Test GCNs/one_block_1_dist.gcn", verbose=False)
 
-    def test_convert_var_timings_to_matlab(self):
-        test_list = ["C_t+1", "C_t", "C_t-1"]
-        answers = ["C(1)", "C", "C(-1)"]
-        converted = convert_var_timings_to_matlab(test_list)
 
-        for x, ans in zip(converted, answers):
-            self.assertEqual(x, ans)
+@pytest.fixture()
+def ss_model():
+    return model_from_gcn("tests/Test GCNs/one_block_1_ss.gcn", verbose=False)
 
-    def test_write_lines_from_list(self):
-        from string import ascii_letters
 
-        file = ""
-        items = np.random.choice(list(ascii_letters), size=1000, replace=True).tolist()
-        file = write_lines_from_list(items, file, line_max=50)
+@pytest.fixture()
+def nk_model():
+    return model_from_gcn("tests/Test GCNs/full_nk.gcn", verbose=False)
 
-        file_lines = file.split("\n")
-        self.assertTrue(all([len(x.strip()) <= 51 for x in file_lines]))
 
-    def test_make_mod_file(self):
-        file_path = os.path.join(
-            ROOT, "Test GCNs/One_Block_Simple_1_w_Distributions.gcn"
-        )
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False)
-        model.solve_model(verbose=False)
+def test_write_variable_declarations(model):
+    out = write_variable_declarations(model)
+    assert out.startswith("var")
 
-        mod_file = make_mod_file(model)
-        self.assertTrue(isinstance(mod_file, str))
+    tokens = out.replace("\n", " ").replace(";", " ").replace(",", " ")
+    tokens = re.sub(" +", " ", tokens).split(" ")
 
+    assert all(x.base_name in tokens for x in model.variables)
 
-if __name__ == "__main__":
-    unittest.main()
+
+def test_write_shock_declarations(model):
+    out = write_shock_declarations(model)
+    assert out.startswith("varexo")
+
+    tokens = out.replace("\n", " ").replace(";", " ").replace(",", " ")
+    tokens = re.sub(" +", " ", tokens).split(" ")
+
+    assert all(x.base_name in tokens for x in model.shocks)
+
+
+def test_write_param_names(model):
+    out = write_param_names(model)
+
+    assert out.startswith("parameters")
+
+    tokens = out.replace("\n", " ").replace(";", " ").replace(",", " ")
+    tokens = re.sub(" +", " ", tokens).split(" ")
+
+    assert all(x.name in tokens for x in model.params)
+
+
+def test_write_parameter_declarations(model):
+    out = write_parameter_declarations(model)
+    lines = [
+        line
+        for line in out.split("\n")
+        if not line.startswith("parameters") and len(line) > 0
+    ]
+    for line in lines:
+        line = line.replace(" ", "").replace(";", "")
+        name, value = line.split("=")
+        assert model.parameters()[name] == float(value)
+
+
+def test_find_ss_variables(nk_model):
+    ss_vars = [x.name for x in find_ss_variables(nk_model)]
+    assert all(x in ss_vars for x in ["pi_ss", "r_G_ss"])
+
+
+def test_write_model_equations(nk_model):
+    out = write_model_equations(nk_model)
+
+    assert out.startswith("model;")
+    assert out.endswith("end;")
+
+    lines = [
+        line
+        for line in out.split("\n")
+        if line not in ["model;", "end;"] and len(line) > 0
+    ]
+    count = 0
+    expect_ss_definition = True
+
+    for line in lines:
+        line = line.replace(" ", "").replace(";", "")
+        if line.startswith("#"):
+            assert "=" in line
+            assert (
+                expect_ss_definition
+            )  # All the ss definitions should be at the beginning and all together
+            line = line.replace(" ", "").replace(";", "")
+            name, value = line.split("=")
+            assert name.endswith("_ss")
+
+        else:
+            expect_ss_definition = False
+            count += 1
+
+    assert len(nk_model.equations) == count
+
+
+def test_write_steady_state(model):
+    out = write_steady_state(model)
+    assert out.startswith("initval;")
+    assert out.endswith("end;\n\nsteady;\nresid;")
+    lines = [
+        line
+        for line in out.split("\n")
+        if line not in ["initval;", "end;", "steady;", "resid;"] and len(line) > 0
+    ]
+    ss_dict = {}
+    for line in lines:
+        name, value = line.replace(";", "").replace(" ", "").split("=")
+        ss_dict[f"{name}_ss"] = float(value)
+
+    np.testing.assert_allclose(
+        model.f_ss_resid(**ss_dict, **model.parameters()),
+        np.zeros(len(ss_dict)),
+        atol=1e-3,
+        rtol=1e-3,
+    )
+
+
+def test_write_analytical_steady_state(ss_model):
+    out = write_steady_state(ss_model)
+    assert out.startswith("steady_state_model;")
+    lines = [
+        line.replace(" ", "").replace(";", "")
+        for line in out.split("\n")
+        if "=" in line and len(line) > 0
+    ]
+    names, exprs = zip(*[line.split("=") for line in lines])
+    n_vars = len(ss_model.variables)
+
+    assert all(x.base_name in names[-n_vars:] for x in ss_model.variables)
+
+
+def test_write_shock_std(model):
+    out = write_shock_std(model)
+    assert out.startswith("shocks;")
+    assert out.endswith("end;")
+    lines = [
+        line
+        for line in out.split("\n")
+        if line not in ["shocks;", "end;"] and len(line) > 0
+    ]
+    assert all(line.startswith("var") for line in lines[::2])
+    assert all(line.startswith("stderr") for line in lines[1::2])
+
+
+@pytest.mark.parametrize("linewidth", [100, 50], ids=["long_lines", "short_lines"])
+def test_make_mod_file(linewidth, nk_model):
+    out = make_mod_file(nk_model, linewidth=linewidth)
+    assert isinstance(out, str)
+
+    lines = out.split("\n")
+
+    # Model equations don't respect the line length -- filter them out
+    eq_start_idx = lines.index("model;")
+    eq_end_idx = lines.index("end;", eq_start_idx)
+
+    lines = lines[:eq_start_idx] + lines[eq_end_idx:]
+    lines = [line for line in lines if "=" not in line]
+
+    assert max([len(x) for x in lines]) <= linewidth
diff --git a/tests/test_estimation.py b/tests/test_estimation.py
deleted file mode 100644
index d93ecb1..0000000
--- a/tests/test_estimation.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-import unittest
-from pathlib import Path
-
-import numpy as np
-
-from gEconpy.classes.model import gEconModel
-from gEconpy.estimation.estimate import build_and_solve, build_Q_and_H
-from gEconpy.estimation.estimation_utilities import extract_sparse_data_from_model
-
-ROOT = Path(__file__).parent.absolute()
-
-
-class TestEstimationHelpers(unittest.TestCase):
-    def setUp(self) -> None:
-        file_path = os.path.join(
-            ROOT, "Test GCNs/One_Block_Simple_1_w_Steady_State.gcn"
-        )
-        self.model = gEconModel(file_path, verbose=False)
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(verbose=False)
-
-    def test_build_and_solve(self):
-        param_dict = self.model.free_param_dict
-        to_estimate = list(param_dict.to_string().keys())
-        sparse_data = extract_sparse_data_from_model(
-            self.model, params_to_estimate=to_estimate
-        )
-
-        T, R, success = build_and_solve(param_dict, sparse_data, to_estimate)
-
-        self.assertTrue(np.allclose(T, self.model.T.values))
-        self.assertTrue(np.allclose(R, self.model.R.values))
-
-    def test_build_Q_and_R(self):
-        shock_names = [x.base_name for x in self.model.shocks]
-        state_sigmas = dict(zip(shock_names, [0.1] * self.model.n_shocks))
-        observed_vars = list(self.model.steady_state_dict.keys())
-        n = len(observed_vars)
-
-        Q, H = build_Q_and_H(
-            state_sigmas,
-            shock_variables=shock_names,
-            obs_variables=observed_vars,
-            obs_sigmas=None,
-        )
-
-        Q_result = np.array([[0.1]])
-        H_result = np.zeros((n, n))
-
-        self.assertTrue(np.allclose(Q, Q_result))
-        self.assertTrue(np.allclose(H, H_result))
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/test_gensys.py b/tests/test_gensys.py
index f84c1fb..b03c32a 100644
--- a/tests/test_gensys.py
+++ b/tests/test_gensys.py
@@ -3,6 +3,7 @@
 import numpy as np
 
 from gEconpy.solvers.gensys import (
+    build_u_v_d,
     determine_n_unstable,
     qzdiv,
     qzswitch,
@@ -19,7 +20,7 @@ def setUp(self):
 
         self.div = 1.01
 
-        self.A = np.array(
+        A = np.array(
             [
                 [
                     -2.0123 - 0.5490j,
@@ -59,7 +60,7 @@ def setUp(self):
             ]
         )
 
-        self.B = np.array(
+        B = np.array(
             [
                 [
                     2.2056 + 0.0000j,
@@ -99,7 +100,7 @@ def setUp(self):
             ]
         )
 
-        self.Q = np.array(
+        Q = np.array(
             [
                 [
                     -0.1666 + 0.0735j,
@@ -139,7 +140,7 @@ def setUp(self):
             ]
         )
 
-        self.Z = np.array(
+        Z = np.array(
             [
                 [
                     -0.1455 + 0.1400j,
@@ -179,6 +180,11 @@ def setUp(self):
             ]
         )
 
+        self.A = np.asfortranarray(A)
+        self.B = np.asfortranarray(B)
+        self.Q = np.asfortranarray(Q)
+        self.Z = np.asfortranarray(Z)
+
     def unpack_matrices(self):
         return self.A, self.B, self.Q, self.Z
 
@@ -346,14 +352,22 @@ def test_qzdiv(self):
             ]
         )
 
-        self.assertEqual(np.allclose(A, ans_A), True)
-        self.assertEqual(np.allclose(B, ans_B), True)
-        self.assertEqual(np.allclose(Q, ans_Q), True)
-        self.assertEqual(np.allclose(Z, ans_Z), True)
+        np.testing.assert_allclose(
+            A, ans_A, err_msg="A not equal to requested precision"
+        )
+        np.testing.assert_allclose(
+            B, ans_B, err_msg="B not equal to requested precision"
+        )
+        np.testing.assert_allclose(
+            Q, ans_Q, err_msg="Q not equal to requested precision"
+        )
+        np.testing.assert_allclose(
+            Z, ans_Z, err_msg="Z not equal to requested precision"
+        )
 
     def test_qzswitch(self):
         # TODO: Find matrices that test conditions (1) and (2) in qzswitch (most will only hit condition 3)
-        A, B, Q, Z = self.unpack_matrices()
+        A, B, Q, Z = list(map(np.asfortranarray, self.unpack_matrices()))
         A, B, Q, Z = qzswitch(2, A, B, Q, Z)
 
         ans_A = np.array(
@@ -519,15 +533,23 @@ def test_qzswitch(self):
         # Riddle me this: qzswitch tests only pass at 3 decimal places of precision, but qzdiv tests, which call
         # qzswitch multiple times, pass at 4!
 
-        self.assertEqual(np.allclose(A, ans_A, atol=1e-3), True)
-        self.assertEqual(np.allclose(B, ans_B, atol=1e-3), True)
-        self.assertEqual(np.allclose(Q, ans_Q, atol=1e-3), True)
-        self.assertEqual(np.allclose(Z, ans_Z, atol=1e-3), True)
+        np.testing.assert_allclose(
+            A, ans_A, atol=1e-3, err_msg="A not close to requested precision"
+        )
+        np.testing.assert_allclose(
+            B, ans_B, atol=1e-3, err_msg="B not close to requested precision"
+        )
+        np.testing.assert_allclose(
+            Q, ans_Q, atol=1e-3, err_msg="Q not close to requested precision"
+        )
+        np.testing.assert_allclose(
+            Z, ans_Z, atol=1e-3, err_msg="Z not close to requested precision"
+        )
 
     def test_determine_n_unstable(self):
         A, B, _, _ = self.unpack_matrices()
 
-        div, n_unstable, zxz = determine_n_unstable(A, B, self.div, realsmall=-6)
+        div, n_unstable, zxz = determine_n_unstable(A, B, self.div, realsmall=1e-6)
 
         self.assertEqual(div, 1.01)
         self.assertEqual(n_unstable, 5)
diff --git a/tests/test_kalman_filter.py b/tests/test_kalman_filter.py
deleted file mode 100644
index 595340a..0000000
--- a/tests/test_kalman_filter.py
+++ /dev/null
@@ -1,217 +0,0 @@
-import os
-import unittest
-from pathlib import Path
-
-import numpy as np
-
-from gEconpy.classes.model import gEconModel
-from gEconpy.estimation.estimation_utilities import (
-    build_system_matrices,
-    check_bk_condition,
-    extract_sparse_data_from_model,
-)
-from gEconpy.estimation.kalman_filter import kalman_filter, univariate_kalman_filter
-
-ROOT = Path(__file__).parent.absolute()
-
-
-class BasicFunctionalityTests(unittest.TestCase):
-    def setUp(self):
-        file_path = os.path.join(
-            ROOT, "Test GCNs/One_Block_Simple_1_w_Steady_State.gcn"
-        )
-        self.model = gEconModel(file_path, verbose=False)
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(verbose=False)
-
-    def test_extract_system_matrics(self):
-        param_dict = self.model.free_param_dict
-
-        sparse_data = extract_sparse_data_from_model(
-            self.model, params_to_estimate=["theta"]
-        )
-        A, B, C, D = build_system_matrices(
-            param_dict, sparse_data, vars_to_estimate=["theta"]
-        )
-
-        system = self.model.build_perturbation_matrices(
-            np.fromiter(
-                (self.model.free_param_dict | self.model.calib_param_dict).values(),
-                dtype="float",
-            ),
-            np.fromiter(self.model.steady_state_dict.values(), dtype="float"),
-        )
-
-        self.assertTrue(np.allclose(A, system[0]))
-        self.assertTrue(np.allclose(B, system[1]))
-        self.assertTrue(np.allclose(C, system[2]))
-        self.assertTrue(np.allclose(D, system[3]))
-
-        self.assertTrue(check_bk_condition(A, B, C, tol=1e-8))
-
-
-class KalmanFilterTest(unittest.TestCase):
-    def test_likelihood(self):
-        # Test against an AR(1) model with rho=0.8
-        # Expected value comes from statsmodels
-
-        expected = np.array(
-            [
-                -1.42976416,
-                -1.41893853,
-                -1.63893853,
-                -1.89893853,
-                -2.19893853,
-                -2.53893853,
-                -2.91893853,
-                -3.33893853,
-                -3.79893853,
-                -4.29893853,
-            ]
-        )
-        data = np.arange(10, dtype="float64")[:, None]
-
-        a0 = np.array([[0.0]])
-        P0 = np.array([[1000000.0]])
-        T = np.array([[0.8]])
-        Z = np.array([[1.0]])
-        R = np.array([[1.0]])
-        H = np.array([[0.0]])
-        Q = np.array([[1.0]])
-
-        *_, ll_obs = kalman_filter(data, T, Z, R, H, Q, a0, P0)
-
-        # The first observation is different from statsmodels because they apply some adjustment for the diffuse
-        # initialization.
-        self.assertTrue(np.allclose(expected[1:], ll_obs[1:]))
-
-    def test_likelihood_with_missing(self):
-        # Test against an AR(1) model with rho=0.8
-        # Expected value comes from statsmodels
-
-        expected = np.array(
-            [
-                -1.42976416,
-                -1.41893853,
-                -1.63893853,
-                -1.89893853,
-                -2.19893853,
-                0.0,
-                -4.77409153,
-                -3.33893853,
-                -3.79893853,
-                -4.29893853,
-            ]
-        )
-
-        data = np.arange(10, dtype="float64")[:, None]
-        data[5] = np.nan
-
-        a0 = np.array([[0.0]])
-        P0 = np.array([[1000000.0]])
-        T = np.array([[0.8]])
-        Z = np.array([[1.0]])
-        R = np.array([[1.0]])
-        H = np.array([[0.0]])
-        Q = np.array([[1.0]])
-
-        *_, ll_obs = kalman_filter(data, T, Z, R, H, Q, a0, P0)
-
-        # The first observation is different from statsmodels because they apply some adjustment for the diffuse
-        # initialization.
-        self.assertTrue(np.allclose(expected[1:], ll_obs[1:]))
-
-
-class UnivariateKalmanFilterTest(unittest.TestCase):
-    def test_likelihood(self):
-        # Test against an AR(1) model with rho=0.8
-        # Expected value comes from statsmodels
-
-        expected = np.array(
-            [
-                -1.42976416,
-                -1.41893853,
-                -1.63893853,
-                -1.89893853,
-                -2.19893853,
-                -2.53893853,
-                -2.91893853,
-                -3.33893853,
-                -3.79893853,
-                -4.29893853,
-            ]
-        )
-        data = np.arange(10, dtype="float64")[:, None]
-
-        a0 = np.array([[0.0]])
-        P0 = np.array([[1000000.0]])
-        T = np.array([[0.8]])
-        Z = np.array([[1.0]])
-        R = np.array([[1.0]])
-        H = np.array([[0.0]])
-        Q = np.array([[1.0]])
-
-        *_, ll_obs = univariate_kalman_filter(data, T, Z, R, H, Q, a0, P0)
-
-        # The first observation is different from statsmodels because they apply some adjustment for the diffuse
-        # initialization.
-        self.assertTrue(np.allclose(expected[1:], ll_obs[1:]))
-
-    def test_likelihood_with_missing(self):
-        # Test against an AR(1) model with rho=0.8
-        # Expected value comes from statsmodels
-
-        expected = np.array(
-            [
-                -1.42976416,
-                -1.41893853,
-                -1.63893853,
-                -1.89893853,
-                -2.19893853,
-                0.0,
-                -4.77409153,
-                -3.33893853,
-                -3.79893853,
-                -4.29893853,
-            ]
-        )
-
-        data = np.arange(10, dtype="float64")[:, None]
-        data[5] = np.nan
-
-        a0 = np.array([[0.0]])
-        P0 = np.array([[1000000.0]])
-        T = np.array([[0.8]])
-        Z = np.array([[1.0]])
-        R = np.array([[1.0]])
-        H = np.array([[0.0]])
-        Q = np.array([[1.0]])
-
-        *_, ll_obs = univariate_kalman_filter(data, T, Z, R, H, Q, a0, P0)
-
-        # The first observation is different from statsmodels because they apply some adjustment for the diffuse
-        # initialization.
-        self.assertTrue(np.allclose(expected[1:], ll_obs[1:]))
-
-
-class TestModelEstimation(unittest.TestCase):
-    def setUp(self):
-        file_path = os.path.join(
-            ROOT, "Test GCNs/One_Block_Simple_1_w_Steady_State.gcn"
-        )
-        self.model = gEconModel(file_path, verbose=False)
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(verbose=False)
-
-        self.data = (
-            self.model.simulate(simulation_length=100, n_simulations=1)
-            .xs(axis=1, level=1, key=0)
-            .T
-        )
-
-    def filter_random_sample(self):
-        pass
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/test_model.py b/tests/test_model.py
index 2aadb7e..dbb0d88 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -1,1122 +1,1243 @@
 import os
 import re
 import unittest
-from pathlib import Path
+
+from importlib.util import find_spec
 from unittest import mock
 
-import arviz as az
+import numdifftools as nd
 import numpy as np
 import pandas as pd
-import sympy as sp
+import pytest
+import xarray as xr
+
 from numpy.testing import assert_allclose
 
-from gEconpy.classes.containers import SymbolDictionary
-from gEconpy.classes.model import gEconModel
-from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.exceptions.exceptions import GensysFailedException
-from gEconpy.parser.constants import DEFAULT_ASSUMPTIONS
-from gEconpy.sampling import (
-    simulate_trajectories_from_posterior,
+from gEconpy.exceptions import GensysFailedException, OrphanParameterError
+from gEconpy.model.build import model_from_gcn
+from gEconpy.model.compile import BACKENDS
+from gEconpy.model.model import (
+    autocorrelation_matrix,
+    build_Q_matrix,
+    impulse_response_function,
+    matrix_to_dataframe,
+    scipy_wrapper,
+    simulate,
+    stationary_covariance_matrix,
+    summarize_perturbation_solution,
+)
+from gEconpy.model.perturbation import (
+    check_bk_condition,
+    statespace_to_gEcon_representation,
 )
-from gEconpy.shared.utilities import string_keys_to_sympy
+from tests.utilities.expected_matrices import expected_linearization_result
+from tests.utilities.load_dynare import load_dynare_outputs
+from tests.utilities.shared_fixtures import load_and_cache_model
 
-ROOT = Path(__file__).parent.absolute()
+JAX_INSTALLED = find_spec("jax") is not None
 
 
-class ModelErrorTests(unittest.TestCase):
-    def setUp(self):
-        self.GCN_file = """
-            block HOUSEHOLD
-            {
-                definitions
+@pytest.fixture
+def gcn_file_1():
+    GCN_file = """
+                block HOUSEHOLD
                 {
-                    u[] = log(C[]);
-                };
+                    definitions
+                    {
+                        u[] = log(C[]);
+                    };
 
-                objective
-                {
-                    U[] = u[] + beta * E[][U[1]];
-                };
+                    objective
+                    {
+                        U[] = u[] + beta * E[][U[1]];
+                    };
 
-                controls
-                {
-                    C[], K[];
-                };
+                    controls
+                    {
+                        C[], K[];
+                    };
 
-                constraints
-                {
-                    Y[] = K[-1] ^ alpha;
-                    C[] = r[] * K[-1];
-                    K[] = (1 - delta) * K[-1];
-                    X[] = Y[] + C[];
-                    Z[] = 3;
-                };
+                    constraints
+                    {
+                        Y[] = K[-1] ^ alpha;
+                        C[] = r[] * K[-1];
+                        K[] = (1 - delta) * K[-1];
+                        X[] = Y[] + C[];
+                        Z[] = 3;
+                    };
 
-                calibration
-                {
-                    alpha = 0.33;
-                    beta = 0.99;
-                    delta = 0.035;
+                    calibration
+                    {
+                        alpha = 0.33;
+                        beta = 0.99;
+                        delta = 0.035;
+                    };
                 };
-            };
-            """
-
-    def test_build_warns_if_model_not_defined(self):
-        expected_warnings = [
-            "Simplification via try_reduce was requested but not possible because the system is not well defined.",
-            "Removal of constant variables was requested but not possible because the system is not well defined.",
-            "The model does not appear correctly specified, there are 8 equations but "
-            "11 variables. It will not be possible to solve this model. Please check the "
-            "specification using available diagnostic tools, and check the GCN file for typos.",
-        ]
-
-        with unittest.mock.patch(
-            "builtins.open",
-            new=unittest.mock.mock_open(read_data=self.GCN_file),
-            create=True,
-        ):
-            with self.assertWarns(UserWarning) as warnings:
-                gEconModel(
-                    "", verbose=False, simplify_tryreduce=True, simplify_constants=True
-                )
-
-            for w in warnings.warnings:
-                warning_msg = str(w.message)
-                self.assertIn(warning_msg, expected_warnings)
-
-    def test_invalid_solver_raises(self):
-        file_path = os.path.join(ROOT, "Test GCNs/One_Block_Simple_2.gcn")
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False)
-
-        with self.assertRaises(NotImplementedError):
-            model.solve_model(solver="invalid_solver")
-
-    def test_bad_failure_argument_raises(self):
-        file_path = os.path.join(ROOT, "Test GCNs/pert_fails.gcn")
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False, model_is_linear=True)
-
-        with self.assertRaises(ValueError):
-            model.solve_model(solver="gensys", on_failure="raise", model_is_linear=True)
-
-    def test_bad_argument_to_bk_condition_raises(self):
-        file_path = os.path.join(ROOT, "Test GCNs/One_Block_Simple_2.gcn")
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False)
-        model.solve_model(verbose=False)
-
-        with self.assertRaises(ValueError):
-            model.check_bk_condition(return_value="invalid_argument")
-
-    def test_gensys_fails_to_solve(self):
-        file_path = os.path.join(ROOT, "Test GCNs/pert_fails.gcn")
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False, model_is_linear=True)
-
-        with self.assertRaises(GensysFailedException):
-            model.solve_model(
-                solver="gensys", on_failure="error", model_is_linear=True, verbose=False
+                """
+    return GCN_file
+
+
+expected_warnings = [
+    "Simplification via a tryreduce block was requested but not possible because the system is not well defined.",
+    "Removal of constant variables was requested but not possible because the system is not well defined.",
+    "The model does not appear correctly specified, there are 8 equations but 12 variables. It will not be possible to "
+    "solve this model. Please check the specification using available diagnostic tools, and check the GCN file for "
+    "typos.",
+]
+
+
+@pytest.mark.parametrize(
+    ["simplify_tryreduce", "simplify_constants", "expected_warning"],
+    [
+        (True, False, expected_warnings[0]),
+        (False, True, expected_warnings[1]),
+        (False, False, expected_warnings[2]),
+    ],
+    ids=["tryreduce", "constants", "no_simplify"],
+)
+def test_build_warns_if_model_not_defined(
+    gcn_file_1, simplify_tryreduce, simplify_constants, expected_warning
+):
+    with unittest.mock.patch(
+        "builtins.open",
+        new=unittest.mock.mock_open(read_data=gcn_file_1),
+        create=True,
+    ):
+        with pytest.warns(UserWarning, match=expected_warning):
+            model_from_gcn(
+                gcn_file_1,
+                simplify_constants=simplify_constants,
+                simplify_tryreduce=simplify_tryreduce,
+                verbose=not (simplify_tryreduce or simplify_constants),
             )
 
-    @mock.patch("builtins.print")
-    def test_outputs_after_gensys_failure(self, mock_print):
-        file_path = os.path.join(ROOT, "Test GCNs/pert_fails.gcn")
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False, model_is_linear=True)
-        model.solve_model(
-            solver="gensys", on_failure="ignore", model_is_linear=True, verbose=True
-        )
 
-        gensys_message = mock_print.call_args.args[0]
-        self.assertEqual(gensys_message, "Solution exists, but is not unique.")
-
-        P, Q, R, S = model.P, model.Q, model.R, model.S
-        for X, name in zip([P, Q, R, S], ["P", "Q", "R", "S"]):
-            self.assertIsNone(X, msg=name)
-
-    @mock.patch("builtins.print")
-    def test_outputs_after_pert_success(self, mock_print):
-        file_path = os.path.join(ROOT, "Test GCNs/RBC_Linearized.gcn")
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False, model_is_linear=True)
-        model.solve_model(solver="gensys", verbose=True, model_is_linear=True)
-
-        # TODO: Can i get more print calls without having to parse through call_args_list?
-        result_messages = mock_print.call_args.args[0]
-        self.assertEqual(result_messages, "Norm of stochastic part:    0.000000000")
-
-    def test_compute_stationary_covariance_warns_if_using_default(self):
-        file_path = os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn")
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False)
-        model.solve_model(solver="gensys", verbose=False)
-
-        with self.assertWarns(UserWarning):
-            model.compute_stationary_covariance_matrix()
-
-    def test_sample_priors_fails_without_priors(self):
-        file_path = os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn")
-        model = gEconModel(file_path, verbose=False)
-        model.steady_state(verbose=False)
-        model.solve_model(solver="gensys", verbose=False)
-
-        with self.assertRaises(ValueError):
-            model.sample_param_dict_from_prior()
-
-    def test_missing_parameter_definition_raises(self):
-        GCN_file = """
-                    block HOUSEHOLD
+def test_missing_parameters_raises():
+    GCN_file = """
+                block HOUSEHOLD
+                {
+                    definitions
                     {
-                        definitions
-                        {
-                            u[] = log(C[]);
-                        };
-
-                        objective
-                        {
-                            U[] = u[] + beta * E[][U[1]];
-                        };
-
-                        controls
-                        {
-                            C[], K[], K[-1], Y[];
-                        };
-
-                        constraints
-                        {
-                            Y[] = K[-1] ^ alpha;
-                            Y[] = r[] * K[-1];
-                            K[] = (1 - delta) * K[-1];
-
-                        };
-
-                        calibration
-                        {
-                            K[ss] / Y[ss] = 0.33 -> alpha;
-                            delta = 0.035;
-                        };
+                        u[] = log(C[]);
                     };
-                    """
 
-        with unittest.mock.patch(
-            "builtins.open",
-            new=unittest.mock.mock_open(read_data=GCN_file),
-            create=True,
-        ):
-            with self.assertRaises(ValueError) as error:
-                gEconModel(
-                    "",
-                    verbose=False,
-                    simplify_tryreduce=False,
-                    simplify_constants=False,
-                )
-            msg = str(error.exception)
-
-        self.assertEqual(
-            msg,
-            "The following parameters were found among model equations, but were not found among "
-            "defined defined or calibrated parameters: beta.\n Verify that these "
-            "parameters have been defined in a calibration block somewhere in your GCN file.",
-        )
-
-
-class ModelClassTestsOne(unittest.TestCase):
-    def setUp(self):
-        file_path = os.path.join(ROOT, "Test GCNs/One_Block_Simple_2.gcn")
-        self.model = gEconModel(file_path, verbose=False)
-
-    @unittest.mock.patch("builtins.print")
-    def test_build_report(self, mock_print):
-        self.model.build_report(reduced_vars=["A"], singletons=["B"], verbose=True)
-
-        expected_output = """
-            Model Building Complete.
-            Found:
-                9 equations
-                9 variables
-                The following variables were eliminated at user request:
-                    A
-                The following "variables" were defined as constants and have been substituted away:
-                    B
-                1 stochastic shock
-                    0 / 1 has a defined prior.
-                5 parameters
-                    0 / 5 has a defined prior.
-                1 calibrating equation
-                1 parameter to calibrate
-                Model appears well defined and ready to proceed to solving."""
-        report = mock_print.call_args.args[0]
-
-        simple_output = re.sub("[\n\t]", " ", expected_output)
-        simple_output = re.sub(" +", " ", simple_output)
-
-        simple_report = re.sub("[\n\t]", " ", report)
-        simple_report = re.sub(" +", " ", simple_report)
-        self.assertEqual(simple_output.strip(), simple_report.strip())
-
-    def test_model_options(self):
-        self.assertEqual(
-            self.model.options, {"output logfile": False, "output LaTeX": False}
-        )
+                    objective
+                    {
+                        U[] = u[] + beta * E[][U[1]];
+                    };
 
-    def test_reduce_vars_saved(self):
-        self.assertEqual(
-            self.model.try_reduce_vars,
-            [TimeAwareSymbol("C", 0, **self.model.assumptions["C"])],
-        )
+                    controls
+                    {
+                        C[], K[], K[-1], Y[];
+                    };
 
-    def test_model_file_loading(self):
-        block_names = ["HOUSEHOLD"]
-        result = [block_name for block_name in self.model.blocks.keys()]
-        self.assertEqual(block_names, result)
-
-        param_dict = {
-            "theta": 0.357,
-            "beta": 0.99,
-            "delta": 0.02,
-            "tau": 2,
-            "rho": 0.95,
-        }
+                    constraints
+                    {
+                        Y[] = K[-1] ^ alpha;
+                        Y[] = r[] * K[-1];
+                        K[] = (1 - delta) * K[-1];
 
-        self.assertEqual(
-            all([x in param_dict.keys() for x in self.model.free_param_dict.keys()]),
-            True,
-        )
-        self.assertEqual(
-            all(
-                [
-                    self.model.free_param_dict[x] == param_dict[x]
-                    for x in param_dict.keys()
-                ]
-            ),
-            True,
-        )
-        self.assertEqual(
-            self.model.params_to_calibrate,
-            [sp.Symbol("alpha", **self.model.assumptions["alpha"])],
-        )
+                    };
 
-    def test_conflicting_assumptions_are_removed(self):
-        with self.assertWarns(UserWarning):
-            model = gEconModel(
-                os.path.join(ROOT, "Test GCNs/conflicting_assumptions.gcn"),
+                    calibration
+                    {
+                        K[ss] / Y[ss] = 0.33 -> alpha;
+                        delta = 0.035;
+                    };
+                };
+                """
+
+    with unittest.mock.patch(
+        "builtins.open",
+        new=unittest.mock.mock_open(read_data=GCN_file),
+        create=True,
+    ):
+        with pytest.raises(
+            OrphanParameterError,
+            match=r"The following parameter was found among model equations but did not appear in "
+            r"any calibration block: beta",
+        ):
+            model_from_gcn(
+                GCN_file,
                 verbose=False,
+                simplify_tryreduce=False,
+                simplify_constants=False,
             )
 
-        self.assertTrue("real" not in model.assumptions["TC"].keys())
-        self.assertTrue("imaginary" in model.assumptions["TC"].keys())
-        self.assertTrue(model.assumptions["TC"]["imaginary"])
-
-    def test_solve_model_gensys(self):
-        self.setUp()
-        self.model.steady_state(verbose=False)
-        self.assertEqual(self.model.steady_state_solved, True)
-        self.model.solve_model(verbose=False, solver="gensys")
-        self.assertEqual(self.model.perturbation_solved, True)
-
-        # Values from R gEcon solution
-        P = np.array([[0.950, 0.0000], [0.2710273, 0.8916969]])
-
-        Q = np.array([[1.000], [0.2852917]])
-
-        # TODO: Bug? When the SS value is negative, the sign of the S and R matrix entries are flipped relative to
-        #   those of gEcon (row 4 -- Utility). This code flips the sign on my values to make the comparison.
-        #   Check Dynare.
-        R = np.array(
-            [
-                [0.70641931, 0.162459910],
-                [13.55135517, -4.415155354],
-                [0.42838971, -0.152667442],
-                [-0.06008706, -0.009473984],
-                [1.36634369, -0.072720705],
-                [-0.80973441, -0.273514035],
-                [-0.80973441, -0.273514035],
-            ]
-        )
-
-        S = np.array(
-            [
-                [0.74359928],
-                [14.26458439],
-                [0.45093654],
-                [-0.06324954],
-                [1.43825652],
-                [-0.85235201],
-                [-0.85235201],
-            ]
-        )
-
-        ss_df = pd.Series(string_keys_to_sympy(self.model.steady_state_dict))
-        ss_df.index = list(map(lambda x: x.exit_ss().name, ss_df.index))
-        # ss_df = ss_df.reindex(self.model.S.index)
-        # neg_ss_mask = ss_df < 0
-
-        A, _, _, _ = self.model.build_perturbation_matrices(
-            np.fromiter(
-                (self.model.free_param_dict | self.model.calib_param_dict).values(),
-                dtype="float",
-            ),
-            np.fromiter(self.model.steady_state_dict.values(), dtype="float"),
-        )
-
-        (
-            _,
-            variables,
-            _,
-        ) = self.model.perturbation_solver.make_all_variable_time_combinations()
-
-        gEcon_matrices = (
-            self.model.perturbation_solver.statespace_to_gEcon_representation(
-                A, self.model.T.values, self.model.R.values, variables, 1e-7
-            )
-        )
-        model_P, model_Q, model_R, model_S, *_ = gEcon_matrices
-
-        assert_allclose(model_P, P, equal_nan=True, err_msg="P", rtol=1e-5)
-        assert_allclose(model_Q, Q, equal_nan=True, err_msg="Q", rtol=1e-5)
-        assert_allclose(model_R, R, equal_nan=True, err_msg="R", rtol=1e-5)
-        assert_allclose(model_S, S, equal_nan=True, err_msg="S", rtol=1e-5)
-
-    def test_solve_model_cycle_reduction(self):
-        self.setUp()
-        self.model.steady_state(verbose=True)
-        self.assertEqual(self.model.steady_state_solved, True)
-        self.model.solve_model(verbose=True, solver="cycle_reduction")
-        self.assertEqual(self.model.perturbation_solved, True)
-
-        # Values from R gEcon solution
-        P = np.array([[0.950, 0.0000], [0.2710273, 0.8916969]])
-
-        Q = np.array([[1.000], [0.2852917]])
-
-        # TODO: Check dynare outputs for sign flip
-        R = np.array(
-            [
-                [0.70641931, 0.162459910],
-                [13.55135517, -4.415155354],
-                [0.42838971, -0.152667442],
-                [-0.06008706, -0.009473984],
-                [1.36634369, -0.072720705],
-                [-0.80973441, -0.273514035],
-                [-0.80973441, -0.273514035],
-            ]
-        )
-
-        S = np.array(
-            [
-                [0.74359928],
-                [14.26458439],
-                [0.45093654],
-                [-0.06324954],
-                [1.43825652],
-                [-0.85235201],
-                [-0.85235201],
-            ]
-        )
-
-        A, _, _, _ = self.model.build_perturbation_matrices(
-            np.fromiter(
-                (self.model.free_param_dict | self.model.calib_param_dict).values(),
-                dtype="float",
-            ),
-            np.fromiter(self.model.steady_state_dict.values(), dtype="float"),
-        )
 
+simple_vars = ["L", "K", "A", "Y", "I", "C", "q", "U", "lambda", "q"]
+simple_params = ["alpha", "theta", "beta", "delta", "tau", "rho"]
+simple_shocks = ["epsilon"]
+
+open_vars = [
+    "A",
+    "IIP",
+    "r",
+    "r_given",
+    "KtoN",
+    "N",
+    "K",
+    "C",
+    "U",
+    "Y",
+    "I",
+    "TB",
+    "TBtoY",
+    "CA",
+    "lambda",
+]
+open_params = [
+    "beta",
+    "delta",
+    "gamma",
+    "omega",
+    "psi2",
+    "psi",
+    "alpha",
+    "rstar",
+    "IIPbar",
+    "rho_A",
+]
+open_shocks = ["epsilon_A"]
+
+nk_vars = [
+    "shock_technology",
+    "shock_preference",
+    "pi",
+    "pi_star",
+    "pi_obj",
+    "B",
+    "r",
+    "r_G",
+    "mc",
+    "w",
+    "w_star",
+    "Y",
+    "C",
+    "lambda",
+    "q",
+    "I",
+    "K",
+    "L",
+    "U",
+    "TC",
+    "Div",
+    "LHS",
+    "RHS",
+    "LHS_w",
+    "RHS_w",
+]
+nk_params = [
+    "delta",
+    "beta",
+    "sigma_C",
+    "sigma_L",
+    "gamma_I",
+    "phi_H",
+    "psi_w",
+    "eta_w",
+    "alpha",
+    "rho_technology",
+    "rho_preference",
+    "psi_p",
+    "eta_p",
+    "gamma_R",
+    "gamma_pi",
+    "gamma_Y",
+    "phi_pi_obj",
+    "rho_pi_dot",
+]
+nk_shocks = ["epsilon_R", "epsilon_pi", "epsilon_Y", "epsilon_preference"]
+
+
+@pytest.mark.parametrize(
+    "gcn_path, expected_variables, expected_params, expected_shocks",
+    [
         (
-            _,
-            variables,
-            _,
-        ) = self.model.perturbation_solver.make_all_variable_time_combinations()
-
-        gEcon_matrices = (
-            self.model.perturbation_solver.statespace_to_gEcon_representation(
-                A, self.model.T.values, self.model.R.values, variables, 1e-7
-            )
-        )
-        model_P, model_Q, model_R, model_S, *_ = gEcon_matrices
-
-        self.assertEqual(np.allclose(model_P, P), True, msg="P")
-        self.assertEqual(np.allclose(model_Q, Q), True, msg="Q")
-        self.assertEqual(np.allclose(model_R, R), True, msg="R")
-        self.assertEqual(np.allclose(model_S, S), True, msg="S")
+            "one_block_1_ss.gcn",
+            simple_vars,
+            simple_params,
+            simple_shocks,
+        ),
+        ("open_rbc.gcn", open_vars, open_params, open_shocks),
+        ("full_nk.gcn", nk_vars, nk_params, nk_shocks),
+    ],
+)
+def test_variables_parsed(
+    gcn_path, expected_variables, expected_params, expected_shocks
+):
+    file_path = os.path.join("tests/Test GCNs", gcn_path)
+    model = model_from_gcn(
+        file_path,
+        verbose=False,
+        backend="numpy",
+        mode="FAST_COMPILE",
+        simplify_constants=False,
+        simplify_tryreduce=False,
+    )
+
+    model_vars = [v.base_name for v in model.variables]
+    model_params = [
+        p.name
+        for p in model.params + model.calibrated_params + model.deterministic_params
+    ]
+    model_shocks = [s.base_name for s in model.shocks]
+
+    assert (
+        set(model_vars) - set(expected_variables) == set()
+        and set(expected_variables) - set(model_vars) == set()
+    )
+    assert (
+        set(model_params) - set(expected_params) == set()
+        and set(expected_params) - set(model_params) == set()
+    )
+    assert (
+        set(model_shocks) - set(expected_shocks) == set()
+        and set(expected_shocks) - set(model_shocks) == set()
+    )
+
+
+@pytest.mark.parametrize(
+    "gcn_path, name",
+    [
+        ("one_block_1_dist.gcn", "one_block_prior"),
+        ("one_block_1_ss.gcn", "one_block_ss"),
+        ("full_nk.gcn", "full_nk"),
+    ],
+    ids=["one_block_prior", "one_block_ss", "full_nk"],
+)
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+def test_model_parameters(gcn_path: str, name: str, backend: BACKENDS):
+    model = load_and_cache_model(gcn_path, backend, use_jax=JAX_INSTALLED)
 
-    def test_solvers_agree(self):
-        self.setUp()
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(solver="gensys", verbose=False)
-        Tg, Rg = self.model.T, self.model.R
+    # Test default parameters
+    params = model.parameters()
 
-        self.setUp()
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(solver="cycle_reduction", verbose=False)
-        Tc, Rc = self.model.T, self.model.R
+    assert all([params[k] == model._default_params[k] for k in model._default_params])
+    assert all(isinstance(v, float) for v in params.values())
 
-        assert_allclose(
-            Tg.round(5).values,
-            Tc.round(5).values,
-            rtol=1e-5,
-            equal_nan=True,
-            err_msg="T",
-        )
-        assert_allclose(
-            Rg.round(5).values,
-            Rc.round(5).values,
-            rtol=1e-5,
-            equal_nan=True,
-            err_msg="R",
-        )
+    # Test parameter update
+    old_params = model._default_params.copy()
+    params = model.parameters(beta=0.5)
+    assert params["beta"] == 0.5
+    assert model._default_params["beta"] == old_params["beta"]
 
-    def test_blanchard_kahn_conditions(self):
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(verbose=False)
-        bk_cond = self.model.check_bk_condition(return_value="bool", verbose=True)
-        self.assertTrue(bk_cond)
 
-        bk_df = self.model.check_bk_condition(return_value="df")
-        self.assertTrue(isinstance(bk_df, pd.DataFrame))
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+def test_deterministic_model_parameters(backend: BACKENDS):
+    model = load_and_cache_model("one_block_2.gcn", backend, use_jax=JAX_INSTALLED)
+    params = model.parameters()
 
-    def test_compute_autocorrelation_matrix(self):
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(verbose=False)
+    # Test numeric expression in calibration block
+    assert_allclose(params["beta"], 1 / 1.01)
 
-        n_lags = 10
-        acorr_df = self.model.compute_autocorrelation_matrix(
-            shock_dict={"epsilon_A": 0.01}, n_lags=n_lags
-        )
+    # Test deterministic relationship
+    params = model.parameters(theta=0.9)
+    assert params["theta"] == 0.9
+    assert_allclose(params["zeta"], -np.log(0.9))
 
-        self.assertTrue(isinstance(acorr_df, pd.DataFrame))
-        self.assertEqual(acorr_df.shape[0], self.model.n_variables)
-        self.assertEqual(acorr_df.shape[1], n_lags)
 
-    def test_compute_stationary_covariance(self):
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(verbose=False)
+@pytest.mark.parametrize(
+    "gcn_path",
+    ["one_block_1_ss.gcn", "open_rbc.gcn", "full_nk.gcn"],
+    ids=["one_block_prior", "one_block_ss", "full_nk"],
+)
+def test_all_backends_agree_on_parameters(gcn_path):
+    models = [
+        load_and_cache_model(gcn_path, backend, use_jax=JAX_INSTALLED)
+        for backend in ["numpy", "numba", "pytensor"]
+    ]
+    params = [np.r_[list(model.parameters().values())] for model in models]
+
+    for i in range(3):
+        for j in range(i):
+            assert_allclose(params[i], params[j])
+
+
+@pytest.mark.parametrize(
+    "gcn_path",
+    ["one_block_1_ss.gcn", "open_rbc.gcn", "full_nk.gcn"],
+    ids=["one_block_prior", "one_block_ss", "full_nk"],
+)
+@pytest.mark.parametrize(
+    "func",
+    ["f_ss_error_grad", "f_ss_error_hess", "f_ss_jac"],
+    ids=["grad", "hess", "jac"],
+)
+def test_all_backends_agree_on_functions(gcn_path, func):
+    backends = ["numpy", "numba", "pytensor"]
+    models = [
+        load_and_cache_model(gcn_path, backend, use_jax=JAX_INSTALLED)
+        for backend in backends
+    ]
+    params = models[0].parameters().to_string()
+    ss_vars = [x.to_ss().name for x in models[0].variables]
+    x0 = dict(zip(ss_vars, np.full(len(models[0].variables), 0.8)))
+
+    vals = [getattr(model, func)(**params, **x0) for model in models]
+    for i in range(3):
+        for j in range(i):
+            assert_allclose(
+                vals[i], vals[j], err_msg=f"{backends[i]} and {backends[j]} disagree"
+            )
 
-        Sigma = self.model.compute_stationary_covariance_matrix(
-            shock_dict={"epsilon_A": 0.01}
-        )
-        self.assertTrue(isinstance(Sigma, pd.DataFrame))
-        self.assertTrue(all([x == self.model.n_variables for x in Sigma.shape]))
 
+@pytest.mark.parametrize(
+    "gcn_path",
+    [
+        "rbc_2_block_partial_ss.gcn",
+        "full_nk_partial_ss.gcn",
+    ],
+    ids=["two_block", "full_nk"],
+)
+@pytest.mark.parametrize(
+    "func", ["f_ss_error_grad", "f_ss_error_hess"], ids=["grad", "hess"]
+)
+def test_scipy_wrapped_functions_agree(gcn_path, func):
+    backend_names = ["numpy", "numba", "pytensor"]
+    models = [
+        load_and_cache_model(gcn_path, backend, use_jax=JAX_INSTALLED)
+        for backend in backend_names
+    ]
+
+    ss_variables = [x.to_ss() for x in models[0].variables]
+    known_variables = list(models[0].f_ss(**models[0].parameters()).to_sympy().keys())
+
+    vars_to_solve = [var for var in ss_variables if var not in known_variables]
+    unknown_var_idx = np.array([x in vars_to_solve for x in ss_variables], dtype="bool")
+
+    params = models[0].parameters().to_string()
+    x0 = np.full(len(vars_to_solve), 0.8)
+
+    vals = [
+        scipy_wrapper(
+            getattr(model, func),
+            vars_to_solve,
+            unknown_var_idx,
+            unknown_var_idx,
+            model.f_ss,
+        )(x0, params)
+        for model in models
+    ]
+    for i in range(3):
+        for j in range(i):
+            assert_allclose(
+                vals[i],
+                vals[j],
+                err_msg=f"{backend_names[i]} and {backend_names[j]} disagree",
+                rtol=1e-8,
+                atol=1e-8,
+            )
 
-class ModelClassTestsTwo(unittest.TestCase):
-    def setUp(self):
-        file_path = os.path.join(ROOT, "Test GCNs/Two_Block_RBC_1.gcn")
-        self.model = gEconModel(file_path, verbose=False)
 
-    def test_model_options(self):
-        self.assertEqual(
-            self.model.options,
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+@pytest.mark.parametrize(
+    ("gcn_file", "expected_result"),
+    [
+        (
+            "one_block_1_ss.gcn",
             {
-                "output logfile": True,
-                "output LaTeX": True,
-                "output LaTeX landscape": True,
+                "A_ss": 1.0,
+                "C_ss": 0.91982617,
+                "I_ss": 0.27872301,
+                "K_ss": 13.9361507,
+                "L_ss": 0.3198395,
+                "U_ss": -132.00424906,
+                "Y_ss": 1.19854918,
+                "lambda_ss": 0.51233068,
+                "q_ss": 0.51233068,
             },
-        )
-
-    def test_reduce_vars_saved(self):
-        self.assertEqual(self.model.try_reduce_vars, None)
-
-    def test_model_file_loading(self):
-        block_names = ["HOUSEHOLD", "FIRM"]
-        result = [block_name for block_name in self.model.blocks.keys()]
-        self.assertEqual(result, block_names)
-
-        param_dict = {
-            "beta": 0.985,
-            "delta": 0.025,
-            "sigma_C": 2,
-            "sigma_L": 1.5,
-            "alpha": 0.35,
-            "rho_A": 0.95,
-        }
-
-        self.assertEqual(
-            all(
-                [
-                    self.model.free_param_dict[x] == param_dict[x]
-                    for x in param_dict.keys()
-                ]
-            ),
-            True,
-        )
-        self.assertEqual(self.model.params_to_calibrate, [])
-
-    def test_solve_model_gensys(self):
-        self.model.steady_state(verbose=False)
-        self.assertEqual(self.model.steady_state_solved, True)
-        self.model.solve_model(verbose=False, solver="gensys")
-        self.assertEqual(self.model.perturbation_solved, True)
-
-        P = np.array([[0.95000000, 0.0000000], [0.08887552, 0.9614003]])
-
-        Q = np.array([[1.00000000], [0.09355318]])
-
-        # TODO: Investigate sign flip on row 5, 6 (TC, U)
-        R = np.array(
-            [
-                [0.3437521, 0.3981261],
-                [3.5550207, -0.5439888],
-                [0.1418896, -0.2412174],
-                [1.0422283, 0.1932087],
-                [-0.2127497, -0.1270917],
-                [1.0422282, 0.1932087],
-                [-0.6875042, -0.7962522],
-                [-0.6875042, -0.7962522],
-                [1.0422284, -0.8067914],
-                [0.9003386, 0.4344261],
-            ]
-        )
+        ),
+        (
+            "open_rbc.gcn",
+            {
+                "A_ss": 1.00000000e00,
+                "CA_ss": 0.00000000e00,
+                "C_ss": 9.23561040e00,
+                "IIP_ss": 0.00000000e00,
+                "I_ss": 2.73647613e00,
+                "K_ss": 1.09459045e02,
+                "KtoN_ss": 2.59033302e01,
+                "N_ss": 4.22567464e00,
+                "TB_ss": 0.00000000e00,
+                "TBtoY_ss": 0.00000000e00,
+                "U_ss": 7.32557872e01,
+                "Y_ss": 1.19720865e01,
+                "lambda_ss": 7.54570414e-02,
+                "r_ss": 1.00000101e-02,
+                "r_given_ss": 1.00000101e-02,
+            },
+        ),
+        (
+            "full_nk.gcn",
+            {
+                "C_ss": 1.50620761e00,
+                "Div_ss": 6.69069052e-01,
+                "I_ss": 2.77976530e-01,
+                "K_ss": 1.11190612e01,
+                "LHS_ss": 6.16941715e00,
+                "LHS_w_ss": 1.40646786e00,
+                "L_ss": 6.66135866e-01,
+                "RHS_ss": 3.85588572e00,
+                "RHS_w_ss": 1.40646786e00,
+                "TC_ss": -1.11511509e00,
+                "U_ss": -1.47270439e02,
+                "Y_ss": 1.78418414e00,
+                "q_ss": 8.90392916e-01,
+                "mc_ss": 6.25000000e-01,
+                "shock_preference_ss": 1.00000000e00,
+                "shock_technology_ss": 1.00000000e00,
+                "pi_ss": 1.00000000e00,
+                "lambda_ss": 8.90392916e-01,
+                "r_G_ss": 1.01010101e00,
+                "r_ss": 3.51010101e-02,
+                "pi_obj_ss": 1.00000000e00,
+                "pi_star_ss": 1.00000000e00,
+                "w_ss": 1.08810356e00,
+                "w_star_ss": 1.08810356e00,
+            },
+        ),
+    ],
+    ids=["one_block", "open_rbc", "nk"],
+)
+def test_steady_state(backend: BACKENDS, gcn_file: str, expected_result: np.ndarray):
+    n = len(expected_result)
 
-        S = np.array(
-            [
-                [0.3618443],
-                [3.7421271],
-                [0.1493575],
-                [1.0970824],
-                [-0.2239471],
-                [1.0970823],
-                [-0.7236886],
-                [-0.7236886],
-                [1.0970825],
-                [0.9477249],
-            ]
-        )
+    model = load_and_cache_model(gcn_file, backend, use_jax=JAX_INSTALLED)
 
-        A, _, _, _ = self.model.build_perturbation_matrices(
-            np.fromiter(
-                (self.model.free_param_dict | self.model.calib_param_dict).values(),
-                dtype="float",
-            ),
-            np.fromiter(self.model.steady_state_dict.values(), dtype="float"),
-        )
+    params = model.parameters()
+    ss_dict = model.f_ss(**params)
+    ss = np.array(np.r_[list(ss_dict.values())])
+    expected_ss = np.r_[[expected_result[var] for var in ss_dict.to_string().keys()]]
 
-        (
-            _,
-            variables,
-            _,
-        ) = self.model.perturbation_solver.make_all_variable_time_combinations()
-
-        gEcon_matrices = (
-            self.model.perturbation_solver.statespace_to_gEcon_representation(
-                A, self.model.T.values, self.model.R.values, variables, 1e-7
-            )
-        )
-        model_P, model_Q, model_R, model_S, *_ = gEcon_matrices
-
-        assert_allclose(model_P, P, equal_nan=True, err_msg="P", rtol=1e-5)
-        assert_allclose(model_Q, Q, equal_nan=True, err_msg="Q", rtol=1e-5)
-        assert_allclose(model_R, R, equal_nan=True, err_msg="R", rtol=1e-5)
-        assert_allclose(model_S, S, equal_nan=True, err_msg="S", rtol=1e-5)
-
-    def test_solve_model_cycle_reduction(self):
-        self.model.steady_state(verbose=False)
-        self.assertEqual(self.model.steady_state_solved, True)
-        self.model.solve_model(verbose=False, solver="cycle_reduction")
-
-        P = np.array([[0.95000000, 0.0000000], [0.08887552, 0.9614003]])
-
-        Q = np.array([[1.00000000], [0.09355318]])
-
-        # TODO: Investigate sign flip on row 5, 6 (TC, U)
-        R = np.array(
-            [
-                [0.3437521, 0.3981261],
-                [3.5550207, -0.5439888],
-                [0.1418896, -0.2412174],
-                [1.0422283, 0.1932087],
-                [-0.2127497, -0.1270917],
-                [1.0422282, 0.1932087],
-                [-0.6875042, -0.7962522],
-                [-0.6875042, -0.7962522],
-                [1.0422284, -0.8067914],
-                [0.9003386, 0.4344261],
-            ]
-        )
+    assert_allclose(ss, expected_ss)
+    assert_allclose(model._evaluate_steady_state(), np.zeros(n), atol=1e-8)
 
-        S = np.array(
-            [
-                [0.3618443],
-                [3.7421271],
-                [0.1493575],
-                [1.0970824],
-                [-0.2239471],
-                [1.0970823],
-                [-0.7236886],
-                [-0.7236886],
-                [1.0970825],
-                [0.9477249],
-            ]
-        )
+    # Total error and gradient should be zero at the steady state as well
+    error = model.f_ss_error(**params, **ss_dict)
+    grad = model.f_ss_error_grad(**params, **ss_dict)
+    hess = model.f_ss_error_hess(**params, **ss_dict)
 
-        A, _, _, _ = self.model.build_perturbation_matrices(
-            np.fromiter(
-                (self.model.free_param_dict | self.model.calib_param_dict).values(),
-                dtype="float",
-            ),
-            np.fromiter(self.model.steady_state_dict.values(), dtype="float"),
-        )
+    assert isinstance(error, float)
+    assert isinstance(grad, np.ndarray)
+    assert isinstance(hess, np.ndarray)
 
-        (
-            _,
-            variables,
-            _,
-        ) = self.model.perturbation_solver.make_all_variable_time_combinations()
-
-        gEcon_matrices = (
-            self.model.perturbation_solver.statespace_to_gEcon_representation(
-                A, self.model.T.values, self.model.R.values, variables, 1e-7
-            )
-        )
-        model_P, model_Q, model_R, model_S, *_ = gEcon_matrices
+    assert grad.ndim == 1
+    assert hess.ndim == 2
 
-        assert_allclose(model_P, P, equal_nan=True, err_msg="P", rtol=1e-5)
-        assert_allclose(model_Q, Q, equal_nan=True, err_msg="Q", rtol=1e-5)
-        assert_allclose(model_R, R, equal_nan=True, err_msg="R", rtol=1e-5)
-        assert_allclose(model_S, S, equal_nan=True, err_msg="S", rtol=1e-5)
+    assert_allclose(error, 0.0, atol=1e-8)
+    assert_allclose(grad.ravel(), np.zeros((n,)), atol=1e-8)
 
-    def test_solvers_agree(self):
-        self.setUp()
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(solver="gensys", verbose=False)
-        Tg, Rg = self.model.T, self.model.R
+    # Hessian should be PSD at the minimum (since it's a convex function)
+    assert np.all(np.linalg.eigvals(hess) > -1e8)
 
-        self.setUp()
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(solver="cycle_reduction", verbose=False)
-        Tc, Rc = self.model.T, self.model.R
 
-        assert_allclose(
-            Tg.round(5).values,
-            Tc.round(5).values,
-            rtol=1e-5,
-            equal_nan=True,
-            err_msg="T",
-        )
-        assert_allclose(
-            Rg.round(5).values,
-            Rc.round(5).values,
-            rtol=1e-5,
-            equal_nan=True,
-            err_msg="R",
-        )
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+@pytest.mark.parametrize(
+    "gcn_file",
+    ["one_block_1_ss.gcn", "open_rbc.gcn", "full_nk.gcn"],
+)
+def test_model_gradient(backend, gcn_file):
+    model = load_and_cache_model(gcn_file, backend, use_jax=JAX_INSTALLED)
 
+    ss_result = model.steady_state()
 
-class ModelClassTestsThree(unittest.TestCase):
-    def setUp(self):
-        file_path = os.path.join(ROOT, "Test GCNs/Full_New_Keyensian.gcn")
-        self.model = gEconModel(
-            file_path, verbose=False, simplify_constants=False, simplify_tryreduce=False
-        )
+    np.testing.assert_allclose(
+        model.f_ss_error_grad(**ss_result, **model.parameters()),
+        0.0,
+        rtol=1e-12,
+        atol=1e-12,
+    )
 
-    def test_model_options(self):
-        self.assertEqual(
-            self.model.options,
-            {
-                "output logfile": True,
-                "output LaTeX": True,
-                "output LaTeX landscape": True,
-            },
-        )
+    perturbed_point = {k: 0.8 for k, v in ss_result.items()}
+    test_point = np.array(list(perturbed_point.values()))
 
-    def test_reduce_vars_saved(self):
-        self.assertEqual(
-            self.model.try_reduce_vars,
-            [
-                "Div[]",
-                "TC[]",
-                # TimeAwareSymbol("Div", 0, **self.model.assumptions["DIV"]),
-                # TimeAwareSymbol("TC", 0, **self.model.assumptions["TC"]),
-            ],
-        )
+    grad = model.f_ss_error_grad(**perturbed_point, **model.parameters())
+    numeric_grad = nd.Gradient(lambda x: model.f_ss_error(*x, **model.parameters()))(
+        test_point
+    )
 
-    def test_model_file_loading(self):
-        block_names = [
-            "HOUSEHOLD",
-            "WAGE_SETTING",
-            "WAGE_EVOLUTION",
-            "PREFERENCE_SHOCKS",
-            "FIRM",
-            "TECHNOLOGY_SHOCKS",
-            "FIRM_PRICE_SETTING_PROBLEM",
-            "PRICE_EVOLUTION",
-            "MONETARY_POLICY",
-            "EQUILIBRIUM",
-        ]
-
-        result = [block_name for block_name in self.model.blocks.keys()]
-        self.assertEqual(result, block_names)
+    np.testing.assert_allclose(grad, numeric_grad, rtol=1e-8, atol=1e-8)
 
-        (
-            rho_technology,
-            gamma_R,
-            gamma_pi,
-            gamma_Y,
-            phi_pi_obj,
-            phi_pi,
-            rho_pi_dot,
-        ) = sp.symbols(
-            [
-                "rho_technology",
-                "gamma_R",
-                "gamma_pi",
-                "gamma_Y",
-                "phi_pi_obj",
-                "phi_pi",
-                "rho_pi_dot",
-            ],
-            **DEFAULT_ASSUMPTIONS,
-        )
+    hess = model.f_ss_error_hess(**perturbed_point, **model.parameters())
+    numeric_hess = nd.Hessian(lambda x: model.f_ss_error(*x, **model.parameters()))(
+        test_point
+    )
 
-        param_dict = {
-            "delta": 0.025,
-            "beta": 0.99,
-            "sigma_C": 2,
-            "sigma_L": 1.5,
-            "gamma_I": 10,
-            "phi_H": 0.5,
-            "psi_w": 0.782,
-            "eta_w": 0.75,
-            "alpha": 0.35,
-            "rho_technology": 0.95,
-            "rho_preference": 0.95,
-            "psi_p": 0.6,
-            "eta_p": 0.75,
-            "gamma_R": 0.9,
-            "gamma_pi": 1.5,
-            "gamma_Y": 0.05,
-            "rho_pi_dot": 0.924,
-        }
+    np.testing.assert_allclose(hess, numeric_hess, rtol=1e-8, atol=1e-8)
 
-        self.assertEqual(
-            all([x in param_dict.keys() for x in self.model.free_param_dict.keys()]),
-            True,
-        )
-        self.assertEqual(
-            all(
-                [
-                    self.model.free_param_dict[x] == param_dict[x]
-                    for x in param_dict.keys()
-                ]
-            ),
-            True,
-        )
-        self.assertEqual(self.model.params_to_calibrate, [phi_pi, phi_pi_obj])
+    jac = model.f_ss_jac(**perturbed_point, **model.parameters())
+    numeric_jac = nd.Jacobian(lambda x: model.f_ss_resid(*x, **model.parameters()))(
+        test_point
+    )
 
-    def test_solvers_agree(self):
-        self.setUp()
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(solver="gensys", verbose=False)
-        Tg, Rg = self.model.T, self.model.R
+    np.testing.assert_allclose(jac, numeric_jac, rtol=1e-8, atol=1e-8)
 
-        self.setUp()
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(solver="cycle_reduction", verbose=False)
-        Tc, Rc = self.model.T, self.model.R
 
-        assert_allclose(
-            Tg.values, Tc.values, rtol=1e-5, atol=1e-5, equal_nan=True, err_msg="T"
-        )
-        assert_allclose(
-            Rg.values, Rc.values, rtol=1e-5, atol=1e-5, equal_nan=True, err_msg="R"
-        )
+@pytest.mark.parametrize("how", ["root", "minimize"], ids=["root", "minimize"])
+@pytest.mark.parametrize(
+    "gcn_file",
+    ["one_block_1_ss.gcn", "open_rbc.gcn", "full_nk.gcn", "rbc_with_excluded.gcn"],
+)
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+def test_numerical_steady_state(how: str, gcn_file: str, backend: BACKENDS):
+    # TODO: I was hitting errors when the models were reused, something about the fixed values was breaking stuff.
+    #  Need to track this bug down.
+    model = load_and_cache_model(gcn_file, backend, use_jax=JAX_INSTALLED)
+    analytic_res = model.steady_state()
+    analytic_values = np.array([analytic_res[x.to_ss().name] for x in model.variables])
+
+    # Overwrite the f_ss function with None to trigger numerical optimization
+    # Save it so we can put it back later, or else the cached model won't have a steady state function anymore
+    f_ss = model.f_ss
+    model.f_ss = None
+
+    if gcn_file == "full_nk.gcn":
+        fixed_values = {
+            "shock_technology_ss": 1.0,
+            "shock_preference_ss": 1.0,
+            "pi_ss": 1.0,
+            "pi_star_ss": 1.0,
+            "pi_obj_ss": 1.0,
+        }
+    else:
+        fixed_values = None
+
+    numeric_res = model.steady_state(
+        how=how,
+        verbose=False,
+        use_hess=True,
+        use_hessp=False,
+        optimizer_kwargs={
+            "maxiter": 50_000,
+            "method": "hybr" if how == "root" else "Newton-CG",
+        },
+        fixed_values=fixed_values,
+    )
+
+    # Restore steady state function in the cached function
+    model.f_ss = f_ss
+
+    numeric_values = np.array([numeric_res[x.to_ss().name] for x in model.variables])
+    errors = model.f_ss_resid(**numeric_res, **model.parameters())
+
+    if how == "root":
+        assert_allclose(analytic_values, numeric_values, atol=1e-2)
+    elif how == "minimize":
+        assert_allclose(errors, np.zeros_like(errors), atol=1e-2)
+
+
+def test_numerical_steady_state_with_calibrated_params():
+    file_path = "one_block_2_no_extra.gcn"
+    model = load_and_cache_model(file_path, "numpy", use_jax=JAX_INSTALLED)
+
+    res = model.steady_state(
+        how="minimize",
+        verbose=False,
+        optimizer_kwargs={"method": "trust-constr", "options": {"maxiter": 100_000}},
+        bounds={"alpha": (0.05, 0.7)},
+    )
+    res = res.to_string()
+    assert_allclose(res["L_ss"] / res["K_ss"], 0.36)
+
+
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+def test_steady_state_with_parameter_updates(backend):
+    file_path = "rbc_2_block_ss.gcn"
+    model = load_and_cache_model(file_path, "numpy", use_jax=JAX_INSTALLED)
 
-    # def test_solve_model(self):
-    #     self.model.steady_state(verbose=False)
-
-    #     self.model.solve_model(verbose=False, solver='gensys')
-    #
-    #     P = np.array([[0.92400000, 0.00000000, 0.000000000, 0.000000000, 0.000000000, 0.0000000000, 0.0000000000,
-    #                    0.000000000, 0.00000000, 0.0000000000],
-    #                   [0.04464553, 0.77386407, 0.008429303, -0.035640523, 0.019260369, -0.0061647545, 0.0064098938,
-    #                    0.003811426, -0.01635691, -0.0042992448],
-    #                   [0.00000000, 0.00000000, 0.950000000, 0.000000000, 0.000000000, 0.0000000000, 0.0000000000,
-    #                    0.000000000, 0.00000000, 0.0000000000],
-    #                   [0.00000000, 0.00000000, 0.000000000, 0.950000000, 0.000000000, 0.0000000000, 0.0000000000,
-    #                    0.000000000, 0.00000000, 0.0000000000],
-    #                   [0.11400712, -0.23033661, 0.017018503, 0.246571939, 0.714089188, 0.0015115630, -0.0025199985,
-    #                    0.003439315, 0.09953510, 0.0012796478],
-    #                   [0.00000000, 0.00000000, 0.000000000, 0.000000000, 0.000000000, 0.0000000000, 0.0000000000,
-    #                    0.000000000, 0.00000000, 0.0000000000],
-    #                   [0.56944713, -1.31534877, 0.116205871, 0.279528217, -0.069058930, 0.0055509980, 0.4892113664,
-    #                    0.001268753, 0.13710342, 0.0073074932],
-    #                   [0.77344786, -1.65448037, -0.084222852, 0.373554371, -0.110359402, 0.0067463467, -0.0129713461,
-    #                    0.893824526, -0.07071734, 0.0091915576],
-    #                   [0.01933620, -0.04136201, -0.002105571, 0.009338859, -0.002758985, 0.0001686587, -0.0003242837,
-    #                    0.022345613, 0.97323207, 0.0002297889],
-    #                   [0.60123052, -1.36818560, 0.084979004, 0.294177526, -0.075493558, -0.5547430352, 0.4109711206,
-    #                    0.140329261, 0.10472487, 0.0076010311]])
-    #
-    #     Q = np.array([[0.000000000, 0.000000000, 0.00000000, 1.00000000],
-    #                   [0.008872950, -0.037516340, 0.85984896, 0.04831767],
-    #                   [1.000000000, 0.000000000, 0.00000000, 0.00000000],
-    #                   [0.000000000, 1.000000000, 0.00000000, 0.00000000],
-    #                   [0.017914213, 0.259549409, -0.25592956, 0.12338433],
-    #                   [0.000000000, 0.000000000, 0.00000000, 0.00000000],
-    #                   [0.122321970, 0.294240229, -1.46149864, 0.61628477],
-    #                   [-0.088655634, 0.393215127, -1.83831153, 0.83706479],
-    #                   [-0.002216391, 0.009830378, -0.04595779, 0.02092662],
-    #                   [0.089451584, 0.309660553, -1.52020622, 0.65068238]])
-    #
-    #     R = np.array([[-2.70120790, 6.4759672, 0.45684368, -1.0523862, 0.25304694, -0.028589270, 0.043922008,
-    #                    -0.010211851, -0.50854833, -0.0359775957],
-    #                   [0.43774664, -0.9670519, 0.06277643, -1.0565632, 0.67343881, -0.297196226, 0.218772144,
-    #                    0.079001225, -0.38253612, 0.0053725107],
-    #                   [0.58559582, -0.7953000, 0.05336272, -0.2474094, 0.13091891, -0.022606929, 0.029033588,
-    #                    0.020731865, -0.11253692, 0.0044183336],
-    #                   [1.75678747, -2.3859001, 0.16008816, -0.7422282, 0.39275674, -0.067820786, 0.087100765,
-    #                    0.062195594, -0.33761076, 0.0132550007],
-    #                   [-0.34114299, 0.5424464, 0.48057739, -0.7361740, 0.12517618, -0.002047156, 0.028009363,
-    #                    -0.063210365, -0.75978505, -0.0030135913],
-    #                   [1.03897717, -2.3352376, 0.14775544, -0.7623857, 0.59794526, -0.851939276, 0.629743275,
-    #                    0.219330490, -1.27781127, 0.0129735420],
-    #                   [2.21281597, -3.3072465, 0.22816217, 0.2440595, 0.24911350, -0.061774534, 0.077020771,
-    #                    0.075952852, 0.06052965, 0.0183735919],
-    #                   [0.92497003, -2.1049009, 0.13073693, -1.0089577, -0.11614394, -0.853450826, 0.632263264,
-    #                    0.215891172, -0.37734635, 0.0116938940],
-    #                   [-1.86247082, 3.7798186, 0.45779728, -1.0016774, 0.69227986, -0.140940083, 0.177078457,
-    #                    0.079706624, -0.54739326, -0.0209989924],
-    #                   [2.76788546, -2.2659060, 0.88668501, -1.6781507, 0.64733010, -0.213012025, 0.289374259,
-    #                    0.186334186, -0.95855542, 0.0125883667],
-    #                   [-1.86247082, 3.7798186, 0.45779728, -1.0016774, 0.69227986, -0.140940083, 0.177078457,
-    #                    0.079706624, -0.54739326, -0.0209989924],
-    #                   [2.76788546, -2.2659060, 0.88668501, -1.6781507, 0.64733010, -0.213012025, 0.289374259,
-    #                    0.186334186, -0.95855542, 0.0125883667],
-    #                   [0.07745758, -0.1102706, -0.16967797, 0.1782883, -0.01302221, 0.002553406, -0.004433502,
-    #                    0.007300968, 0.07817343, 0.0006126142]])
-    #
-    #     S = np.array([[0.48088808, -1.1077749, 7.1955191, -2.92338518],
-    #                   [0.06608045, -1.1121718, -1.0745021, 0.47375177],
-    #                   [0.05617128, -0.2604309, -0.8836667, 0.63376171],
-    #                   [0.16851385, -0.7812928, -2.6510001, 1.90128514],
-    #                   [0.50587094, -0.7749200, 0.6027183, -0.36920237],
-    #                   [0.15553204, -0.8025113, -2.5947084, 1.12443417],
-    #                   [0.24017070, 0.2569048, -3.6747184, 2.39482247],
-    #                   [0.13761782, -1.0620607, -2.3387788, 1.00104982],
-    #                   [0.48189187, -1.0543973, 4.1997985, -2.01566106],
-    #                   [0.93335265, -1.7664744, -2.5176733, 2.99554704],
-    #                   [0.48189187, -1.0543973, 4.1997985, -2.01566106],
-    #                   [0.93335265, -1.7664744, -2.5176733, 2.99554704],
-    #                   [-0.17860839, 0.1876719, -0.1225228, 0.08382855]])
-    #
-    #     index_10 = ['pi_obj', 'r_G', 'shock_preference', 'shock_technology', 'w', 'B', 'C', 'I', 'K', 'Y']
-    #     cols_10 = ['pi_obj', 'r_G', 'shock_preference', 'shock_technology', 'w', 'B', 'C',  'I', 'K', 'Y']
-    #
-    #
-    #
-    #     index_11 = ['lambda_t', 'q_t', 'r_t', 'w_t', 'C_t', 'I_t', 'L_t', 'P_t', 'TC_t', 'U_t', 'Y_t']
-    #     ss_df = pd.Series(self.model.steady_state_dict)
-    #     ss_df.index = list(map(lambda x: x.exit_ss().name, ss_df.index))
-    #     ss_df = ss_df.reindex(self.model.S.index)
-    #     neg_ss_mask = ss_df < 0
-    #
-    #     for answer, result in zip([P, Q, R, S], [self.model.P, self.model.Q, self.model.R, self.model.S]):
-    #         if result.shape[0] == 11:
-    #             result = result.loc[index_11, :]
-    #             result.loc[neg_ss_mask, :] = result.loc[neg_ss_mask, :] * -1
-    #         self.assertEqual(np.allclose(answer, result.values), True)
-
-
-class TestLinearModel(unittest.TestCase):
-    def setUp(self):
-        file_path = os.path.join(ROOT, "Test GCNs/RBC_Linearized.gcn")
-        self.model = gEconModel(file_path, verbose=False)
-
-    def test_deterministics_are_extracted(self):
-        self.assertEqual(len(self.model.deterministic_params), 7)
-
-    def test_steady_state(self):
-        self.model.steady_state(model_is_linear=True, verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        self.assertTrue(
-            np.allclose(
-                np.array(list(self.model.steady_state_dict.values())),
-                np.array([0, 0, 0, 0, 0, 0, 0, 0]),
-            )
-        )
+    rng = np.random.default_rng()
+    delta = rng.beta(1, 1)
+    beta = rng.beta(1, 1)
+    ss_dict = model.steady_state(delta=delta, beta=beta)
 
-    def test_perturbation_solver(self):
-        self.model.steady_state(verbose=False, model_is_linear=True)
-        self.model.solve_model(verbose=False, model_is_linear=True)
-        self.assertTrue(self.model.perturbation_solved)
-
-        T_dynare = np.array(
-            [
-                [0.95, 0.0],
-                [0.34375208, 0.39812608],
-                [3.55502044, -0.54398862],
-                [0.08887551, 0.96140028],
-                [0.14188965, -0.24121738],
-                [1.04222827, -0.8067913],
-                [0.90033862, 0.43442608],
-                [1.04222827, 0.1932087],
-            ]
-        )
+    assert_allclose(ss_dict["r_ss"], (1 / beta - (1 - delta)))
 
-        R_dynare = np.array(
-            [1.0, 0.361844, 3.742127, 0.093553, 0.149358, 1.097082, 0.947725, 1.097082]
-        )
 
-        assert_allclose(
-            self.model.T[["A", "K"]].values, T_dynare, rtol=1e-5, atol=1e-5, err_msg="T"
-        )
-        assert_allclose(
-            self.model.R.values,
-            R_dynare.reshape(-1, 1),
-            rtol=1e-5,
-            atol=1e-5,
-            err_msg="R",
-        )
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+@pytest.mark.parametrize(
+    "partial_file, analytic_file",
+    [
+        (
+            "rbc_2_block_partial_ss.gcn",
+            "rbc_2_block_ss.gcn",
+        ),
+        ("full_nk_partial_ss.gcn", "full_nk.gcn"),
+    ],
+)
+def test_partially_analytical_steady_state(
+    backend: BACKENDS, partial_file, analytic_file
+):
+    analytic_model = load_and_cache_model(analytic_file, backend, use_jax=JAX_INSTALLED)
+    analytic_res = analytic_model.steady_state()
+    analytic_values = np.array(list(analytic_res.values()))
+
+    partial_model = load_and_cache_model(partial_file, backend, use_jax=JAX_INSTALLED)
+    numeric_res = partial_model.steady_state(
+        how="minimize",
+        verbose=False,
+        optimizer_kwargs={"method": "trust-ncg", "options": {"gtol": 1e-24}},
+    )
+
+    numeric_values = np.array(list(numeric_res.values()))
+
+    errors = partial_model.f_ss_resid(
+        **numeric_res.to_string(), **partial_model.parameters().to_string()
+    )
+    resid = partial_model.f_ss_resid(
+        **numeric_res.to_string(), **partial_model.parameters().to_string()
+    )
+
+    ATOL = RTOL = 1e-1
+    if partial_file == "Two_Block_RBC_w_Partial_Steady_State":
+        assert_allclose(analytic_values, numeric_values, atol=ATOL, rtol=RTOL)
+
+    assert_allclose(resid, 0, atol=ATOL, rtol=RTOL)
+    assert_allclose(errors, np.zeros_like(errors), atol=ATOL, rtol=RTOL)
+
+
+@pytest.mark.parametrize(
+    "gcn_file, name",
+    [
+        ("one_block_1_ss.gcn", "one_block_ss"),
+        ("rbc_2_block_ss.gcn", "two_block_ss"),
+        ("full_nk.gcn", "full_nk"),
+    ],
+    ids=["one_block_ss", "two_block_ss", "full_nk"],
+)
+@pytest.mark.parametrize("backend", ["numba"], ids=["numba"])
+def test_linearize(gcn_file, name, backend: BACKENDS):
+    model = load_and_cache_model(gcn_file, backend, use_jax=JAX_INSTALLED)
+    steady_state_dict = model.steady_state()
+    outputs = model.linearize_model(
+        loglin_negative_ss=True, steady_state=steady_state_dict
+    )
 
-    def test_solvers_agree(self):
-        self.setUp()
-        self.model.steady_state(verbose=False, model_is_linear=True)
-        self.model.solve_model(solver="gensys", verbose=False, model_is_linear=True)
-        Tg, Rg = self.model.T, self.model.R
+    for mat_name, out in zip(["A", "B", "C", "D"], outputs):
+        expected_out = expected_linearization_result[gcn_file][mat_name]
+        assert_allclose(out, expected_out, atol=1e-8, err_msg=f"{mat_name} failed")
 
-        self.setUp()
-        self.model.steady_state(verbose=False, model_is_linear=True)
-        self.model.solve_model(
-            solver="cycle_reduction", verbose=False, model_is_linear=True
-        )
-        Tc, Rc = self.model.T, self.model.R
 
-        assert_allclose(
-            Tg.values, Tc.values, rtol=1e-5, atol=1e-5, equal_nan=True, err_msg="T"
-        )
-        assert_allclose(
-            Rg.values, Rc.values, rtol=1e-5, atol=1e-5, equal_nan=True, err_msg="R"
-        )
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+def test_linearize_with_custom_params(backend):
+    model = load_and_cache_model("one_block_1_ss.gcn", backend, use_jax=JAX_INSTALLED)
+    params = model.parameters(rho=0.5)
+    assert params["rho"] == 0.5
 
+    # Use rho because d_shock_transiton/d_A = rho
+    rho = np.random.beta(1, 1)
+    A_idx = [x.base_name for x in model.variables].index("A")
+    technology_eq_idx = next(
+        i for i, eq in enumerate(model.equations) if model.shocks[0] in eq.atoms()
+    )
 
-class TestModelSimulationTools(unittest.TestCase):
-    def setUp(self):
-        file_path = os.path.join(
-            ROOT, "Test GCNs/One_Block_Simple_1_w_Distributions.gcn"
-        )
-        self.model = gEconModel(file_path, verbose=False)
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(verbose=False)
+    A, *_ = model.linearize_model(rho=rho)
+    assert A[technology_eq_idx, A_idx] == rho
 
-    def test_sample_param_dicts(self):
-        param_dict, shock_dict, obs_dict = self.model.sample_param_dict_from_prior(
-            n_samples=100
-        )
 
-        self.assertTrue(
-            all([x in self.model.free_param_dict for x in param_dict.to_string()])
-        )
-        self.assertTrue(len(param_dict) == 3)
+def test_invalid_solver_raises():
+    file_path = "tests/Test GCNs/one_block_1_ss.gcn"
+    model = model_from_gcn(file_path, verbose=False)
+    model.steady_state(verbose=False)
 
-        self.assertTrue(all([x.name in shock_dict for x in self.model.shocks]))
-        self.assertTrue(len(shock_dict) == 1)
+    with pytest.raises(NotImplementedError):
+        model.solve_model(solver="invalid_solver")
 
-        self.assertTrue(len(obs_dict) == 0)
 
-    def test_irf(self):
-        simulation_length = 40
-        irf = self.model.impulse_response_function(
-            simulation_length=simulation_length, shock_size=0.1
-        )
+def test_bad_failure_argument_raises():
+    file_path = "tests/Test GCNs/pert_fails.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
 
-        self.assertTrue(isinstance(irf, pd.DataFrame))
-        self.assertTrue(irf.shape[0] == self.model.n_variables)
-        self.assertTrue(irf.shape[1] == self.model.n_shocks * simulation_length)
+    with pytest.raises(ValueError):
+        model.solve_model(solver="gensys", on_failure="raise", model_is_linear=True)
 
-    def test_simulate_warns_on_defaults(self):
-        simulation_length = 40
-        n_simulations = 1
 
-        # Overwrite the priors to get the warning
-        self.model.hyper_priors = SymbolDictionary()
-        self.model.shock_priors = SymbolDictionary()
-        with self.assertWarns(UserWarning):
-            self.model.simulate(
-                simulation_length=simulation_length, n_simulations=n_simulations
-            )
+def test_gensys_fails_to_solve():
+    file_path = "tests/Test GCNs/pert_fails.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
 
-    def test_simulate_from_covariance_matrix(self):
-        simulation_length = 40
-        n_simulations = 1
-        Q = np.array([[0.01]])
-        data = self.model.simulate(
-            simulation_length=simulation_length,
-            n_simulations=n_simulations,
-            shock_cov_matrix=Q,
-        )
+    with pytest.raises(GensysFailedException):
+        model.solve_model(solver="gensys", on_failure="error", verbose=False)
 
-        self.assertTrue(isinstance(data, pd.DataFrame))
-        self.assertTrue(data.shape[0] == self.model.n_variables)
-        self.assertTrue(data.shape[1] == simulation_length * n_simulations)
-
-    def test_simulate_from_shock_dict(self):
-        simulation_length = 40
-        n_simulations = 1
-        shock_dict = {"epsilon_A": 0.1}
-        data = self.model.simulate(
-            simulation_length=simulation_length,
-            n_simulations=n_simulations,
-            shock_dict=shock_dict,
-        )
 
-        self.assertTrue(isinstance(data, pd.DataFrame))
-        self.assertTrue(data.shape[0] == self.model.n_variables)
-        self.assertTrue(data.shape[1] == simulation_length * n_simulations)
+def test_outputs_after_gensys_failure(caplog):
+    file_path = "tests/Test GCNs/pert_fails.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
+    T, R = model.solve_model(solver="gensys", on_failure="ignore", verbose=True)
 
-    def test_fit_model_and_sample_posterior_trajectories(self):
-        T = 100
-        n_simulations = 1
+    captured_message = caplog.messages[-1]
+    assert captured_message == (
+        "Gensys return codes: 1 0 2, with the following meaning:\n"
+        "Solution exists, but is not unique."
+    )
+    assert T is None
+    assert R is None
 
-        # Draw from shock prior
-        data = self.model.simulate(simulation_length=T, n_simulations=n_simulations)
 
-        # Only Y is observed
-        data = data.droplevel(axis=1, level=1).T[["C"]]
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+@pytest.mark.parametrize(
+    "model_name, log_linearize",
+    [
+        ("one_block_1_ss", False),
+        ("rbc_2_block_ss", False),
+        ("full_nk", False),
+        ("basic_rbc", False),
+        ("basic_rbc", True),
+    ],
+    ids=lambda x: str(x),
+)
+def test_solve_matches_dynare(backend, model_name, log_linearize):
+    gcn_file = model_name + ".gcn"
+    model = load_and_cache_model(gcn_file, backend, use_jax=JAX_INSTALLED)
+    T, R = model.solve_model(
+        solver="gensys", verbose=False, log_linearize=log_linearize
+    )
+
+    if log_linearize:
+        model_name = model_name + "_loglinear"
+
+    dynare_T, dynare_R = load_dynare_outputs(model_name).values()
+
+    T = matrix_to_dataframe(T, model).reindex_like(dynare_T)
+    R = matrix_to_dataframe(R, model).reindex_like(dynare_R)
+
+    assert_allclose(T[dynare_T.columns], dynare_T, atol=1e-5, rtol=1e-5)
+    assert_allclose(R[dynare_R.columns], dynare_R, atol=1e-5, rtol=1e-5)
+
+
+def test_outputs_after_pert_success(caplog):
+    file_path = "tests/Test GCNs/rbc_linearized.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
+    model.solve_model(solver="gensys", verbose=True)
+
+    result_messages = caplog.messages[-2:]
+    expected_messages = [
+        "Norm of deterministic part: 0.000000000",
+        "Norm of stochastic part:    0.000000000",
+    ]
+
+    for message, expected_message in zip(result_messages, expected_messages):
+        assert message == expected_message
+
+
+def test_bad_argument_to_bk_condition_raises():
+    file_path = "tests/Test GCNs/rbc_linearized.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
+
+    A, B, C, D = model.linearize_model()
+    with pytest.raises(ValueError, match='Unknown return type "invalid_argument"'):
+        check_bk_condition(A, B, C, D, return_value="invalid_argument")
+
+
+def test_check_bk_condition():
+    file_path = "tests/Test GCNs/rbc_linearized.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
+    A, B, C, D = model.linearize_model()
+
+    bk_df = check_bk_condition(A, B, C, D, return_value="dataframe", verbose=False)
+    assert isinstance(bk_df, pd.DataFrame)
+
+    assert_allclose(
+        bk_df["Modulus"].values,
+        np.abs(bk_df["Real"].values + bk_df["Imaginary"].values * 1j),
+    )
+
+    bk_res = check_bk_condition(A, B, C, D, return_value="bool", verbose=False)
+    assert bk_res
+
+
+def test_summarize_perturbation_solution():
+    file_path = "tests/Test GCNs/rbc_linearized.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
+    linear_system = [A, B, C, D] = model.linearize_model()
+    policy_function = [T, R] = model.solve_model(solver="gensys", verbose=False)
+
+    res = summarize_perturbation_solution(linear_system, policy_function, model)
+    matrix_names = ["A", "B", "C", "D", "T", "R"]
+    assert isinstance(res, xr.Dataset)
+    assert all(name in res.data_vars for name in matrix_names)
+    for matrix, name in zip([*linear_system, *policy_function], matrix_names):
+        assert_allclose(res[name].to_numpy(), matrix)
+
+
+def test_validate_shock_options():
+    file_path = "tests/Test GCNs/full_nk.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
+    T, R = model.solve_model(solver="gensys", verbose=False)
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "Exactly one of shock_std_dict, shock_cov_matrix, or shock_std should be provided. "
+            "You passed 0."
+        ),
+    ):
+        stationary_covariance_matrix(model, T, R)
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "Exactly one of shock_std_dict, shock_cov_matrix, or shock_std should be provided. "
+            "You passed 2."
+        ),
+    ):
+        stationary_covariance_matrix(
+            model, T, R, shock_cov_matrix=np.eye(1), shock_std=0.1
+        )
 
-        idata = self.model.fit(
-            data,
-            filter_type="univariate",
-            draws=36,
-            n_walkers=36,
-            return_inferencedata=True,
-            burn_in=0,
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "If shock_std_dict is specified, it must give values for all shocks. "
+            "The following shocks were not found among the provided keys: lol :)"
+        ),
+    ):
+        stationary_covariance_matrix(model, T, R, shock_std_dict={"lol :)": 0.1})
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "Incorrect covariance matrix shape. Expected (4, 4), found (2, 2)"
+        ),
+    ):
+        stationary_covariance_matrix(model, T, R, shock_cov_matrix=np.eye(2))
+
+
+def test_build_Q_matrix():
+    file_path = "tests/Test GCNs/full_nk.gcn"
+    model = model_from_gcn(file_path, verbose=False, on_unused_parameters="ignore")
+    shocks = model.shocks
+
+    # From std
+    Q = build_Q_matrix(
+        model_shocks=shocks,
+        shock_std=10,
+    )
+
+    assert_allclose(Q, np.eye(4) * 100)
+
+    # From dictionary
+    Q = build_Q_matrix(
+        model_shocks=shocks,
+        shock_std_dict={
+            "epsilon_R": 0.1,
+            "epsilon_pi": 0.2,
+            "epsilon_Y": 0.3,
+            "epsilon_preference": 0.4,
+        },
+    )
+    # shocks get stored alphabetically (capitals first)
+    expected_Q = np.diag(np.array([0.1, 0.3, 0.2, 0.4]) ** 2)
+    assert_allclose(Q, expected_Q)
+
+    # From cov
+    L = np.random.normal(size=(4, 4))
+    cov = L @ L.T
+
+    Q = build_Q_matrix(
+        model_shocks=shocks,
+        shock_cov_matrix=cov,
+    )
+
+    assert_allclose(Q, cov)
+
+
+def test_build_Q_matrix_from_dict():
+    file_path = "full_nk.gcn"
+    model = load_and_cache_model(file_path, "numpy", use_jax=JAX_INSTALLED)
+    shocks = model.shocks
+
+    L = np.random.normal(size=(4, 4))
+    cov = L @ L.T
+
+    Q = build_Q_matrix(
+        model_shocks=shocks,
+        shock_cov_matrix=cov,
+    )
+
+    assert_allclose(Q, cov)
+
+
+def test_compute_stationary_covariance_warns_on_partial_specification(caplog):
+    model = load_and_cache_model("rbc_linearized.gcn", "numpy", use_jax=JAX_INSTALLED)
+    T, R = model.solve_model(solver="gensys", verbose=False)
+
+    stationary_covariance_matrix(model, T, shock_std=0.1, verbose=False)
+    messages = caplog.messages
+    assert messages[-1].startswith("Passing only one of T or R will still trigger")
+
+
+@pytest.mark.parametrize(
+    "gcn_file",
+    [
+        "one_block_1_ss.gcn",
+        "open_rbc.gcn",
+        "full_nk.gcn",
+        "rbc_linearized.gcn",
+    ],
+)
+def test_compute_stationary_covariance(caplog, gcn_file):
+    model = load_and_cache_model(gcn_file, backend="numpy", use_jax=JAX_INSTALLED)
+    T, R = model.solve_model(solver="gensys", verbose=False)
+    n_variables, n_shocks = R.shape
+
+    Sigma = stationary_covariance_matrix(model, T, R, shock_std=0.1, return_df=False)
+    assert len(caplog.messages) == 0
+    assert Sigma.shape == (n_variables, n_variables)
+
+    assert_allclose(Sigma, Sigma.T, atol=1e-8)
+    assert all(x > 0 for x in np.diagonal(Sigma))
+
+    # Check for PSD by getting the closest PSD matrix (setting negative eigenvalues to zero) then
+    # checking if the result is close to the original.
+    eigvals, eigvecs = np.linalg.eig(Sigma)
+    eigvals = np.where(eigvals < 0, 0, eigvals)
+    Sigma_psd = eigvecs @ np.diag(eigvals) @ eigvecs.T
+    assert_allclose(Sigma, Sigma_psd, atol=1e-8)
+
+
+@pytest.mark.parametrize(
+    "gcn_file",
+    [
+        "one_block_1_ss.gcn",
+        "open_rbc.gcn",
+        "full_nk.gcn",
+        "rbc_linearized.gcn",
+    ],
+)
+def test_autocovariance_matrix(caplog, gcn_file):
+    model = load_and_cache_model(gcn_file, backend="numpy", use_jax=JAX_INSTALLED)
+
+    shocks = model.shocks
+    shock_eqs = [eq for eq in model.equations if any(s in eq.atoms() for s in shocks)]
+
+    for eq in shock_eqs:
+        atoms = eq.atoms()
+        shock = next(x for x in atoms if x in shocks)
+        if shock.base_name in ["epsilon_R", "epsilon_pi"]:
+            # These aren't a normal AR(1) shocks, so we skip them
+            continue
+
+        state = next(x for x in atoms if x in model.variables)
+        state_idx = model.variables.index(state)
+
+        rho = next(x for x in atoms if x in model.params)
+        rho_value = np.random.beta(10, 1)
+
+        # The autocorrelation of the AR(1) states decay at rate rho ** t
+        # Other autocovarainces are more complex, but this one is easy to check
+        autocorr = autocorrelation_matrix(
+            model,
+            shock_std=0.1,
+            solver="gensys",
             verbose=False,
-            compute_sampler_stats=False,
+            return_xr=False,
+            **{rho.name: rho_value},
         )
 
-        self.assertIsNotNone(idata)
-
-        # Check posterior sampling. It should be its own test, but I want to minimize expensive model fitting calls
-        posterior = az.extract(idata, "posterior")
-        conditional_posterior = simulate_trajectories_from_posterior(
-            self.model, posterior, n_samples=10, n_simulations=10, simulation_length=10
+        assert_allclose(
+            autocorr[:, state_idx, state_idx],
+            rho_value ** np.arange(10),
+            atol=1e-8,
+            rtol=1e-8,
+            err_msg=f"Error computing {state} autocovariance in {gcn_file}",
         )
 
-        self.assertIsNotNone(conditional_posterior)
 
-    def test_fit_model_raises_on_stochastic_singularity(self):
-        T = 100
-        n_simulations = 1
-
-        # Draw from shock prior
-        data = self.model.simulate(simulation_length=T, n_simulations=n_simulations)
+def setup_cov_arguments(argument, n_shocks, model):
+    shock_std = None
+    shock_dict = None
+    shock_cov_matrix = None
+    if argument == "shock_std":
+        shock_std = 0.1
+    elif argument == "shock_std_dict":
+        shock_dict = {shock.base_name: 0.1 for shock in model.shocks}
+    elif argument == "shock_cov_matrix":
+        shock_cov_matrix = np.eye(n_shocks) * 0.1**2
+
+    return shock_std, shock_dict, shock_cov_matrix
+
+
+@pytest.mark.parametrize(
+    "shock_size",
+    [
+        0.1,
+        np.array([0.1, 0.1]),
+        {"epsilon_A": 0.1, "epsilon_B": 0.1},
+        {"epsilon_B": 0.1},
+    ],
+    ids=["single_float", "array", "dict", "partial_dict"],
+)
+@pytest.mark.parametrize(
+    "return_individual_shocks", [True, False], ids=["individual_shocks", "joint_shocks"]
+)
+def test_irf_from_shock_size(shock_size, return_individual_shocks):
+    file_path = "one_block_1_ss_2shock.gcn"
+    model = load_and_cache_model(file_path, backend="numpy", use_jax=JAX_INSTALLED)
+    T, R = model.solve_model(solver="gensys", verbose=False)
+    n_variables, n_shocks = R.shape
+
+    irf = impulse_response_function(
+        model,
+        T,
+        R,
+        simulation_length=1000,
+        shock_size=shock_size,
+        return_individual_shocks=return_individual_shocks,
+    )
+
+    assert "time" in irf.coords
+    assert "variable" in irf.coords
+
+    if return_individual_shocks:
+        assert "shock" in irf.coords
+        if isinstance(shock_size, dict):
+            assert set(irf.coords["shock"].values) == set(shock_size.keys())
+    else:
+        assert "shock" not in irf.coords
+
+    assert len(irf.coords["time"]) == 1000
+    assert len(irf.coords["variable"]) == n_variables
+
+    # After 1000 steps the shocks should have mostly died out
+    assert np.all(np.abs(irf.isel(time=-1).values) < 1e-3)
+
+    n_test_shocks = 1 if isinstance(shock_size, float | int) else len(shock_size)
+    if (n_shocks > 1) and (n_test_shocks > 1) and return_individual_shocks:
+        assert not np.allclose(
+            irf.sel(shock="epsilon_A").values, irf.sel(shock="epsilon_B").values
+        )
 
-        # Only Y is observed
-        data = data.droplevel(axis=1, level=1).T[["C", "K"]]
 
-        with self.assertRaises(ValueError):
-            self.model.fit(
-                data,
-                filter_type="univariate",
-                draws=36,
-                n_walkers=36,
-                return_inferencedata=True,
-                burn_in=0,
-                verbose=False,
-                compute_sampler_stats=False,
-            )
+@pytest.mark.parametrize(
+    "return_individual_shocks", [True, False], ids=["individual_shocks", "joint_shocks"]
+)
+@pytest.mark.parametrize("n_shocks", [1, 2], ids=["single_shock", "two_shocks"])
+def test_irf_from_trajectory(return_individual_shocks, n_shocks):
+    file_path = "one_block_1_ss_2shock.gcn"
+    model = load_and_cache_model(file_path, backend="numpy", use_jax=JAX_INSTALLED)
+    T, R = model.solve_model(solver="gensys", verbose=False)
+    n_variables, n_shocks = R.shape
+
+    shock_trajectory = np.zeros((1000, n_shocks))
+    for i in range(n_shocks):
+        shock_trajectory[0, i] = 0.1
+
+    irf = impulse_response_function(
+        model,
+        T,
+        R,
+        simulation_length=1000,
+        shock_trajectory=shock_trajectory,
+        return_individual_shocks=return_individual_shocks,
+    )
+
+    assert "time" in irf.coords
+    assert "variable" in irf.coords
+
+    if return_individual_shocks:
+        assert "shock" in irf.coords
+    else:
+        assert "shock" not in irf.coords
+
+    assert len(irf.coords["time"]) == 1000
+    assert len(irf.coords["variable"]) == n_variables
+    assert np.all(np.abs(irf.isel(time=-1).values) < 1e-3)
+
+    if (n_shocks == 2) and return_individual_shocks:
+        assert not np.allclose(
+            irf.sel(shock="epsilon_A").values, irf.sel(shock="epsilon_B").values
+        )
 
 
-if __name__ == "__main__":
-    unittest.main()
+@pytest.mark.parametrize(
+    "gcn_file",
+    [
+        "one_block_1_ss.gcn",
+        "open_rbc.gcn",
+        "full_nk.gcn",
+    ],
+)
+@pytest.mark.parametrize(
+    "argument", ["shock_std", "shock_std_dict", "shock_cov_matrix"]
+)
+def test_simulate(gcn_file, argument):
+    model = load_and_cache_model(gcn_file, backend="numpy", use_jax=JAX_INSTALLED)
+    T, R = model.solve_model(solver="gensys", verbose=False)
+    n_variables, n_shocks = R.shape
+
+    n_simulations = 2000
+    simulation_length = 2000
+
+    shock_std, shock_std_dict, shock_cov_matrix = setup_cov_arguments(
+        argument, n_shocks, model
+    )
+
+    data = simulate(
+        model,
+        T,
+        R,
+        simulation_length=simulation_length,
+        n_simulations=n_simulations,
+        shock_std=shock_std,
+        shock_std_dict=shock_std_dict,
+        shock_cov_matrix=shock_cov_matrix,
+    )
+
+    assert data.shape == (n_simulations, simulation_length, n_variables)
+
+    # Check that the simulated covariance matrix is at least strong correlated with the stationary covariance matrix
+    # across many trajectories
+    Sigma = stationary_covariance_matrix(model, T, R, shock_std=0.1, return_df=False)
+    sigma = np.cov(data.isel(time=-1).values.T)
+
+    corr = np.corrcoef(np.r_[Sigma.ravel(), sigma.ravel()])
+    assert abs(corr) > 0.99
+
+    assert_allclose(np.diag(Sigma), np.diag(sigma), rtol=0.1)
+
+
+def test_objective_with_complex_discount_factor():
+    gcn_file = "rbc_firm_capital.gcn"
+    model = load_and_cache_model(gcn_file, backend="numpy", use_jax=JAX_INSTALLED)
+
+    ss_res = model.steady_state(
+        verbose=False, how="minimize", optimizer_kwargs={"method": "Newton-CG"}
+    )
+    assert ss_res.success
+
+    bk_success = check_bk_condition(
+        *model.linearize_model(steady_state=ss_res),
+        return_value="bool",
+        verbose=False,
+    )
+    assert bk_success
+
+    gcn_file = "rbc_firm_capital_comparison.gcn"
+    model_2 = load_and_cache_model(gcn_file, backend="numpy", use_jax=JAX_INSTALLED)
+
+    ss_res_2 = model_2.steady_state(verbose=False)
+    assert ss_res_2.success
+
+    assert_allclose(ss_res["Y_ss"], ss_res_2["Y_ss"], rtol=1e-8, atol=1e-8)
+    assert_allclose(ss_res["K_ss"], ss_res_2["K_ss"], rtol=1e-8, atol=1e-8)
+    assert_allclose(ss_res["L_ss"], ss_res_2["L_ss"], rtol=1e-8, atol=1e-8)
+    assert_allclose(ss_res["I_ss"], ss_res_2["I_ss"], rtol=1e-8, atol=1e-8)
diff --git a/tests/test_model_loaders.py b/tests/test_model_loaders.py
new file mode 100644
index 0000000..6ed55d7
--- /dev/null
+++ b/tests/test_model_loaders.py
@@ -0,0 +1,432 @@
+import os
+
+from collections import defaultdict
+from importlib.util import find_spec
+
+import numpy as np
+import pytest
+import sympy as sp
+
+from gEconpy.exceptions import (
+    DuplicateParameterError,
+    ExtraParameterError,
+    OrphanParameterError,
+)
+from gEconpy.model.build import model_from_gcn
+from gEconpy.model.compile import BACKENDS
+from gEconpy.model.parameters import compile_param_dict_func
+from gEconpy.model.steady_state import (
+    compile_model_ss_functions,
+    system_to_steady_state,
+)
+from gEconpy.parser.constants import DEFAULT_ASSUMPTIONS
+from gEconpy.parser.file_loaders import (
+    block_dict_to_model_primitives,
+    block_dict_to_param_dict,
+    block_dict_to_variables_and_shocks,
+    gcn_to_block_dict,
+    load_gcn,
+    parsed_model_to_data,
+    simplify_provided_ss_equations,
+    validate_results,
+)
+from gEconpy.parser.gEcon_parser import preprocess_gcn
+
+JAX_INSTALLED = find_spec("jax") is not None
+
+GCN_ROOT = "tests/Test GCNs"
+
+TEST_GCN_FILES = [
+    "one_block_1.gcn",
+    "one_block_1_ss.gcn",
+    "one_block_2.gcn",
+    "full_nk.gcn",
+]
+
+TEST_NAMES = ["one_block", "one_block_ss", "one_block_2", "full_nk"]
+
+EXPECTED_BLOCKS = {
+    "one_block": ["HOUSEHOLD"],
+    "one_block_ss": ["HOUSEHOLD"],
+    "one_block_2": ["HOUSEHOLD"],
+    "full_nk": [
+        "HOUSEHOLD",
+        "WAGE_SETTING",
+        "WAGE_EVOLUTION",
+        "PREFERENCE_SHOCKS",
+        "FIRM",
+        "TECHNOLOGY_SHOCKS",
+        "FIRM_PRICE_SETTING_PROBLEM",
+        "PRICE_EVOLUTION",
+        "MONETARY_POLICY",
+        "EQUILIBRIUM",
+    ],
+}
+
+nk_assumptions = defaultdict(lambda: DEFAULT_ASSUMPTIONS)
+nk_other = {
+    "TC": {"real": True, "negative": True},
+    "delta": {"real": True, "positive": True},
+    "beta": {"real": True, "positive": True},
+    "sigma_C": {"real": True, "positive": True},
+    "sigma_L": {"real": True, "positive": True},
+    "gamma_I": {"real": True, "positive": True},
+    "phi_H": {"real": True, "positive": True},
+    "shock_technology": {"real": True, "positive": True},
+    "shock_preference": {"real": True, "positive": True},
+    "pi": {"real": True, "positive": True},
+    "pi_star": {"real": True, "positive": True},
+    "pi_obj": {"real": True, "positive": True},
+    "r": {"real": True, "positive": True},
+    "r_G": {"real": True, "positive": True},
+    "mc": {"real": True, "positive": True},
+    "w": {"real": True, "positive": True},
+    "w_star": {"real": True, "positive": True},
+    "Y": {"real": True, "positive": True},
+    "C": {"real": True, "positive": True},
+    "I": {"real": True, "positive": True},
+    "K": {"real": True, "positive": True},
+    "L": {"real": True, "positive": True},
+}
+
+nk_assumptions.update(nk_other)
+
+one_block_2_assumptions = defaultdict(lambda: DEFAULT_ASSUMPTIONS)
+one_2_other = {
+    "Y": {"real": True, "positive": True},
+    "C": {"real": True, "positive": True},
+    "I": {"real": True, "positive": True},
+    "K": {"real": True, "positive": True},
+    "L": {"real": True, "positive": True},
+    "A": {"real": True, "positive": True},
+    "theta": {"real": True, "positive": True},
+    "beta": {"real": True, "positive": True},
+    "delta": {"real": True, "positive": True},
+    "tau": {"real": True, "positive": True},
+    "rho": {"real": True, "positive": True},
+    "alpha": {"real": True, "positive": True},
+}
+one_block_2_assumptions.update(one_2_other)
+
+EXPECTED_ASSUMPTIONS = {
+    "one_block": defaultdict(lambda: DEFAULT_ASSUMPTIONS),
+    "one_block_ss": defaultdict(lambda: DEFAULT_ASSUMPTIONS),
+    "one_block_2": one_block_2_assumptions,
+    "full_nk": nk_assumptions,
+}
+
+EXPECTED_OPTIONS = {
+    "one_block": {},
+    "one_block_ss": {"output logfile": False, "output LaTeX": False},
+    "one_block_2": {"output logfile": False, "output LaTeX": False},
+    "full_nk": {
+        "output logfile": True,
+        "output LaTeX": True,
+        "output LaTeX landscape": True,
+    },
+}
+
+EXPECTED_TRYREDUCE = {
+    "one_block": [],
+    "one_block_ss": ["C[]"],
+    "one_block_2": ["C[]"],
+    "full_nk": [],
+}
+
+EXPECTED_SS_LEN = {"one_block": 0, "one_block_ss": 9, "one_block_2": 0, "full_nk": 25}
+
+EXPECTED_VARIABLES = {
+    "one_block": ["A", "C", "K", "U", "lambda"],
+    "one_block_ss": ["C", "L", "I", "K", "Y", "U", "A", "lambda", "q", "lambda__H_1"],
+    "one_block_2": ["Y", "C", "I", "K", "L", "A", "U", "lambda", "q", "lambda__H_1"],
+    "full_nk": [
+        "Y",
+        "C",
+        "I",
+        "K",
+        "B",
+        "U",
+        "L",
+        "w",
+        "r",
+        "r_G",
+        "pi",
+        "Div",
+        "lambda",
+        "q",
+        "w_star",
+        "LHS_w",
+        "RHS_w",
+        "TC",
+        "LHS",
+        "RHS",
+        "shock_preference",
+        "shock_technology",
+        "pi_star",
+        "mc",
+        "pi_obj",
+    ],
+}
+
+EXPECTED_SHOCKS = {
+    "one_block": ["epsilon"],
+    "one_block_ss": ["epsilon"],
+    "one_block_2": ["epsilon"],
+    "full_nk": ["epsilon_preference", "epsilon_Y", "epsilon_pi", "epsilon_R"],
+}
+
+
+@pytest.mark.parametrize(
+    "gcn_path, name", zip(TEST_GCN_FILES, TEST_NAMES), ids=TEST_NAMES
+)
+def test_build_model_blocks(gcn_path, name):
+    raw_model = load_gcn(os.path.join("tests/Test GCNs", gcn_path))
+    parsed_model, prior_dict = preprocess_gcn(raw_model)
+
+    parse_result = parsed_model_to_data(parsed_model, False)
+    blocks, assumptions, options, try_reduce_vars, steady_state_equations = parse_result
+
+    assert list(blocks.keys()) == EXPECTED_BLOCKS[name]
+    assert all(
+        [
+            assumptions[var] == EXPECTED_ASSUMPTIONS[name][var]
+            for var in assumptions.keys()
+        ]
+    )
+    assert options.keys() == EXPECTED_OPTIONS[name].keys()
+    assert all([options[k] == EXPECTED_OPTIONS[name][k] for k in options.keys()])
+    assert try_reduce_vars == EXPECTED_TRYREDUCE[name]
+    assert len(steady_state_equations) == EXPECTED_SS_LEN[name]
+
+
+@pytest.mark.parametrize(
+    "gcn_path, name", zip(TEST_GCN_FILES, TEST_NAMES), ids=TEST_NAMES
+)
+def test_block_dict_to_variables_and_shocks(gcn_path, name):
+    raw_model = load_gcn(os.path.join("tests/Test GCNs", gcn_path))
+    parsed_model, prior_dict = preprocess_gcn(raw_model)
+
+    parse_result = parsed_model_to_data(parsed_model, False)
+    blocks, assumptions, options, try_reduce_vars, steady_state_equations = parse_result
+    variables, shocks = block_dict_to_variables_and_shocks(blocks)
+
+    expected_vars = set(EXPECTED_VARIABLES[name])
+    var_names = {x.base_name for x in variables}
+    assert var_names == expected_vars
+
+    expected_shocks = set(EXPECTED_SHOCKS[name])
+    shock_names = {x.base_name for x in shocks}
+    assert shock_names == expected_shocks
+
+
+@pytest.mark.parametrize(
+    "gcn_file",
+    [
+        "one_block_1_duplicate_params.gcn",
+        "one_block_1_duplicate_params_2.gcn",
+    ],
+    ids=["within_block", "between_blocks"],
+)
+def test_loading_fails_if_duplicate_parameters_in_two_blocks(gcn_file):
+    with pytest.raises(DuplicateParameterError):
+        outputs = gcn_to_block_dict(os.path.join("tests/Test GCNs", gcn_file), True)
+        (
+            block_dict,
+            assumptions,
+            options,
+            tryreduce,
+            steady_state_equations,
+            prior_dict,
+        ) = outputs
+        block_dict_to_model_primitives(block_dict, assumptions, tryreduce, prior_dict)
+
+
+EXPECTED_PARAM_DICT = {
+    "one_block_simple": dict(alpha=0.4, beta=0.99, delta=0.02, rho=0.95, gamma=1.5),
+    "one_block_simple_2": dict(
+        theta=0.357,
+        beta=1 / 1.01,
+        delta=0.02,
+        tau=2,
+        rho=0.95,
+        Theta=0.95 * 1 / 1.01 + 3,
+        zeta=-np.log(0.357),
+    ),
+}
+
+
+@pytest.mark.parametrize(
+    "gcn_path, name",
+    [
+        ("one_block_1.gcn", "one_block_simple"),
+        ("one_block_2.gcn", "one_block_simple_2"),
+    ],
+    ids=["one_block_simple", "one_block_simple_2"],
+)
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+def test_create_parameter_function(gcn_path, name, backend):
+    expected = EXPECTED_PARAM_DICT[name]
+    block_dict, *outputs = gcn_to_block_dict(
+        os.path.join("tests/Test GCNs", gcn_path), True
+    )
+    param_dict = block_dict_to_param_dict(block_dict, "param_dict")
+    deterministic_dict = block_dict_to_param_dict(block_dict, "deterministic_dict")
+
+    f, _ = compile_param_dict_func(param_dict, deterministic_dict, backend)
+
+    inputs = list(param_dict.keys())
+    np.random.shuffle(inputs)
+    shuffled_input_dict = {k: param_dict[k] for k in inputs}
+    output = f(**shuffled_input_dict)
+
+    computed_param_dict = output.to_string().values_to_float()
+
+    for k in expected.keys():
+        np.testing.assert_allclose(
+            computed_param_dict[k], expected[k], err_msg=f"{k} not close to tolerance"
+        )
+
+
+@pytest.mark.parametrize(
+    "backend", ["numpy", "numba", "pytensor"], ids=["numpy", "numba", "pytensor"]
+)
+def test_all_model_functions_return_arrays(backend: BACKENDS):
+    outputs = gcn_to_block_dict(
+        "tests/Test GCNs/one_block_1_ss.gcn", simplify_blocks=True
+    )
+    block_dict, assumptions, options, try_reduce, ss_solution_dict, prior_info = outputs
+
+    (
+        equations,
+        param_dict,
+        calib_dict,
+        deterministic_dict,
+        variables,
+        shocks,
+        param_priors,
+        shock_priors,
+        hyper_priors_final,
+        reduced_vars,
+        singletons,
+    ) = block_dict_to_model_primitives(
+        block_dict,
+        assumptions,
+        try_reduce,
+        prior_info,
+        simplify_tryreduce=True,
+        simplify_constants=True,
+    )
+
+    ss_solution_dict = simplify_provided_ss_equations(ss_solution_dict, variables)
+    steady_state_relationships = [
+        sp.Eq(var, eq) for var, eq in ss_solution_dict.to_sympy().items()
+    ]
+    validate_results(
+        equations,
+        steady_state_relationships,
+        param_dict,
+        calib_dict,
+        deterministic_dict,
+    )
+    steady_state_equations = system_to_steady_state(equations, shocks)
+
+    kwargs = {}
+    if backend == "pytensor":
+        kwargs["mode"] = "JAX" if JAX_INSTALLED else "FAST_RUN"
+    (f_params, f_ss, resid_funcs, error_funcs), cache = compile_model_ss_functions(
+        steady_state_equations,
+        ss_solution_dict,
+        variables,
+        param_dict,
+        deterministic_dict,
+        calib_dict,
+        error_func="squared",
+        backend=backend,
+        **kwargs,
+    )
+
+    f_ss_resid, f_ss_jac = resid_funcs
+    f_ss_error, f_ss_grad, f_ss_hess, f_ss_hessp = error_funcs
+
+    parameters = f_params(**param_dict)
+    ss = f_ss(**parameters)
+    x0 = {var.to_ss().name: 0.8 for var in variables}
+    x0.update(ss)
+    for f in [f_ss_resid, f_ss_jac, f_ss_grad, f_ss_hess]:
+        result = f(**x0, **parameters)
+        assert isinstance(result, np.ndarray)
+
+    result = f_ss_hessp(np.ones(len(variables)), **x0, **parameters)
+    assert isinstance(result, np.ndarray)
+
+
+@pytest.mark.parametrize(
+    "gcn_file",
+    [
+        "one_block_1_ss.gcn",
+        "open_rbc.gcn",
+        "full_nk.gcn",
+    ],
+    ids=["one_block_simple", "open_rbc", "full_nk"],
+)
+def test_load_gcn(gcn_file):
+    from gEconpy.model.model import Model
+
+    mod = model_from_gcn(
+        os.path.join("tests/Test GCNs", gcn_file), simplify_blocks=True, verbose=False
+    )
+    assert isinstance(mod, Model)
+    assert len(mod.shocks) > 0
+    assert len(mod.variables) > 0
+    assert len(mod.equations) > 0
+
+    assert mod.f_params is not None
+
+    assert mod.f_ss is not None
+    assert mod.f_ss_jac is not None
+
+    assert mod.f_ss_resid is not None
+    assert mod.f_ss_error_grad is not None
+    assert mod.f_ss_error_hess is not None
+
+
+def test_loading_fails_if_orphan_parameters():
+    with pytest.raises(OrphanParameterError):
+        model_from_gcn(os.path.join("tests/Test GCNs", "open_rbc_orphan_params.gcn"))
+
+
+def test_loading_fails_if_extra_parameters():
+    with pytest.raises(ExtraParameterError):
+        model_from_gcn(os.path.join("tests/Test GCNs", "open_rbc_extra_params.gcn"))
+
+
+def test_build_report(caplog):
+    model_from_gcn(
+        "tests/Test GCNs/rbc_2_block.gcn",
+        verbose=True,
+        simplify_tryreduce=True,
+        simplify_constants=True,
+        simplify_blocks=True,
+    )
+
+    expected_report = r"""
+                Model Building Complete.
+                Found:
+                    12 equations
+                    12 variables
+                    The following "variables" were defined as constants and have been substituted away:
+                        P_t
+                    1 stochastic shock
+                        0 / 1 has a defined prior.
+                    6 parameters
+                        0 / 6 parameters has a defined prior.
+                    0 parameters to calibrate.
+                    Model appears well defined and ready to proceed to solving."""
+
+    expected_lines = [x.strip() for x in expected_report.strip().split("\n")]
+    found_lines = [x.strip() for x in caplog.messages[-1].strip().split("\n")]
+
+    for line1, line2 in zip(expected_lines, found_lines, strict=True):
+        assert line1 == line2
diff --git a/tests/test_parser.py b/tests/test_parser.py
index 49bddf7..c488a51 100644
--- a/tests/test_parser.py
+++ b/tests/test_parser.py
@@ -1,10 +1,12 @@
 import os
 import unittest
-from collections import defaultdict
+
 from pathlib import Path
 
 import pyparsing
+import pytest
 import sympy as sp
+
 from scipy.stats import invgamma, norm
 
 from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
@@ -15,437 +17,441 @@
 ROOT = Path(__file__).parent.absolute()
 
 
-class ParserDistributionCases(unittest.TestCase):
-    def setUp(self):
-        self.model = file_loaders.load_gcn(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1_w_Distributions.gcn")
-        )
-
-    def test_distribution_extraction_simple(self):
-        test_str = "alpha ~ Normal(0, 1) = 0.5;"
-        line, prior_dict = parse_plaintext.extract_distributions(test_str)
-        self.assertEqual(line, "alpha = 0.5;")
-        self.assertEqual(list(prior_dict.keys()), ["alpha"])
-        self.assertEqual(list(prior_dict.values()), ["Normal(0, 1)"])
+@pytest.fixture
+def model():
+    return file_loaders.load_gcn(os.path.join(ROOT, "Test GCNs/one_block_1_dist.gcn"))
+
+
+def test_distribution_extraction_simple(model):
+    test_str = "alpha ~ Normal(0, 1) = 0.5;"
+    line, prior_dict = parse_plaintext.extract_distributions(test_str)
+    assert line == "alpha = 0.5;"
+    assert list(prior_dict.keys()) == ["alpha"]
+    assert list(prior_dict.values()) == ["Normal(0, 1) = 0.5"]
+
+
+def test_remove_distributions_and_normal_parse(model):
+    parser_output, prior_dict = gEcon_parser.preprocess_gcn(model)
+
+    assert list(prior_dict.keys()) == [
+        "epsilon[]",
+        "alpha",
+        "rho",
+        "gamma",
+        "sigma_epsilon",
+    ]
+    assert list(prior_dict.values()) == (
+        [
+            "N(mean=0, sd=sigma_epsilon)",
+            "Beta(mean=0.5, sd=0.1) = 0.4",
+            "Beta(mean=0.95, sd=0.04) = 0.95",
+            "HalfNormal(sigma=1) = 1.5",
+            "Inv_Gamma(mean=0.1, sd=0.01) = 0.01",
+        ]
+    )
 
-    def test_remove_distributions_and_normal_parse(self):
-        parser_output, prior_dict = gEcon_parser.preprocess_gcn(self.model)
 
-        self.assertEqual(
-            list(prior_dict.keys()),
-            ["epsilon[]", "alpha", "rho", "gamma", "sigma_epsilon"],
-        )
-        self.assertEqual(
-            list(prior_dict.values()),
-            [
-                "N(mean=0, sd=sigma_epsilon)",
-                "Beta(mean=0.5, sd=0.1)",
-                "Beta(mean=0.95, sd=0.04)",
-                "HalfNormal(sigma=1)",
-                "Inv_Gamma(mean=0.1, sd=0.01)",
-            ],
-        )
+def test_remove_comment_line():
+    test_string = """#This is a comment
+                      Y[] = A[] + B[] + C[];"""
+    expected_result = "Y[] = A[] + B[] + C[];"
 
+    parsed_string = parse_plaintext.remove_comments(test_string)
+    assert parsed_string.strip() == expected_result
 
-class ParserTestCases(unittest.TestCase):
-    def test_remove_comment_line(self):
-        test_string = """#This is a comment
-                          Y[] = A[] + B[] + C[];"""
-        expected_result = "Y[] = A[] + B[] + C[];"
 
-        parsed_string = parse_plaintext.remove_comments(test_string)
-        self.assertEqual(parsed_string.strip(), expected_result)
+def test_remove_end_of_line_comment():
+    test_string = "Y[] = A[] + B[] + C[]; #here is a comment at the end"
+    expected_result = "Y[] = A[] + B[] + C[]; "
 
-    def test_remove_end_of_line_comment(self):
-        test_string = "Y[] = A[] + B[] + C[]; #here is a comment at the end"
-        expected_result = "Y[] = A[] + B[] + C[]; "
+    parsed_string = parse_plaintext.remove_comments(test_string)
+    assert parsed_string == expected_result
 
-        parsed_string = parse_plaintext.remove_comments(test_string)
-        self.assertEqual(parsed_string, expected_result)
 
-    def test_add_space_to_equations(self):
-        tests = ["Y[]=K[]^alpha*L[]^(1-alpha):P[];", "K[ss]/L[ss]=3->alpha"]
-        answers = [
+@pytest.mark.parametrize(
+    "test_string, expected_result",
+    [
+        (
+            "Y[]=K[]^alpha*L[]^(1-alpha):P[];",
             "Y[] = K[] ^ alpha * L[] ^ ( 1 - alpha ) : P[] ;",
-            "K[ss] / L[ss] = 3 -> alpha",
-        ]
-
-        for case, expected_result in zip(tests, answers):
-            result = parse_plaintext.add_spaces_around_operators(case)
-            self.assertEqual(result, expected_result)
-
-    def test_add_space_to_expectation_operator(self):
-        test_cases = [
-            "E[][u[] + beta * U[1]];",
-            "AMAZE[-1] + WILDE[] = E[][AMAZE[] + WILDE[1]];",
-            "E[][A[] + 21];",
-            "E[][21 + A[]];",
-            "E[][A[1] + alpha];",
-            "E[][A[1] + alpha] + sigma",
-            "U[] = E[][u[] + beta * U[1]]",
-        ]
-
-        answers = [
-            "E[] [ u[] + beta * U[1] ] ;",
-            "AMAZE[-1] + WILDE[] = E[] [ AMAZE[] + WILDE[1] ] ;",
-            "E[] [ A[] + 21 ] ;",
-            "E[] [ 21 + A[] ] ;",
-            "E[] [ A[1] + alpha ] ;",
-            "E[] [ A[1] + alpha ] + sigma",
-            "U[] = E[] [ u[] + beta * U[1] ]",
-        ]
-        for case, answer in zip(test_cases, answers):
-            result = parse_plaintext.add_spaces_around_expectations(case)
-            result = parse_plaintext.remove_extra_spaces(result)
-            result = parse_plaintext.repair_special_tokens(result)
-
-            self.assertEqual(result, answer)
-
-    def test_parse_gcn(self):
-        test_file = """block HOUSEHOLD
+        ),
+        ("K[ss]/L[ss]=3->alpha", "K[ss] / L[ss] = 3 -> alpha"),
+    ],
+    ids=["equation", "calibration"],
+)
+def test_add_space_to_equations(test_string, expected_result):
+    result = parse_plaintext.add_spaces_around_operators(test_string)
+    assert result == expected_result
+
+
+parse_expectation_tests = [
+    "E[][u[] + beta * U[1]];",
+    "AMAZE[-1] + WILDE[] = E[][AMAZE[] + WILDE[1]];",
+    "E[][A[] + 21];",
+    "E[][21 + A[]];",
+    "E[][A[1] + alpha];",
+    "E[][A[1] + alpha] + sigma",
+    "U[] = E[][u[] + beta * U[1]]",
+]
+
+parse_expectation_expected = [
+    "E[] [ u[] + beta * U[1] ] ;",
+    "AMAZE[-1] + WILDE[] = E[] [ AMAZE[] + WILDE[1] ] ;",
+    "E[] [ A[] + 21 ] ;",
+    "E[] [ 21 + A[] ] ;",
+    "E[] [ A[1] + alpha ] ;",
+    "E[] [ A[1] + alpha ] + sigma",
+    "U[] = E[] [ u[] + beta * U[1] ]",
+]
+
+
+@pytest.mark.parametrize(
+    "test_string, expected_result",
+    zip(parse_expectation_tests, parse_expectation_expected),
+    ids=[
+        "bellman_rhs",
+        "equation",
+        "constant_on_right",
+        "constant_on_left",
+        "variable_in_expectation",
+        "addition",
+        "bellman",
+    ],
+)
+def test_add_space_to_expectation_operator(test_string, expected_result):
+    result = parse_plaintext.add_spaces_around_expectations(test_string)
+    result = parse_plaintext.remove_extra_spaces(result)
+    result = parse_plaintext.repair_special_tokens(result)
+
+    assert result == expected_result
+
+
+def test_parse_gcn():
+    test_file = """block HOUSEHOLD
+    {
+        definitions
         {
-            definitions
-            {
-                u[] = log(C[]) - log(L[]);
-            };
-
-            objective
-            {
-                U[] = u[] + beta * E[][U[1]];
-            };
-
-            controls
-            {
-                C[], L[];
-            };
-
-            constraints
-            {
-                C[] = w[] * L[];
-            };
-
-            calibration
-            {
-                beta = 0.99;
-            };
+            u[] = log(C[]) - log(L[]);
         };
-        """
-
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-        with open(os.path.join(ROOT, "Test Answer Strings/test_parse_gcn.txt")) as file:
-            expected_result = file.read()
-
-        self.assertEqual(parser_output.strip(), expected_result.strip())
-
-    def test_block_extraction(self):
-        test_file = """options
-                        {
-                            output logfile = TRUE;
-                            output LaTeX = TRUE;
-                            output LaTeX landscape = TRUE;
-                        };
-
-                        tryreduce
-                        {
-                            Div[], TC[];
-                        };
-                        """
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-
-        results = gEcon_parser.extract_special_block(parser_output, "options")
-        results.update(gEcon_parser.extract_special_block(parser_output, "tryreduce"))
 
-        self.assertEqual(list(results.keys()), ["options", "tryreduce"])
-        self.assertIsInstance(results["options"], dict)
-        self.assertEqual(
-            list(results["options"].keys()),
-            ["output logfile", "output LaTeX", "output LaTeX landscape"],
-        )
-
-        self.assertEqual(list(results["options"].values()), [True, True, True])
-
-        self.assertEqual(results["tryreduce"], ["Div[]", "TC[]"])
-
-    def test_block_deletion(self):
-        test_file = """options
-                        {
-                            output logfile = TRUE;
-                            output LaTeX = TRUE;
-                            output LaTeX landscape = TRUE;
-                        };
-
-                        tryreduce
-                        {
-                            Div[], TC[];
-                        };
-                        """
-
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-        result = parse_plaintext.delete_block(parser_output, "options")
-
-        self.assertEqual(result.strip(), "tryreduce { Div[], TC[] ; };")
-
-        result = parse_plaintext.delete_block(parser_output, "tryreduce")
-        with open(
-            os.path.join(ROOT, "Test Answer Strings/test_block_deletion.txt")
-        ) as file:
-            expected_result = file.read()
-
-        self.assertEqual(result.strip(), expected_result.strip())
-
-    def test_split_gcn_by_blocks(self):
-        test_file = file_loaders.load_gcn(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn")
-        )
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-
-        with open(
-            os.path.join(ROOT, "Test Answer Strings/test_split_gcn_by_blocks.txt")
-        ) as file:
-            expected_result = file.read()
-
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
-
-        self.assertEqual(
-            list(block_dict.keys()),
-            ["options", "tryreduce", "assumptions", "STEADY_STATE", "HOUSEHOLD"],
-        )
-
-        self.assertIs(block_dict["options"], None)
-        self.assertIs(block_dict["tryreduce"], None)
-        self.assertTrue(isinstance(block_dict["assumptions"], defaultdict))
-
-        self.assertEqual(block_dict["HOUSEHOLD"].strip(), expected_result.strip())
-
-    def test_equation_rebuilding(self):
-        test_eq = "{Y[] = C[] + I[] + G[]; A[] ^ ( ( alpha + 1 ) / alpha ) - B[] / C[] * exp ( L[] ); };"
-
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_eq)
-        parsed_block = (
-            pyparsing.nestedExpr("{", "};").parseString(parser_output).asList()[0]
-        )
-        eqs = gEcon_parser.rebuild_eqs_from_parser_output(parsed_block)
-
-        self.assertEqual(len(eqs), 2)
-        self.assertEqual(
-            " ".join(eqs[0]).strip(), test_eq.split(";")[0].replace("{", "").strip()
-        )
-        self.assertEqual(
-            " ".join(eqs[1]).strip(), test_eq.split(";")[1].replace("};", "").strip()
-        )
-
-    def test_parse_block_to_dict(self):
-        test_eq = "{definitions { u[] = log ( C[] ) + log ( L[] ) ; };"
-        test_eq += "objective { U[] = u[] + beta * E[] [ U[1] ] ; }; };"
+        objective
+        {
+            U[] = u[] + beta * E[][U[1]];
+        };
 
-        block_dict = gEcon_parser.parsed_block_to_dict(test_eq)
-        self.assertEqual(list(block_dict.keys()), ["definitions", "objective"])
-        self.assertEqual(
-            block_dict["definitions"],
-            [["u[]", "=", "log", "(", "C[]", ")", "+", "log", "(", "L[]", ")"]],
-        )
-        self.assertEqual(
-            block_dict["objective"],
-            [["U[]", "=", "u[]", "+", "beta", "*", "E[]", "[", "U[1]", "]"]],
-        )
+        controls
+        {
+            C[], L[];
+        };
 
-        test_file = file_loaders.load_gcn(
-            os.path.join(ROOT, "Test GCNs/Two_Block_RBC_1.gcn")
-        )
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-        block_dict = gEcon_parser.split_gcn_into_block_dictionary(parser_output)
-        household = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
-        firm = gEcon_parser.parsed_block_to_dict(block_dict["FIRM"])
-
-        self.assertEqual(household["controls"], [["K[]", "C[]", "L[]", "I[]"]])
-        self.assertEqual(firm["controls"], [["K[-1]", "L[]"]])
-
-    def test_token_classification(self):
-        tests = [
-            "Y[] = C[] + I[]",
-            "Y[] = A[] * C[] ^ alpha * L[] ^ ( 1 - alpha ) : mc[]",
-            "K[ss] / L[ss] = 3 -> alpha",
-        ]
+        constraints
+        {
+            C[] = w[] * L[];
+        };
 
-        results = [
-            ["variable", "operator", "variable", "operator", "variable"],
-            [
-                "variable",
-                "operator",
-                "variable",
-                "operator",
-                "variable",
-                "operator",
-                "parameter",
-                "operator",
-                "variable",
-                "operator",
-                "operator",
-                "number",
-                "operator",
-                "parameter",
-                "operator",
-                "lagrange_definition",
-                "variable",
-            ],
-            [
-                "variable",
-                "operator",
-                "variable",
-                "operator",
-                "number",
-                "calibration_definition",
-                "parameter",
-            ],
-        ]
+        calibration
+        {
+            beta = 0.99;
+        };
+    };
+    """
 
-        for case, expected_result in zip(tests, results):
-            result = [parse_equations.token_classifier(token) for token in case.split()]
-            self.assertEqual(result, expected_result)
-
-    def test_time_index_extraction(self):
-        tests = [
-            "A[1]",
-            "A[2]",
-            "Happy[10]",
-            "A[-1]",
-            "A[-2]",
-            "HAPPY[-10]",
-            "alpha_1[-1]",
-            "A[ss]",
-        ]
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    with open(os.path.join(ROOT, "Test Answer Strings/test_parse_gcn.txt")) as file:
+        expected_result = file.read()
 
-        results = ["t1", "t2", "t10", "tL1", "tL2", "tL10", "tL1", "ss"]
-
-        for case, expected_result in zip(tests, results):
-            result = parse_equations.extract_time_index(case)
-            self.assertEqual(result, expected_result)
-
-    def test_single_symbol_to_sympy(self):
-        tests = [
-            "A[]",
-            "A[1]",
-            "Happy[10]",
-            "A[-1]",
-            "A[-2]",
-            "HAPPY[-10]",
-            "alpha_1[-1]",
-            "A[ss]",
-            "pi",
-        ]
-        results = [
-            TimeAwareSymbol("A", 0),
-            TimeAwareSymbol("A", 1),
-            TimeAwareSymbol("Happy", 10),
-            TimeAwareSymbol("A", -1),
-            TimeAwareSymbol("A", -2),
-            TimeAwareSymbol("HAPPY", -10),
-            TimeAwareSymbol("alpha_1", -1),
-            TimeAwareSymbol("A", 0).to_ss(),
-            sp.Symbol("pi"),
-        ]
+    assert parser_output.strip() == expected_result.strip()
 
-        for case, expected_result in zip(tests, results):
-            result = parse_equations.single_symbol_to_sympy(case)
-            self.assertEqual(expected_result, result)
 
-    def test_sympy_rename_time_index(self):
-        x_t, x_t1, x_tL1, x_10t, x_tL10, x_ss = sp.symbols(
-            ["x_t", "x_t1", "x_tL1", "x_t10", "x_tL10", "x_ss"]
-        )
-        long_name_t, name_with_num = sp.symbols(
-            ["This_is_a_variable_with_a_super_long_name_t10000", "alpha_1_t10"]
-        )
+def test_block_extraction():
+    test_file = """options
+                    {
+                        output logfile = TRUE;
+                        output LaTeX = TRUE;
+                        output LaTeX landscape = TRUE;
+                    };
 
-        tests = [
-            sp.Eq(x_t, 0),
-            sp.Eq(x_t1, 0),
-            sp.Eq(x_tL1, 0),
-            sp.Eq(x_10t, 0),
-            sp.Eq(x_tL10, 0),
-            sp.Eq(x_ss, 0),
-            sp.Eq(long_name_t, 0),
-            sp.Eq(name_with_num, 0),
-        ]
+                    tryreduce
+                    {
+                        Div[], TC[];
+                    };
+                    """
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+
+    options = gEcon_parser.extract_special_block(parser_output, "options")
+    tryreduce = gEcon_parser.extract_special_block(parser_output, "tryreduce")
+
+    assert isinstance(options, dict)
+    assert list(options.keys()) == [
+        "output logfile",
+        "output LaTeX",
+        "output LaTeX landscape",
+    ]
+
+    assert list(options.values()) == [True, True, True]
+    assert tryreduce == ["Div[]", "TC[]"]
+
+
+def test_block_deletion():
+    test_file = """options
+                    {
+                        output logfile = TRUE;
+                        output LaTeX = TRUE;
+                        output LaTeX landscape = TRUE;
+                    };
+
+                    tryreduce
+                    {
+                        Div[], TC[];
+                    };
+                    """
 
-        answers = [
-            sp.Symbol("x_t"),
-            sp.Symbol("x_{t+1}"),
-            sp.Symbol("x_{t-1}"),
-            sp.Symbol("x_{t+10}"),
-            sp.Symbol("x_{t-10}"),
-            sp.Symbol("x_ss"),
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    result = parse_plaintext.delete_block(parser_output, "options")
+
+    assert result.strip() == "tryreduce { Div[], TC[] ; };"
+
+    result = parse_plaintext.delete_block(parser_output, "tryreduce")
+    with open(
+        os.path.join(ROOT, "Test Answer Strings/test_block_deletion.txt")
+    ) as file:
+        expected_result = file.read()
+
+    assert result.strip() == expected_result.strip()
+
+
+def test_split_gcn_by_blocks():
+    test_file = file_loaders.load_gcn(os.path.join(ROOT, "Test GCNs/one_block_1.gcn"))
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+
+    with open(
+        os.path.join(ROOT, "Test Answer Strings/test_split_gcn_by_blocks.txt")
+    ) as file:
+        expected_result = file.read()
+
+    block_dict, options, tryreduce, assumptions = (
+        gEcon_parser.split_gcn_into_dictionaries(parser_output)
+    )
+
+    assert list(block_dict.keys()) == ["HOUSEHOLD"]
+
+    assert options == {}
+    assert tryreduce == []
+    assert isinstance(assumptions, dict)
+
+    assert block_dict["HOUSEHOLD"].strip() == expected_result.strip()
+
+
+def test_equation_rebuilding():
+    test_eq = "{Y[] = C[] + I[] + G[]; A[] ^ ( ( alpha + 1 ) / alpha ) - B[] / C[] * exp ( L[] ); };"
+
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_eq)
+    parsed_block = (
+        pyparsing.nestedExpr("{", "};").parseString(parser_output).asList()[0]
+    )
+    eqs = gEcon_parser.rebuild_eqs_from_parser_output(parsed_block)
+
+    assert len(eqs) == 2
+    assert " ".join(eqs[0]).strip() == test_eq.split(";")[0].replace("{", "").strip()
+    assert " ".join(eqs[1]).strip() == test_eq.split(";")[1].replace("};", "").strip()
+
+
+def test_parse_block_to_dict():
+    test_eq = "{definitions { u[] = log ( C[] ) + log ( L[] ) ; };"
+    test_eq += "objective { U[] = u[] + beta * E[] [ U[1] ] ; }; };"
+
+    block_dict = gEcon_parser.parsed_block_to_dict(test_eq)
+
+    assert list(block_dict.keys()) == ["definitions", "objective"]
+    assert block_dict["definitions"] == [
+        ["u[]", "=", "log", "(", "C[]", ")", "+", "log", "(", "L[]", ")"]
+    ]
+    assert block_dict["objective"] == [
+        ["U[]", "=", "u[]", "+", "beta", "*", "E[]", "[", "U[1]", "]"]
+    ]
+
+    test_file = file_loaders.load_gcn(os.path.join(ROOT, "Test GCNs/rbc_2_block.gcn"))
+
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    block_dict, *_ = gEcon_parser.split_gcn_into_dictionaries(parser_output)
+    household = gEcon_parser.parsed_block_to_dict(block_dict["HOUSEHOLD"])
+    firm = gEcon_parser.parsed_block_to_dict(block_dict["FIRM"])
+
+    assert household["controls"] == [["K[]", "C[]", "L[]", "I[]"]]
+    assert firm["controls"] == [["K[-1]", "L[]"]]
+
+
+classification_cases = [
+    "Y[] = C[] + I[]",
+    "Y[] = A[] * C[] ^ alpha * L[] ^ ( 1 - alpha ) : mc[]",
+    "K[ss] / L[ss] = 3 -> alpha",
+]
+
+classification_answers = [
+    ["variable", "operator", "variable", "operator", "variable"],
+    [
+        "variable",
+        "operator",
+        "variable",
+        "operator",
+        "variable",
+        "operator",
+        "parameter",
+        "operator",
+        "variable",
+        "operator",
+        "operator",
+        "number",
+        "operator",
+        "parameter",
+        "operator",
+        "lagrange_definition",
+        "variable",
+    ],
+    [
+        "variable",
+        "operator",
+        "variable",
+        "operator",
+        "number",
+        "calibration_definition",
+        "parameter",
+    ],
+]
+
+
+@pytest.mark.parametrize(
+    "case, expected_result",
+    zip(classification_cases, classification_answers),
+    ids=["simple", "complex", "calibration"],
+)
+def test_token_classification(case, expected_result):
+    result = [parse_equations.token_classifier(token) for token in case.split()]
+    assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "case, expected_result",
+    [
+        ("A[1]", "t1"),
+        ("A[2]", "t2"),
+        ("Happy[10]", "t10"),
+        ("A[-1]", "tL1"),
+        ("A[-2]", "tL2"),
+        ("HAPPY[-10]", "tL10"),
+        ("alpha_1[-1]", "tL1"),
+        ("A[ss]", "ss"),
+    ],
+    ids=[
+        "t+1",
+        "t+2",
+        "t+10",
+        "t-1",
+        "t-2",
+        "t-10",
+        "numerical_suffix",
+        "steady_state",
+    ],
+)
+def test_time_index_extraction(case, expected_result):
+    result = parse_equations.extract_time_index(case)
+    assert result == expected_result
+
+
+@pytest.mark.parametrize(
+    "case, expected_result",
+    [
+        ("A[]", TimeAwareSymbol("A", 0)),
+        ("A[1]", TimeAwareSymbol("A", 1)),
+        ("Happy[10]", TimeAwareSymbol("Happy", 10)),
+        ("A[-1]", TimeAwareSymbol("A", -1)),
+        ("A[-2]", TimeAwareSymbol("A", -2)),
+        ("HAPPY[-10]", TimeAwareSymbol("HAPPY", -10)),
+        ("alpha_1[-1]", TimeAwareSymbol("alpha_1", -1)),
+        ("A[ss]", TimeAwareSymbol("A", 0).to_ss()),
+        ("pi", sp.Symbol("pi")),
+    ],
+    ids=[
+        "t",
+        "t+1",
+        "t+10",
+        "t-1",
+        "t-2",
+        "t-10",
+        "numerical_suffix",
+        "steady_state",
+        "parameter",
+    ],
+)
+def test_single_symbol_to_sympy(case, expected_result):
+    result = parse_equations.single_symbol_to_sympy(case)
+    assert expected_result == result
+
+
+@pytest.mark.parametrize(
+    "case, expected_symbol, expected_name, expected_t",
+    [
+        (sp.Eq(sp.Symbol("x_t"), 0), sp.Symbol("x_t"), "x", 0),
+        (sp.Eq(sp.Symbol("x_t1"), 0), sp.Symbol("x_{t+1}"), "x", 1),
+        (sp.Eq(sp.Symbol("x_tL1"), 0), sp.Symbol("x_{t-1}"), "x", -1),
+        (sp.Eq(sp.Symbol("x_t10"), 0), sp.Symbol("x_{t+10}"), "x", 10),
+        (sp.Eq(sp.Symbol("x_tL10"), 0), sp.Symbol("x_{t-10}"), "x", -10),
+        (sp.Eq(sp.Symbol("x_ss"), 0), sp.Symbol("x_ss"), "x", "ss"),
+        (
+            sp.Eq(sp.Symbol("This_is_a_variable_with_a_super_long_name_t10000"), 0),
             sp.Symbol("This_is_a_variable_with_a_super_long_name_{t+10000}"),
+            "This_is_a_variable_with_a_super_long_name",
+            10000,
+        ),
+        (
+            sp.Eq(sp.Symbol("alpha_1_t10"), 0),
             sp.Symbol("alpha_1_{t+10}"),
-        ]
-
-        for case, expected_result in zip(tests, answers):
-            result = parse_equations.rename_time_indexes(case)
-            result = [x for x in result.atoms() if isinstance(x, sp.Symbol)][0]
-            self.assertEqual(result, expected_result)
-
-        eq_test = sp.Eq(
-            x_t + x_t1 - x_tL1 * x_10t**x_tL10, x_ss - long_name_t / name_with_num
-        )
-        eq_answer = sp.Eq(
-            answers[0] + answers[1] - answers[2] * answers[3] ** answers[4],
-            answers[5] - answers[6] / answers[7],
-        )
-
-        self.assertEqual(eq_test, eq_answer)
-
-    def test_convert_to_time_aware_equation(self):
-        x_t, x_t1, x_tL1, x_10t, x_tL10, x_ss = sp.symbols(
-            ["x_{t}", "x_{t+1}", "x_{t-1}", "x_{t+10}", "x_{t-10}", "x_ss"]
-        )
-        long_name_t, name_with_num = sp.symbols(
-            ["This_is_a_variable_with_a_super_long_name_{t+10000}", "alpha_1_{t+10}"]
-        )
-
-        tests = [
-            sp.Eq(x_t, 0),
-            sp.Eq(x_t1, 0),
-            sp.Eq(x_tL1, 0),
-            sp.Eq(x_10t, 0),
-            sp.Eq(x_tL10, 0),
-            sp.Eq(x_ss, 0),
-            sp.Eq(long_name_t, 0),
-            sp.Eq(name_with_num, 0),
-        ]
-
-        answers = [
-            ("x", 0),
-            ("x", 1),
-            ("x", -1),
-            ("x", 10),
-            ("x", -10),
-            ("x", "ss"),
-            ("This_is_a_variable_with_a_super_long_name", 10000),
-            ("alpha_1", 10),
-        ]
-
-        for case, expected_results in zip(tests, answers):
-            result = parse_equations.convert_symbols_to_time_symbols(case)
-            result = [x for x in result.atoms() if isinstance(x, sp.Symbol)][0]
-            self.assertIsInstance(result, TimeAwareSymbol)
-            self.assertEqual(result.base_name, expected_results[0])
-            self.assertEqual(result.time_index, expected_results[1])
-
-    def test_extract_assumption_blocks(self):
-        test_file = """positive
+            "alpha_1",
+            10,
+        ),
+    ],
+    ids=[
+        "t",
+        "t+1",
+        "t-1",
+        "t+10",
+        "t-10",
+        "steady_state",
+        "long_name",
+        "name_with_num",
+    ],
+)
+def test_sympy_to_time_aware(case, expected_symbol, expected_name, expected_t):
+    result = parse_equations.rename_time_indexes(case)
+    result = next(iter([x for x in result.atoms() if isinstance(x, sp.Symbol)]))
+    assert result == expected_symbol
+
+    result = parse_equations.convert_symbols_to_time_symbols(case)
+    result = next(iter([x for x in result.atoms() if isinstance(x, sp.Symbol)]))
+    assert isinstance(result, TimeAwareSymbol)
+    assert result.base_name == expected_name
+    assert result.time_index == expected_t
+
+
+def test_extract_assumption_blocks():
+    test_file = """assumptions
+                    {
+                        positive
                         {
                             C[], K[], L[], A[], lambda[], w[], r[], mc[],
                             beta, delta, sigma_C, sigma_L, alpha;
                         };
-                    """
+                    };
+                """
+
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
 
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    assumptions = gEcon_parser.extract_special_block(parser_output, "assumptions")
+    assert all(v == {"real": True, "positive": True} for v in assumptions.values())
 
-        results = gEcon_parser.extract_special_block(parser_output, "assumptions")
-        self.assertTrue(list(results.keys()), ["positive"])
 
-    def test_invalid_assumptions_raise_error(self):
-        test_file = """assumptions
+def test_invalid_assumptions_raise_error():
+    test_file = """assumptions
         {
             random_words
             {
@@ -453,180 +459,252 @@ def test_invalid_assumptions_raise_error(self):
             };
         };
         """
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-        self.assertRaises(
-            ValueError, gEcon_parser.extract_special_block, parser_output, "assumptions"
-        )
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    with pytest.raises(
+        ValueError, match='Assumption "random_words" is not a valid Sympy assumption.'
+    ):
+        gEcon_parser.extract_special_block(parser_output, "assumptions")
+
 
-    def test_typo_in_assumptions_gives_suggestion(self):
-        test_file = """assumptions
+def test_typo_in_assumptions_gives_suggestion():
+    test_file = """assumptions
+    {
+        possitive
         {
-            possitive
-            {
-                L[], M[], P[];
-            };
+            L[], M[], P[];
         };
-        """
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-        try:
-            gEcon_parser.extract_special_block(parser_output, "assumptions")
-        except ValueError as e:
-            self.assertEqual(
-                str(e),
-                'Assumption "possitive" is not a valid Sympy assumption. Did you mean "positive"?',
-            )
-
-    def test_default_assumptions_set_if_no_assumption_block(self):
-        test_file = """
-            block HOUSEHOLD
+    };
+    """
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    with pytest.raises(
+        ValueError,
+        match='Assumption "possitive" is not a valid Sympy assumption. '
+        'Did you mean "positive"?',
+    ):
+        gEcon_parser.extract_special_block(parser_output, "assumptions")
+
+
+def test_default_assumptions_set_if_no_assumption_block():
+    test_file = """
+        block HOUSEHOLD
+        {
+            identities
             {
-                identities
-                {
-                    C[] = 1;
-                };
+                C[] = 1;
             };
-        """
+        };
+    """
 
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-        results = gEcon_parser.extract_special_block(parser_output, "assumptions")
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    assumptions = gEcon_parser.extract_special_block(parser_output, "assumptions")
 
-        self.assertEqual(results["assumptions"]["C"], DEFAULT_ASSUMPTIONS)
+    assert assumptions["C"] == DEFAULT_ASSUMPTIONS
 
-    def test_defaults_removed_if_conflicting_with_user_spec(self):
-        test_file = """
-            assumptions
+
+def test_defaults_removed_if_conflicting_with_user_spec():
+    test_file = """
+        assumptions
+        {
+            imaginary
             {
-                imaginary
-                {
-                    C[];
-                };
+                C[];
             };
+        };
 
-            block HOUSEHOLD
+        block HOUSEHOLD
+        {
+            identities
             {
-                identities
-                {
-                    C[] = 1;
-                };
+                C[] = 1;
             };
-        """
+        };
+    """
+
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    assumptions = gEcon_parser.extract_special_block(parser_output, "assumptions")
 
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-        results = gEcon_parser.extract_special_block(parser_output, "assumptions")
+    assert "real" not in assumptions["C"].keys()
 
-        self.assertTrue("real" not in results["assumptions"]["C"].keys())
 
-    def test_defaults_given_when_variable_subset_defined(self):
-        test_file = """
-             assumptions
+def test_defaults_given_when_variable_subset_defined():
+    test_file = """
+         assumptions
+         {
+             negative
              {
-                 negative
-                 {
-                     C[];
-                 };
+                 C[];
              };
+         };
 
-             block HOUSEHOLD
+         block HOUSEHOLD
+         {
+             identities
              {
-                 identities
-                 {
-                     C[] = 1;
-                     L[] = 1;
-                 };
+                 C[] = 1;
+                 L[] = 1;
              };
-         """
-
-        parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
-        results = gEcon_parser.extract_special_block(parser_output, "assumptions")
-
-        self.assertEqual(results["assumptions"]["C"], {"real": True, "negative": True})
-        self.assertEqual(results["assumptions"]["L"], DEFAULT_ASSUMPTIONS)
-
-    def test_parse_equations_to_sympy(self):
-        test_eq = "{definitions { u[] = log ( C[] ) + log ( L[] ) ; }; objective { U[] = u[] + beta * E[] [ U[1] ] ; };"
-        test_eq += "calibration { L[ss] / K[ss] = 0.36 -> alpha ; }; };"
-
-        answers = [
-            sp.Eq(
-                TimeAwareSymbol("u", 0),
-                sp.log(TimeAwareSymbol("C", 0)) + sp.log(TimeAwareSymbol("L", 0)),
-            ),
-            sp.Eq(
-                TimeAwareSymbol("U", 0),
-                sp.Symbol("beta") * TimeAwareSymbol("U", 1) + TimeAwareSymbol("u", 0),
-            ),
-            sp.Eq(
-                sp.Symbol("alpha"),
-                TimeAwareSymbol("L", 0).to_ss() / TimeAwareSymbol("K", 0).to_ss()
-                - 0.36,
-            ),
-        ]
-
-        block_dict = gEcon_parser.parsed_block_to_dict(test_eq)
-
-        for i, (component, equations) in enumerate(block_dict.items()):
-            block_dict[component], flags = list(
-                zip(*parse_equations.build_sympy_equations(equations))
-            )
-            eq1 = block_dict[component][0]
-            eq2 = answers[i]
-
-            self.assertEqual(((eq1.lhs - eq1.rhs) - (eq2.lhs - eq2.rhs)).simplify(), 0)
-            self.assertTrue(
-                not flags[0]["is_calibrating"] if i < 2 else flags[0]["is_calibrating"]
-            )
-
-    def test_composite_distribution(self):
-        sigma_epsilon = invgamma(a=20)
-        mu_epsilon = norm(loc=1, scale=0.1)
-
-        d = CompositeDistribution(norm, loc=mu_epsilon, scale=sigma_epsilon)
-        self.assertEqual(d.rv_params["loc"].mean(), mu_epsilon.mean())
-        self.assertEqual(d.rv_params["loc"].std(), mu_epsilon.std())
-        self.assertEqual(d.rv_params["scale"].mean(), sigma_epsilon.mean())
-        self.assertEqual(d.rv_params["scale"].std(), sigma_epsilon.std())
-
-        point_dict = {"loc": 0.1, "scale": 1, "epsilon": 1}
-        self.assertEqual(
-            d.logpdf(point_dict),
-            mu_epsilon.logpdf(0.1)
-            + sigma_epsilon.logpdf(1)
-            + norm(loc=0.1, scale=1).logpdf(1),
+         };
+     """
+
+    parser_output, _ = gEcon_parser.preprocess_gcn(test_file)
+    results = gEcon_parser.extract_special_block(parser_output, "assumptions")
+
+    assert results["C"] == {"real": True, "negative": True}
+    assert results["L"] == DEFAULT_ASSUMPTIONS
+
+
+def test_parse_equations_to_sympy():
+    test_eq = "{definitions { u[] = log ( C[] ) + log ( L[] ) ; }; objective { U[] = u[] + beta * E[] [ U[1] ] ; };"
+    test_eq += "calibration { L[ss] / K[ss] = 0.36 -> alpha ; }; };"
+
+    answers = [
+        sp.Eq(
+            TimeAwareSymbol("u", 0),
+            sp.log(TimeAwareSymbol("C", 0)) + sp.log(TimeAwareSymbol("L", 0)),
+        ),
+        sp.Eq(
+            TimeAwareSymbol("U", 0),
+            sp.Symbol("beta") * TimeAwareSymbol("U", 1) + TimeAwareSymbol("u", 0),
+        ),
+        sp.Eq(
+            sp.Symbol("alpha"),
+            TimeAwareSymbol("L", 0).to_ss() / TimeAwareSymbol("K", 0).to_ss() - 0.36,
+        ),
+    ]
+
+    block_dict = gEcon_parser.parsed_block_to_dict(test_eq)
+
+    for i, (component, equations) in enumerate(block_dict.items()):
+        block_dict[component], flags = list(
+            zip(*parse_equations.build_sympy_equations(equations))
         )
+        eq1 = block_dict[component][0]
+        eq2 = answers[i]
+
+        assert ((eq1.lhs - eq1.rhs) - (eq2.lhs - eq2.rhs)).simplify() == 0
+        assert not flags[0]["is_calibrating"] if i < 2 else flags[0]["is_calibrating"]
 
-    def test_shock_block_with_multiple_distributions(self):
-        test_file = """block TEST_BLOCK
+
+def test_composite_distribution():
+    sigma_epsilon = invgamma(a=20)
+    mu_epsilon = norm(loc=1, scale=0.1)
+
+    d = CompositeDistribution(norm, loc=mu_epsilon, scale=sigma_epsilon)
+    assert d.rv_params["loc"].mean() == mu_epsilon.mean()
+    assert d.rv_params["loc"].std() == mu_epsilon.std()
+    assert d.rv_params["scale"].mean() == sigma_epsilon.mean()
+    assert d.rv_params["scale"].std() == sigma_epsilon.std()
+
+    point_dict = {"loc": 0.1, "scale": 1, "epsilon": 1}
+    assert d.logpdf(point_dict) == mu_epsilon.logpdf(0.1) + sigma_epsilon.logpdf(
+        1
+    ) + norm(loc=0.1, scale=1).logpdf(1)
+
+
+def test_shock_block_with_multiple_distributions():
+    test_file = """block TEST_BLOCK
+                    {
+                        shocks
                         {
-                            shocks
-                            {
-                                epsilon_1[] ~ Normal(mu=0, sd=sigma_1);
-                                epsilon_2[] ~ Normal(mu=0, sd=sigma_2);
-                            };
-                            calibration
-                            {
-                                sigma_1 ~ Invgamma(a=0.1, b=0.2) = 0.1;
-                                sigma_2 ~ Invgamma(a=0.1, b=0.2) = 0.2;
-                            };
+                            epsilon_1[] ~ Normal(mu=0, sd=sigma_1);
+                            epsilon_2[] ~ Normal(mu=0, sd=sigma_2);
                         };
-                        """
+                        calibration
+                        {
+                            sigma_1 ~ Invgamma(a=0.1, b=0.2) = 0.1;
+                            sigma_2 ~ Invgamma(a=0.1, b=0.2) = 0.2;
+                        };
+                    };
+                    """
 
-        parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
+    parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
 
-        self.assertEqual(len(prior_dict), 4)
-        self.assertEqual(
-            list(prior_dict.keys()),
-            ["epsilon_1[]", "epsilon_2[]", "sigma_1", "sigma_2"],
-        )
-        dists = [
-            "Normal(mu=0, sd=sigma_1)",
-            "Normal(mu=0, sd=sigma_2)",
-            "Invgamma(a=0.1, b=0.2)",
-            "Invgamma(a=0.1, b=0.2)",
-        ]
+    assert len(prior_dict) == 4
+    assert list(prior_dict.keys()) == [
+        "epsilon_1[]",
+        "epsilon_2[]",
+        "sigma_1",
+        "sigma_2",
+    ]
+
+    dists = [
+        "Normal(mu=0, sd=sigma_1)",
+        "Normal(mu=0, sd=sigma_2)",
+        "Invgamma(a=0.1, b=0.2) = 0.1",
+        "Invgamma(a=0.1, b=0.2) = 0.2",
+    ]
+
+    for value, d in zip(prior_dict.values(), dists):
+        assert value == d
+
+
+def test_parameters_parsed_with_time_subscripts():
+    test_file = """block SYSTEM_EQUATIONS
+    {
+        identities
+        {
+            #1. Labor supply
+            W[] = sigma * C[] + phi * L[];
+
+            #2. Euler Equation
+            sigma / beta * (E[][C[1]] - C[]) = R_ss * E[][R[1]];
+
+            #3. Law of motion of capital -- Timings have been changed to cause Gensys to fail
+            K[] = (1 - delta) * K[] + delta * I[];
+
+            #4. Production Function -- Timings have been changed to cause Gensys to fail
+            Y[] = A[] + alpha * E[][K[1]] + (1 - alpha) * L[];
 
-        for value, d in zip(prior_dict.values(), dists):
-            self.assertEqual(value, d)
+            #5. Demand for capital
+            R[] = Y[] - K[-1];
 
+            #6. Demand for labor
+            W[] = Y[] - L[];
 
-if __name__ == "__main__":
-    unittest.main()
+            #7. Equlibrium Condition
+            Y_ss * Y[] = C_ss * C[] + I_ss * I[];
+
+            #8. Productivity Shock
+            A[] = rho_A * A[-1] + epsilon_A[];
+
+        };
+
+        shocks
+        {
+            epsilon_A[];
+        };
+
+        calibration
+        {
+            sigma = 2;
+            phi = 1.5;
+            alpha = 0.35;
+            beta = 0.985;
+            delta = 0.025;
+            rho_A = 0.95;
+
+            #P_ss = 1;
+            R_ss = (1 / beta - (1 - delta));
+            W_ss = (1 - alpha) ^ (1 / (1 - alpha)) * (alpha / R_ss) ^ (alpha / (1 - alpha));
+            Y_ss = (R_ss / (R_ss - delta * alpha)) ^ (sigma / (sigma + phi)) *
+                   ((1 - alpha) ^ (-phi) * (W_ss) ^ (1 + phi)) ^ (1 / (sigma + phi));
+            K_ss = alpha * Y_ss / R_ss;
+
+            I_tp1 = delta * K_ss;
+            C_tm1 = Y_ss - I_ss;
+            L_t = (1 - alpha) * Y_ss / W_ss;
+        };
+    };
+    """
+
+    parser_output, prior_dict = gEcon_parser.preprocess_gcn(test_file)
+    block_dict, options, tryreduce, assumptions = (
+        gEcon_parser.split_gcn_into_dictionaries(parser_output)
+    )
+    system = gEcon_parser.parsed_block_to_dict(block_dict["SYSTEM_EQUATIONS"])
+    parser_output = parse_equations.build_sympy_equations(
+        system["calibration"], assumptions
+    )
+
+    for eq, attrs in parser_output:
+        assert not any(isinstance(x, TimeAwareSymbol) for x in eq.atoms())
diff --git a/tests/test_perturbation.py b/tests/test_perturbation.py
new file mode 100644
index 0000000..e9eeac2
--- /dev/null
+++ b/tests/test_perturbation.py
@@ -0,0 +1,243 @@
+from importlib.util import find_spec
+
+import numpy as np
+import pytensor
+import pytensor.tensor as pt
+import pytest
+import sympy as sp
+
+from numpy.testing import assert_allclose
+from pytensor.gradient import DisconnectedType, verify_grad
+
+from gEconpy.model.perturbation import (
+    linearize_model,
+    make_all_variable_time_combinations,
+    override_dummy_wrapper,
+)
+from gEconpy.solvers.cycle_reduction import (
+    cycle_reduction_pt,
+    scan_cycle_reduction,
+    solve_policy_function_with_cycle_reduction,
+)
+from gEconpy.solvers.gensys import gensys_pt, solve_policy_function_with_gensys
+from gEconpy.utilities import eq_to_ss
+from tests.test_model import JAX_INSTALLED
+from tests.utilities.shared_fixtures import load_and_cache_model
+
+JAX_INSTALLED = find_spec("jax") is not None
+
+
+def linearize_method_2(variables, equations, shocks, not_loglin_variables=None):
+    if not_loglin_variables is None:
+        not_loglin_variables = []
+    not_loglin_variables += [x.base_name for x in shocks]
+
+    Fs = []
+    lags, now, leads = make_all_variable_time_combinations(variables)
+
+    for var_group in [lags, now, leads, shocks]:
+        F = []
+        for eq in equations:
+            F_row = []
+            for var in var_group:
+                dydx = sp.powsimp(eq_to_ss(eq.diff(var)))
+                dydx *= 1.0 if var.base_name in not_loglin_variables else var.to_ss()
+                F_row.append(dydx)
+            F.append(F_row)
+        F = sp.Matrix(F)
+        Fs.append(F)
+    return Fs
+
+
+@pytest.mark.parametrize("backend", ["numpy", "numba", "pytensor"])
+def test_variables_to_all_times(backend):
+    mod = load_and_cache_model(
+        "one_block_1.gcn", backend=backend, use_jax=JAX_INSTALLED
+    )
+    variables = mod.variables
+    lags, now, leads = make_all_variable_time_combinations(variables)
+
+    assert set(variables) == set(now)
+    assert all([len(vars) == len(variables) for vars in [lags, now, leads]])
+    for i, var_group in enumerate([lags, now, leads]):
+        t = i - 1
+        assert all([var.time_index == t for var in var_group])
+        assert all([var.set_t(0) in mod.variables for var in var_group])
+
+
+@pytest.mark.parametrize(
+    "gcn_file",
+    ["one_block_1.gcn", "rbc_2_block.gcn", "full_nk.gcn"],
+)
+@pytest.mark.parametrize("backend", ["numpy", "numba", "pytensor"])
+def test_log_linearize_model(gcn_file, backend):
+    mod = load_and_cache_model(gcn_file, backend=backend, use_jax=JAX_INSTALLED)
+    (A, B, C, D), not_loglin_variable = linearize_model(
+        mod.variables, mod.equations, mod.shocks
+    )
+    lags, now, leads = make_all_variable_time_combinations(mod.variables)
+
+    ss_vars = [x.to_ss() for x in mod.variables]
+    ss_shocks = [x.to_ss() for x in mod.shocks]
+    parameters = list(mod.parameters().to_sympy().keys())
+
+    sub_dict = {x.name: 0.8 for x in ss_vars}
+    shock_dict = {x.to_ss().name: 0.0 for x in mod.shocks}
+
+    A2, B2, C2, D2 = linearize_method_2(now, mod.equations, mod.shocks)
+    A22, B22, C22, D22 = linearize_method_2(
+        now,
+        mod.equations,
+        mod.shocks,
+        not_loglin_variables=[x.base_name for x in mod.variables],
+    )
+
+    for i, (M1, M2) in enumerate(
+        zip([A, B, C, D], [(A2, A22), (B2, B22), (C2, C22), (D2, D22)])
+    ):
+        f1 = sp.lambdify(ss_vars + ss_shocks + parameters + [not_loglin_variable], M1)
+        f1 = override_dummy_wrapper(f1, "not_loglin_variable")
+        f2 = sp.lambdify(ss_vars + ss_shocks + parameters, list(M2))
+
+        for loglin_value in [0, 1]:
+            x = f1(
+                **mod.parameters(),
+                **sub_dict,
+                **shock_dict,
+                not_loglin_variable=np.full(len(ss_vars), loglin_value),
+            )
+            x2 = f2(**mod.parameters(), **sub_dict, **shock_dict)
+
+            np.testing.assert_allclose(x, x2[loglin_value])
+
+
+@pytest.mark.parametrize(
+    "gcn_file, state_variables",
+    [
+        ("one_block_1_ss.gcn", ["K", "A"]),
+        ("open_rbc.gcn", ["A", "K", "IIP"]),
+        (
+            "full_nk.gcn",
+            [
+                "K",
+                "C",
+                "I",
+                "Y",
+                "w",
+                "pi_star",
+                "shock_technology",
+                "shock_preference",
+                "pi_obj",
+                "r_G",
+            ],
+        ),
+    ],
+)
+@pytest.mark.parametrize("backend", ["numpy", "numba", "pytensor"])
+def test_solve_policy_function(gcn_file, state_variables, backend):
+    mod = load_and_cache_model(gcn_file, backend=backend, use_jax=JAX_INSTALLED)
+    steady_state_dict = mod.steady_state()
+    A, B, C, D = mod.linearize_model(order=1, steady_state=steady_state_dict)
+
+    gensys_results = solve_policy_function_with_gensys(A, B, C, D, 1e-8)
+    G_1, constant, impact, f_mat, f_wt, y_wt, gev, eu, loose = gensys_results
+
+    state_idxs = [
+        i for i, var in enumerate(mod.variables) if var.base_name in state_variables
+    ]
+    jumper_idxs = [
+        i for i, var in enumerate(mod.variables) if var.base_name not in state_variables
+    ]
+
+    assert not np.allclose(G_1[:, state_idxs], 0.0)
+    assert_allclose(G_1[:, jumper_idxs], 0.0, atol=1e-8, rtol=1e-8)
+
+    n = len(mod.variables)
+    T_gensys = G_1[:n, :][:, :n]
+    R_gensys = impact[:n, :]
+
+    (
+        T,
+        R,
+        result,
+        log_norm,
+    ) = solve_policy_function_with_cycle_reduction(A, B, C, D, 100_000, 1e-16, False)
+
+    assert not np.allclose(T[:, state_idxs], 0.0)
+    assert_allclose(T[:, jumper_idxs], 0.0, atol=1e-8, rtol=1e-8)
+
+    assert_allclose(T_gensys, T, atol=1e-8, rtol=1e-8)
+    assert_allclose(R_gensys, R, atol=1e-8, rtol=1e-8)
+
+
+@pytest.mark.parametrize(
+    "op",
+    [cycle_reduction_pt, scan_cycle_reduction],
+    ids=["cycle_reduction", "scan_cycle_reduction"],
+)
+def test_cycle_reduction_gradients(op):
+    mod = load_and_cache_model("full_nk.gcn", backend="numpy", use_jax=JAX_INSTALLED)
+    A, B, C, D = mod.linearize_model()
+
+    A_pt, B_pt, C_pt, D_pt = (
+        pt.tensor(name=name, shape=x.shape)
+        for name, x in zip(list("ABCD"), [A, B, C, D])
+    )
+
+    T, R, *_ = op(A_pt, B_pt, C_pt, D_pt)
+    T_grad = pt.grad(T.sum(), [A_pt, B_pt, C_pt])
+
+    f = pytensor.function(
+        [A_pt, B_pt, C_pt, D_pt],
+        [T, R, *T_grad],
+        on_unused_input="raise",
+        mode="JAX" if JAX_INSTALLED and op is scan_cycle_reduction else "FAST_RUN",
+    )
+
+    T_np, R_np, A_bar, B_bar, C_bar = f(A, B, C, D)
+
+    resid = A + B @ T_np + C @ T_np @ T_np
+    assert_allclose(resid, 0.0, atol=1e-8, rtol=1e-8)
+
+    def cycle_func(A, B, C, D):
+        T, R, *_ = op(A, B, C, D)
+        return T.sum()
+
+    verify_grad(
+        cycle_func, pt=[A, B, C, D.astype("float64")], rng=np.random.default_rng()
+    )
+
+
+def test_pytensor_gensys():
+    mod = load_and_cache_model("full_nk.gcn", backend="numpy", use_jax=JAX_INSTALLED)
+    A, B, C, D = mod.linearize_model()
+
+    A_pt, B_pt, C_pt, D_pt = (pt.dmatrix(name) for name in list("ABCD"))
+    T1, R1 = cycle_reduction_pt(A_pt, B_pt, C_pt, D_pt)
+    T1_grad = pt.grad(T1.sum(), [A_pt, B_pt, C_pt])
+
+    T2, R2, success = gensys_pt(A_pt, B_pt, C_pt, D_pt, 1e-8)
+    T2_grad = pt.grad(T2.sum(), [A_pt, B_pt, C_pt])
+
+    def gensys_func(A, B, C, D):
+        T, R, _ = gensys_pt(A, B, C, D)
+        return T.sum()
+
+    verify_grad(
+        gensys_func, pt=[A, B, C, D.astype("float64")], rng=np.random.default_rng()
+    )
+
+    f = pytensor.function(
+        [A_pt, B_pt, C_pt, D_pt],
+        [T1, T2, R1, R2, *T1_grad, *T2_grad],
+        on_unused_input="raise",
+        mode="FAST_RUN",
+    )
+
+    T1_np, T2_np, R1_np, R2_np, A_bar_1, B_bar_1, C_bar_1, A_bar_2, B_bar_2, C_bar_2 = (
+        f(A, B, C, D)
+    )
+
+    assert_allclose(A_bar_1, A_bar_2, atol=1e-8, rtol=1e-8)
+    assert_allclose(B_bar_1, B_bar_2, atol=1e-8, rtol=1e-8)
+    assert_allclose(C_bar_1, C_bar_2, atol=1e-8, rtol=1e-8)
diff --git a/tests/test_plotting.py b/tests/test_plotting.py
index 0906167..6b7dd43 100644
--- a/tests/test_plotting.py
+++ b/tests/test_plotting.py
@@ -1,26 +1,30 @@
 import os
 import unittest
+
 from pathlib import Path
 
 import numpy as np
+import pytest
+
 from matplotlib import pyplot as plt
 
-from gEconpy.classes.model import gEconModel
+from gEconpy.model.build import model_from_gcn
+from gEconpy.model.model import (
+    autocorrelation_matrix,
+    check_bk_condition,
+    impulse_response_function,
+    simulate,
+    stationary_covariance_matrix,
+)
 from gEconpy.plotting import (
+    plot_acf,
     plot_covariance_matrix,
     plot_eigenvalues,
+    plot_heatmap,
     plot_irf,
-    plot_prior_solvability,
     plot_simulation,
     prepare_gridspec_figure,
 )
-from gEconpy.plotting.plotting import (
-    plot_acf,
-    plot_corner,
-    plot_heatmap,
-    plot_kalman_filter,
-)
-from gEconpy.sampling import kalman_filter_from_posterior, prior_solvability_check
 
 ROOT = Path(__file__).parent.absolute()
 
@@ -46,16 +50,16 @@ def test_prepare_gridspec_figure_wide(self):
 class TestPlotSimulation(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
-        file_path = os.path.join(ROOT, "Test GCNs/RBC_Linearized.gcn")
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-        cls.data = cls.model.simulate(simulation_length=100, n_simulations=1)
+        file_path = os.path.join(ROOT, "Test GCNs/rbc_linearized.gcn")
+        cls.model = model_from_gcn(file_path, verbose=False)
+        cls.data = simulate(
+            cls.model, simulation_length=100, n_simulations=1000, shock_std=0.1
+        )
 
     def test_plot_simulation_defaults(self):
         fig = plot_simulation(self.data)
 
-        self.assertEqual(len(fig.axes), self.model.n_variables)
+        self.assertEqual(len(fig.axes), len(self.model.variables))
         plt.close()
 
     def test_plot_simulation_vars_to_plot(self):
@@ -69,11 +73,12 @@ def test_var_not_found_raises(self):
             plot_simulation(self.data, vars_to_plot=["Y", "C", "Invalid"])
         error_msg = error.exception
         self.assertEqual(str(error_msg), "Invalid not found among model variables.")
+        plt.close()
 
     def test_plot_simulation_with_ci(self):
         fig = plot_simulation(self.data, ci=0.95)
 
-        self.assertEqual(len(fig.axes), self.model.n_variables)
+        self.assertEqual(len(fig.axes), len(self.model.variables))
         plt.close()
 
     def test_plot_simulation_aesthetic_params(self):
@@ -81,144 +86,112 @@ def test_plot_simulation_aesthetic_params(self):
             self.data, cmap="YlGn", figsize=(14, 4), dpi=100, fill_color="brickred"
         )
 
-        self.assertEqual(len(fig.axes), self.model.n_variables)
+        self.assertEqual(len(fig.axes), len(self.model.variables))
         self.assertEqual(fig.get_dpi(), 100)
         self.assertEqual(fig.get_figwidth(), 14)
         self.assertEqual(fig.get_figheight(), 4)
-
         plt.close()
 
 
-class TestIRFPlot(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        file_path = os.path.join(ROOT, "Test GCNs/Full_New_Keyensian.gcn")
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-        cls.irf = cls.model.impulse_response_function(
-            simulation_length=100, shock_size=0.1
-        )
+@pytest.fixture(scope="session")
+def irf_setup():
+    file_path = os.path.join(ROOT, "Test GCNs/full_nk.gcn")
 
-    def test_plot_irf_defaults(self):
-        fig = plot_irf(self.irf)
+    model = model_from_gcn(file_path, verbose=False)
+    model.steady_state(verbose=False)
+    model.solve_model(verbose=False)
+    irf = impulse_response_function(
+        model,
+        simulation_length=100,
+        shock_size=0.1,
+        return_individual_shocks=True,
+    )
 
-        self.assertEqual(len(fig.axes), self.model.n_variables)
-        self.assertEqual(len(fig.axes[0].get_lines()), self.model.n_shocks)
-        plt.close()
+    return model, irf
 
-    def test_plot_irf_one_shock(self):
-        with self.assertRaises(ValueError):
-            fig = plot_irf(self.irf, shocks_to_plot="epsilon_A")
 
-        fig = plot_irf(self.irf, shocks_to_plot=["epsilon_Y"])
-        self.assertEqual(len(fig.axes), self.model.n_variables)
-        self.assertEqual(len(fig.axes[0].get_lines()), 1)
-        plt.close()
+def test_plot_irf_defaults(irf_setup):
+    model, irf = irf_setup
+    fig = plot_irf(irf, legend=True)
 
-    def test_plot_irf_one_variable(self):
-        with self.assertRaises(ValueError):
-            fig = plot_irf(self.irf, vars_to_plot="Y")
+    assert len(fig.axes) == len(model.variables)
+    assert len(fig.axes[0].get_lines()) == len(model.shocks)
 
-        fig = plot_irf(self.irf, vars_to_plot=["Y"])
-        self.assertEqual(len(fig.axes), 1)
-        self.assertEqual(len(fig.axes[0].get_lines()), self.model.n_shocks)
-        plt.close()
+    plt.close()
 
-    def test_var_not_found_raises(self):
-        with self.assertRaises(ValueError) as error:
-            plot_irf(self.irf, vars_to_plot=["Y", "C", "Invalid"])
-        error_msg = error.exception
-        self.assertEqual(
-            str(error_msg), "Invalid not found among simulated impulse responses."
-        )
-        plt.close()
 
-    def test_shock_not_found_raises(self):
-        with self.assertRaises(ValueError) as error:
-            plot_irf(
-                self.irf,
-                vars_to_plot=["Y", "C"],
-                shocks_to_plot=["epsilon_Y", "Invalid"],
-            )
-        error_msg = error.exception
-        self.assertEqual(
-            str(error_msg),
-            "Invalid not found among shocks used in impulse response data.",
-        )
+@pytest.mark.parametrize(
+    "shocks_to_plot", ["epsilon_Y", ["epsilon_Y"]], ids=["str", "list"]
+)
+def test_plot_irf_one_shock(irf_setup, shocks_to_plot):
+    model, irf = irf_setup
+    fig = plot_irf(irf, shocks_to_plot=shocks_to_plot)
 
-    def test_legend(self):
-        fig = plot_irf(
-            self.irf, vars_to_plot=["Y", "C"], shocks_to_plot=["epsilon_Y"], legend=True
-        )
-        self.assertIsNotNone(fig.axes[0].get_legend())
-        self.assertIsNone(fig.axes[1].get_legend())
-        plt.close()
+    assert len(fig.axes) == len(model.variables)
+    assert len(fig.axes[0].get_lines()) == 1
 
+    plt.close()
 
-class TestPriorSolvabilityPlot(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        file_path = os.path.join(
-            ROOT, "Test GCNs/One_Block_Simple_1_w_Distributions.gcn"
-        )
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-        cls.data = prior_solvability_check(cls.model, n_samples=1_000)
 
-    def test_plot_with_defaults(self):
-        fig = plot_prior_solvability(self.data)
-
-        n_priors = len(self.model.param_priors)
-        self.assertTrue(len(fig.axes) == n_priors**2)
-        plot_idxs = np.arange(n_priors**2).reshape((n_priors, n_priors))
-        upper_idxs = plot_idxs[np.triu_indices_from(plot_idxs, 1)]
-        lower_idxs = plot_idxs[np.tril_indices_from(plot_idxs)]
-        for idx in upper_idxs:
-            self.assertTrue(not fig.axes[idx].get_visible())
-        for idx in lower_idxs:
-            self.assertTrue(fig.axes[idx].get_visible())
-        plt.close()
+def test_plot_irf_one_variable(irf_setup):
+    model, irf = irf_setup
+    fig = plot_irf(irf, vars_to_plot="Y")
 
-    def test_plot_with_vars_to_plot(self):
-        fig = plot_prior_solvability(self.data, params_to_plot=["alpha", "gamma"])
-        n_priors = 2
-
-        self.assertTrue(len(fig.axes) == n_priors**2)
-        plot_idxs = np.arange(n_priors**2).reshape((n_priors, n_priors))
-        upper_idxs = plot_idxs[np.triu_indices_from(plot_idxs, 1)]
-        lower_idxs = plot_idxs[np.tril_indices_from(plot_idxs)]
-        for idx in upper_idxs:
-            self.assertTrue(not fig.axes[idx].get_visible())
-        for idx in lower_idxs:
-            self.assertTrue(fig.axes[idx].get_visible())
-        plt.close()
+    assert len(fig.axes) == 1
+    assert len(fig.axes[0].get_lines()) == len(model.shocks)
 
-    def test_raises_if_param_not_found(self):
-        with self.assertRaises(ValueError) as error:
-            plot_prior_solvability(self.data, params_to_plot=["alpha", "beta"])
+    plt.close()
 
-        msg = str(error.exception)
-        self.assertEqual(
-            msg, 'Cannot plot parameter "beta", it was not found in the provided data.'
+
+def test_plot_irf_raises_if_var_not_found(irf_setup):
+    model, irf = irf_setup
+
+    with pytest.raises(
+        ValueError, match="Invalid not found among simulated impulse responses."
+    ):
+        plot_irf(irf, vars_to_plot=["Y", "C", "Invalid"])
+
+    plt.close()
+
+
+def test_plot_irf_raises_if_shock_not_found(irf_setup):
+    model, irf = irf_setup
+
+    with pytest.raises(
+        ValueError,
+        match="Invalid not found among shocks used in impulse response data.",
+    ):
+        plot_irf(
+            irf,
+            vars_to_plot=["Y", "C"],
+            shocks_to_plot=["epsilon_Y", "Invalid"],
         )
+    plt.close()
+
+
+def test_plot_irf_legend(irf_setup):
+    model, irf = irf_setup
+
+    fig = plot_irf(
+        irf, vars_to_plot=["Y", "C"], shocks_to_plot=["epsilon_Y"], legend=True
+    )
+    assert all(axis.get_legend() is None for axis in fig.axes)
+    assert len(fig.figure.legends) == 1
+    plt.close()
 
 
 class TestPlotEigenvalues(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
-        file_path = os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn")
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
+        file_path = os.path.join(ROOT, "Test GCNs/one_block_1.gcn")
+        cls.model = model_from_gcn(file_path, verbose=False)
 
     def test_plot_with_defaults(self):
         fig = plot_eigenvalues(self.model)
         from matplotlib.collections import PathCollection
 
         scatter_points = fig.axes[0].findobj(PathCollection)[0].get_offsets().data
-        data = self.model.check_bk_condition(return_value="df", verbose=False)
+        data = check_bk_condition(self.model, return_value="dataframe", verbose=False)
 
         n_finite = (data["Modulus"] < 1.5).sum()
         self.assertEqual(n_finite, scatter_points.shape[0])
@@ -236,12 +209,10 @@ def test_plot_with_aesthetic_params(self):
 class TestPlotCovarianceMatrix(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
-        file_path = os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn")
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-        cls.cov_matrix = cls.model.compute_stationary_covariance_matrix(
-            shock_cov_matrix=np.eye(1) * 0.01
+        file_path = os.path.join(ROOT, "Test GCNs/one_block_1.gcn")
+        cls.model = model_from_gcn(file_path, verbose=False)
+        cls.cov_matrix = stationary_covariance_matrix(
+            cls.model, shock_cov_matrix=np.eye(1) * 0.01, return_df=True, verbose=False
         )
 
     def test_plot_with_defaults(self):
@@ -272,22 +243,26 @@ def test_heatmap_kwargs(self):
 class TestPlotACF(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
-        file_path = os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn")
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-        cls.acf = cls.model.compute_autocorrelation_matrix(
-            shock_cov_matrix=np.eye(1) * 0.01
+        file_path = os.path.join(ROOT, "Test GCNs/one_block_1.gcn")
+        cls.model = model_from_gcn(file_path, verbose=False)
+        cls.acf = autocorrelation_matrix(
+            cls.model, shock_cov_matrix=np.eye(1) * 0.01, return_xr=True, verbose=False
         )
 
     def test_plot_with_defaults(self):
         fig = plot_acf(self.acf)
-        self.assertEqual(len(fig.axes), self.model.n_variables)
+        self.assertEqual(len(fig.axes), len(self.model.variables))
+        for axis, variable in zip(fig.axes, self.model.variables):
+            assert axis.get_title() == variable.base_name
+
         plt.close()
 
     def test_plot_with_subset(self):
         fig = plot_acf(self.acf, vars_to_plot=["C", "K", "A"])
         self.assertEqual(len(fig.axes), 3)
+        for axis, variable in zip(fig.axes, ["C", "K", "A"]):
+            assert axis.get_title() == variable
+
         plt.close()
 
     def test_invalid_var_raises(self):
@@ -298,65 +273,8 @@ def test_invalid_var_raises(self):
             msg,
             "Can not plot variable Invalid, it was not found in the provided covariance matrix",
         )
-
-
-class TestPostEstimationPlots(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls) -> None:
-        file_path = os.path.join(
-            ROOT, "Test GCNs/One_Block_Simple_1_w_Distributions.gcn"
-        )
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-
-        cls.data = cls.model.simulate(simulation_length=100, n_simulations=1)
-        cls.data = cls.data.droplevel(axis=1, level=1).T[["C"]]
-
-        cls.idata = cls.model.fit(
-            cls.data,
-            filter_type="univariate",
-            draws=36,
-            n_walkers=36,
-            return_inferencedata=True,
-            burn_in=0,
-            verbose=False,
-            compute_sampler_stats=False,
-        )
-
-    def test_plot_corner_with_defaults(self):
-        fig = plot_corner(self.idata)
-        self.assertIsNotNone(fig)
-        plt.close()
-
-    def test_plot_kalman_with_defaults(self):
-        posterior = self.idata.posterior.stack(sample=["chain", "draw"])
-        conditional_posterior = kalman_filter_from_posterior(
-            self.model, self.data, posterior, n_samples=10
-        )
-
-        fig = plot_kalman_filter(
-            conditional_posterior, self.data, kalman_output="predicted"
-        )
-        self.assertIsNotNone(fig)
         plt.close()
 
-        fig = plot_kalman_filter(
-            conditional_posterior, self.data, kalman_output="filtered"
-        )
-        self.assertIsNotNone(fig)
-        plt.close()
-
-        fig = plot_kalman_filter(
-            conditional_posterior, self.data, kalman_output="smoothed"
-        )
-        self.assertIsNotNone(fig)
-        plt.close()
-
-    def test_plot_kalman_raises_on_invalid_args(self):
-        with self.assertRaises(ValueError):
-            plot_kalman_filter(self.idata, self.data, kalman_output="invalid")
-
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_sampling.py b/tests/test_sampling.py
deleted file mode 100644
index 6668278..0000000
--- a/tests/test_sampling.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import os
-import unittest
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-from scipy import stats
-
-from gEconpy import gEconModel
-from gEconpy.sampling import prior_solvability_check
-from gEconpy.sampling.prior_utilities import (
-    get_initial_time_index,
-    kalman_filter_from_prior,
-    simulate_trajectories_from_prior,
-)
-
-ROOT = Path(__file__).parent.absolute()
-
-
-class TestPriorSampling(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        file_path = os.path.join(ROOT, "Test GCNs/Full_New_Keyensian.gcn")
-        cls.model = gEconModel(file_path, verbose=False)
-
-        # Add some priors
-        cls.model.param_priors["alpha"] = stats.beta(a=3, b=1)
-        cls.model.param_priors["rho_technology"] = stats.beta(a=1, b=3)
-        cls.model.param_priors["rho_preference"] = stats.beta(a=1, b=3)
-
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-
-    def test_sample_solvability_cycle_reduction(self):
-        data = prior_solvability_check(
-            self.model, n_samples=100, pert_solver="cycle_reduction"
-        )
-
-        self.assertEqual(data.shape[0], 100)
-
-    def test_sample_solvability_gensys(self):
-        data = prior_solvability_check(self.model, n_samples=100, pert_solver="gensys")
-
-        self.assertEqual(data.shape[0], 100)
-
-    def test_invalid_solver_raises(self):
-        with self.assertRaises(ValueError):
-            prior_solvability_check(self.model, n_samples=1, pert_solver="invalid")
-
-
-class TestGetInitialTime(unittest.TestCase):
-    def test_integer_index(self):
-        df = pd.DataFrame(np.random.normal(size=100))
-        initial_index = get_initial_time_index(df)
-
-        self.assertEqual(initial_index, -1)
-
-    def test_monthly_period_index(self):
-        index = pd.date_range(start="1900-02-01", periods=100, freq="MS")
-        df = pd.DataFrame(np.random.normal(size=100), index=index)
-        initial_index = get_initial_time_index(df)
-
-        self.assertEqual(
-            initial_index, np.array(pd.to_datetime("1900-01-01"), dtype="datetime64")
-        )
-
-    def test_quarterly_period_index(self):
-        index = pd.date_range(start="1900-04-01", periods=100, freq="QS")
-        df = pd.DataFrame(np.random.normal(size=100), index=index)
-        initial_index = get_initial_time_index(df)
-
-        self.assertEqual(
-            initial_index, np.array(pd.to_datetime("1900-01-01"), dtype="datetime64")
-        )
-
-    def test_annual_period_index(self):
-        index = pd.date_range(start="1901-01-01", periods=100, freq="YS")
-        df = pd.DataFrame(np.random.normal(size=100), index=index)
-        initial_index = get_initial_time_index(df)
-
-        self.assertEqual(
-            initial_index, np.array(pd.to_datetime("1900-01-01"), dtype="datetime64")
-        )
-
-
-class TestSimulateTrajectories(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        file_path = os.path.join(ROOT, "Test GCNs/RBC_Linearized.gcn")
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-
-    def test_simulate_trajectories(self):
-        data = simulate_trajectories_from_prior(
-            self.model, n_simulations=10, n_samples=10, simulation_length=10
-        )
-
-        self.assertEqual(
-            data.index.values.tolist(), [x.base_name for x in self.model.variables]
-        )
-        self.assertTrue(data.shape == (self.model.n_variables, 10 * 10 * 10))
-
-    def test_pert_kwargs(self):
-        data = simulate_trajectories_from_prior(
-            self.model,
-            n_simulations=10,
-            n_samples=10,
-            simulation_length=10,
-            pert_kwargs={"solver": "gensys"},
-        )
-        self.assertIsNotNone(data)
-
-
-class TestKalmanFilterFromPrior(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        file_path = os.path.join(ROOT, "Test GCNs/RBC_Linearized.gcn")
-        cls.model = gEconModel(file_path, verbose=False)
-        cls.model.steady_state(verbose=False)
-        cls.model.solve_model(verbose=False)
-
-    def test_univariate_filter(self):
-        data = self.model.simulate(simulation_length=100, n_simulations=1).T.droplevel(
-            1
-        )[["Y"]]
-        kf_output = kalman_filter_from_prior(
-            self.model, data, n_samples=10, filter_type="univariate"
-        )
-
-        self.assertIsNotNone(kf_output)
-
-
-if __name__ == "main":
-    unittest.main()
diff --git a/tests/test_statespace.py b/tests/test_statespace.py
new file mode 100644
index 0000000..ec1009a
--- /dev/null
+++ b/tests/test_statespace.py
@@ -0,0 +1,55 @@
+import os
+
+import numpy as np
+import pymc as pm
+import pytensor
+import pytest
+
+from tests.utilities.shared_fixtures import (
+    load_and_cache_model,
+    load_and_cache_statespace,
+)
+
+
+@pytest.mark.parametrize(
+    "gcn_file",
+    [
+        "one_block_1_ss.gcn",
+        "open_rbc.gcn",
+        "full_nk.gcn",
+        "rbc_linearized.gcn",
+    ],
+)
+def test_statespace_matrices_agree_with_model(gcn_file):
+    ss_mod = load_and_cache_statespace(gcn_file)
+    model = load_and_cache_model(gcn_file, verbose=False)
+
+    inputs = pm.inputvars(ss_mod.linearized_system)
+    input_names = [x.name for x in inputs]
+    f = pytensor.function(inputs, ss_mod.linearized_system, on_unused_input="ignore")
+    mod_matrices = model.linearize_model()
+
+    param_dict = model.parameters()
+    ss_matrices = f(**{k: param_dict[k] for k in input_names})
+
+    for mod_matrix, ss_matrix in zip(mod_matrices, ss_matrices):
+        np.testing.assert_allclose(mod_matrix, ss_matrix, atol=1e-8, rtol=1e-8)
+
+
+@pytest.mark.parametrize(
+    "gcn_file",
+    [
+        "one_block_1_ss.gcn",
+        "open_rbc.gcn",
+        "full_nk.gcn",
+        "rbc_linearized.gcn",
+    ],
+)
+def test_priors_to_preliz(gcn_file):
+    ss_mod = load_and_cache_statespace(gcn_file)
+    pz_priors = ss_mod.priors_to_preliz()
+
+    assert all(prior in pz_priors for prior in ss_mod.priors[0])
+    for name, prior in ss_mod.priors[0].items():
+        d = ss_mod.priors[0][name]
+        pz_d = pz_priors[name]
diff --git a/tests/test_statsmodel_convert.py b/tests/test_statsmodel_convert.py
deleted file mode 100644
index 23bec5f..0000000
--- a/tests/test_statsmodel_convert.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-import unittest
-from pathlib import Path
-from warnings import catch_warnings, simplefilter
-
-from gEconpy import compile_to_statsmodels, gEconModel
-from gEconpy.classes.transformers import IntervalTransformer, PositiveTransformer
-
-ROOT = Path(__file__).parent.absolute()
-
-
-class TestStatsModelConversion(unittest.TestCase):
-    @classmethod
-    def setUp(self) -> None:
-        file_path = os.path.join(
-            ROOT, "Test GCNs/One_Block_Simple_1_w_Distributions.gcn"
-        )
-        self.model = gEconModel(file_path, verbose=False)
-        self.model.steady_state(verbose=False)
-        self.model.solve_model(verbose=False)
-
-        self.data = self.model.simulate(simulation_length=100, n_simulations=1)
-        self.data = self.data.droplevel(axis=1, level=1).T[["C"]]
-
-    def test_conversion(self):
-        MLEModel = compile_to_statsmodels(self.model)
-        self.assertIsNotNone(MLEModel)
-
-    def test_mle_fit(self):
-        param_start_dict = {
-            "alpha": 0.33,
-            "gamma": 2.0,
-            "rho": 0.85,
-        }
-
-        shock_start_dict = {"epsilon": 0.5}
-
-        # The slope parameter controls the steepness of the gradient around 0 (lower slope = more gentle gradient)
-        param_transforms = {
-            "alpha": IntervalTransformer(low=1e-4, high=0.99),
-            "gamma": IntervalTransformer(low=1.001, high=20),
-            "rho": IntervalTransformer(low=1e-4, high=0.99),
-        }
-
-        MLEModel = compile_to_statsmodels(self.model)
-        initial_params = self.model.free_param_dict.copy()
-        mle_mod = MLEModel(
-            self.data,
-            param_start_dict=param_start_dict,
-            shock_start_dict=shock_start_dict,
-            noise_start_dict=None,
-            param_transforms=param_transforms,
-            shock_transforms=None,  # If None, will automatically transform to positive values only
-            noise_transforms=None,  # If None, will automatically transform to positive values only
-            initialization="stationary",
-        )
-
-        # This shouldn't succeed -- catch the warning
-        with catch_warnings():
-            simplefilter("ignore")
-            mle_res = mle_mod.fit(method="lbfgs", maxiter=10, disp=0)
-
-        # Final estimates in the mle_res object are the same as are saved in the model object
-        for param in mle_res.params.index:
-            if param in self.model.free_param_dict:
-                self.assertEqual(
-                    mle_res.params[param], self.model.free_param_dict[param], msg=param
-                )
-
-        # Check that parameters were changed
-        for param in ["alpha", "gamma", "rho"]:
-            self.assertNotEqual(
-                self.model.free_param_dict[param], initial_params[param], msg=param
-            )
-
-        # Make sure parameters not given start values were not changed
-        for param in ["beta", "delta"]:
-            self.assertEqual(
-                self.model.free_param_dict[param], initial_params[param], msg=param
-            )
-
-    def test_mle_fit_MAP(self):
-        param_start_dict = {
-            "alpha": 0.33,
-            "gamma": 2.0,
-            "rho": 0.85,
-        }
-
-        shock_start_dict = {"epsilon": 0.5}
-
-        # The slope parameter controls the steepness of the gradient around 0 (lower slope = more gentle gradient)
-        param_transforms = {
-            "alpha": IntervalTransformer(low=1e-4, high=0.99, slope=1),
-            "gamma": PositiveTransformer(),
-            "rho": IntervalTransformer(low=1e-4, high=0.99, slope=1),
-        }
-
-        MLEModel = compile_to_statsmodels(self.model)
-        initial_params = self.model.free_param_dict.copy()
-
-        mle_mod = MLEModel(
-            self.data,
-            param_start_dict=param_start_dict,
-            shock_start_dict=shock_start_dict,
-            noise_start_dict=None,
-            param_transforms=param_transforms,
-            shock_transforms=None,  # If None, will automatically transform to positive values only
-            noise_transforms=None,  # If None, will automatically transform to positive values only
-            initialization="stationary",
-            fit_MAP=True,
-        )
-
-        # This shouldn't succeed -- catch the warning
-        with catch_warnings():
-            simplefilter("ignore")
-            mle_res = mle_mod.fit(method="lbfgs", maxiter=10, disp=0)
-
-        # Final estimates in the mle_res object are the same as are saved in the model object
-        for param in mle_res.params.index:
-            if param in self.model.free_param_dict:
-                self.assertEqual(
-                    mle_res.params[param], self.model.free_param_dict[param], msg=param
-                )
-
-        # Check that parameters were changed
-        for param in ["alpha", "gamma", "rho"]:
-            self.assertNotEqual(
-                self.model.free_param_dict[param], initial_params[param], msg=param
-            )
-
-        # Make sure parameters not given start values were not changed
-        for param in ["beta", "delta"]:
-            self.assertEqual(
-                self.model.free_param_dict[param], initial_params[param], msg=param
-            )
-
-        self.assertIsNotNone(mle_res)
-
-
-if __name__ == "main":
-    unittest.main()
diff --git a/tests/test_steady_state.py b/tests/test_steady_state.py
index 3ed06f0..f43b8d4 100644
--- a/tests/test_steady_state.py
+++ b/tests/test_steady_state.py
@@ -1,507 +1,484 @@
-import os
 import re
-import unittest
-from pathlib import Path
-from unittest import mock
 
+import pytest
 import sympy as sp
-from scipy import optimize
-
-from gEconpy.classes.model import gEconModel
-
-ROOT = Path(__file__).parent.absolute()
-
-
-class SteadyStateModelOne(unittest.TestCase):
-    def setUp(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn"), verbose=False
-        )
-
-    def test_solve_ss_with_partial_user_solution(self):
-        self.model.steady_state(verbose=True, apply_user_simplifications=True)
-        self.assertTrue(self.model.steady_state_solved)
-
-    def test_wrong_user_solutions_raises(self):
-        self.model.steady_state_relationships["A_ss"] = 3.0
-
-        self.assertRaises(ValueError, self.model.steady_state, verbose=True)
-
-    @mock.patch("builtins.print")
-    def test_print_steady_state_report_before_solving(self, mock_print):
-        self.model.print_steady_state()
-        ss_report = mock_print.call_args.args[0]
-        self.assertEqual(
-            ss_report,
-            "Run the steady_state method to find a steady state before calling this method.",
-        )
-
-    @mock.patch("builtins.print")
-    def test_print_steady_state_report_solver_successful(self, mock_print):
-        self.model.steady_state(verbose=False)
-        self.model.print_steady_state()
-
-        expected_output = """A_ss               1.000
-                             C_ss               4.119
-                             K_ss              74.553
-                             U_ss             101.458
-                             lambda_ss          0.120"""
-
-        expected_output = re.sub("[\t\n]", " ", expected_output)
-        expected_output = re.sub(" +", " ", expected_output)
-
-        ss_report = mock_print.call_args.args[0]
-        ss_report = re.sub("[\t\n]", " ", ss_report)
-        ss_report = re.sub(" +", " ", ss_report)
-
-        self.assertEqual(ss_report, expected_output)
-
-    @mock.patch("builtins.print")
-    def test_print_steady_state_report_solver_fails(self, mock_print):
-        self.model.steady_state(verbose=False)
-
-        # Spoof a failed solving attempt
-        self.model.steady_state_solved = False
-        self.model.print_steady_state()
-        expected_output = """Values come from the latest solver iteration but are NOT a valid steady state.
-                             A_ss               1.000
-                             C_ss               4.119
-                             K_ss              74.553
-                             U_ss             101.458
-                             lambda_ss          0.120"""
-
-        expected_output = re.sub("[\t\n]", " ", expected_output)
-        expected_output = re.sub(" +", " ", expected_output)
-
-        ss_report = mock_print.call_args.args[0]
-        ss_report = re.sub("[\t\n]", " ", ss_report)
-        ss_report = re.sub(" +", " ", ss_report)
-
-        self.assertEqual(ss_report, expected_output)
-
-    def test_incomplete_ss_relationship_raises_with_root(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn"), verbose=False
-        )
-        self.model.steady_state_relationships["K_ss"] = 3.0
-
-        self.assertRaises(
-            ValueError, self.model.steady_state, verbose=False, method="root"
-        )
-
-    def test_wrong_and_incomplete_ss_relationship_fails_with_minimize(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn"), verbose=False
-        )
-        self.model.steady_state_relationships["K_ss"] = 3.0
-        self.model.steady_state(method="minimize", verbose=False)
-
-        self.assertTrue(not self.model.steady_state_solved)
-
-    def test_numerical_solvers_suceed_and_agree(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1.gcn"), verbose=False
-        )
-        self.model.steady_state(method="root", verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        ss_root = self.model.steady_state_dict.copy()
-
-        self.model.steady_state(method="minimize", verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        ss_minimize = self.model.steady_state_dict.copy()
-
-        for k in ss_root.keys():
-            self.assertAlmostEqual(ss_root[k], ss_minimize[k], places=6, msg=k)
-
-    def test_steady_state_matches_analytic(self):
-        param_dict = self.model.free_param_dict.to_sympy()
-        alpha, beta, delta, gamma, rho = list(param_dict.keys())
-
-        A_ss = sp.Float(1.0)
-        K_ss = ((alpha * beta) / (1 - beta + beta * delta)) ** (1 / (1 - alpha))
-        C_ss = K_ss**alpha - delta * K_ss
-        lambda_ss = C_ss ** (-gamma)
-        U_ss = 1 / (1 - beta) * (C_ss ** (1 - gamma) - 1) / (1 - gamma)
-
-        ss_var = [x.to_ss() for x in self.model.variables]
-        ss_dict = {
-            k: v.subs(param_dict)
-            for k, v in zip(ss_var, [A_ss, C_ss, K_ss, U_ss, lambda_ss])
-        }
-
-        self.model.steady_state(verbose=False)
-        self.assertTrue("A_ss" in self.model.steady_state_dict.keys())
-
-        for k in ss_dict:
-            self.assertAlmostEqual(
-                ss_dict[k], self.model.steady_state_dict[k.name], places=5
-            )
-
-
-class SteadyStateModelTwo(unittest.TestCase):
-    def setUp(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_2.gcn"), verbose=False
-        )
-
-    def test_numerical_solvers_succeed_and_agree(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_2.gcn"), verbose=False
-        )
-        self.model.steady_state(method="root", verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        ss_root = self.model.steady_state_dict.copy()
-
-        self.model.steady_state(method="minimize", verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        ss_minimize = self.model.steady_state_dict.copy()
-
-        for k in ss_root.keys():
-            self.assertAlmostEqual(ss_root[k], ss_minimize[k], places=6, msg=k)
-
-    def test_steady_state_matches_analytic(self):
-        param_dict = self.model.free_param_dict.to_sympy()
-        calib_params = self.model.params_to_calibrate
-
-        beta, delta, rho, tau, theta = list(param_dict.keys())
-        (alpha,) = calib_params
-
-        term_1 = theta * (1 - alpha) / (1 - theta)
-        term_2 = alpha / (1 - beta + beta * delta)
-        a_exp = alpha / (1 - alpha)
-
-        A_ss = sp.Float(1.0)
-        Y_ss = term_1 * term_2**a_exp / (1 + term_1 - delta * term_2)
-        K_ss = term_2 * Y_ss
-        L_ss = term_2 ** (-a_exp) * Y_ss
-        C_ss = term_1 * term_2**a_exp - term_1 * Y_ss
-        I_ss = delta * K_ss
-
-        lambda_ss = (
-            theta * (C_ss**theta * (1 - L_ss) ** (1 - theta)) ** (1 - tau) / C_ss
-        )
-
-        U_ss = (
-            1
-            / (1 - beta)
-            * (C_ss**theta * (1 - L_ss) ** (1 - theta)) ** (1 - tau)
-            / (1 - tau)
-        )
-        f = sp.lambdify(alpha, (L_ss / K_ss - 0.36).simplify().subs(param_dict))
-
-        res = optimize.root_scalar(f, bracket=[1e-4, 0.99])
-        calib_solution = {alpha: res.root}
-        all_params = param_dict | calib_solution
-
-        ss_var = [x.to_ss() for x in self.model.variables]
-        *_, K, L, _, _, _, _ = ss_var
-        ss_dict = {
-            k: v.subs(all_params)
-            for k, v in zip(
-                ss_var, [A_ss, C_ss, I_ss, K_ss, L_ss, U_ss, Y_ss, lambda_ss, lambda_ss]
-            )
-        }
-
-        self.assertAlmostEqual(ss_dict[L] / ss_dict[K], 0.36)
-
-        self.model.steady_state(verbose=False)
-
-        for k in ss_dict:
-            self.assertAlmostEqual(
-                ss_dict[k], self.model.steady_state_dict[k.name], places=5
-            )
-
-
-class SteadyStateModelThree(unittest.TestCase):
-    def setUp(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/Two_Block_RBC_1.gcn"), verbose=False
-        )
-        self.model.steady_state(verbose=False)
-
-    def test_numerical_solvers_succeed_and_agree(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/Two_Block_RBC_1.gcn"), verbose=False
-        )
-        self.model.steady_state(method="root", verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        ss_root = self.model.steady_state_dict.copy()
-
-        self.model.steady_state(method="minimize", verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        ss_minimize = self.model.steady_state_dict.copy()
-
-        for k in ss_root.keys():
-            self.assertAlmostEqual(ss_root[k], ss_minimize[k], places=6, msg=k)
-
-    def test_steady_state_matches_analytic(self):
-        param_dict = self.model.free_param_dict.to_sympy()
-
-        alpha, beta, delta, rho_A, sigma_C, sigma_L = list(param_dict.keys())
-        A_ss = sp.Float(1.0)
-        r_ss = 1 / beta - (1 - delta)
-        w_ss = (1 - alpha) * (alpha / r_ss) ** (alpha / (1 - alpha))
-        Y_ss = (
-            w_ss ** (1 / (sigma_L + sigma_C))
-            * (w_ss / (1 - alpha)) ** (sigma_L / (sigma_L + sigma_C))
-            * (r_ss / (r_ss - delta * alpha)) ** (sigma_C / (sigma_L + sigma_C))
-        )
-
-        C_ss = (w_ss) ** (1 / sigma_C) * (w_ss / (1 - alpha) / Y_ss) ** (
-            sigma_L / sigma_C
-        )
-
-        lambda_ss = C_ss ** (-sigma_C)
-        q_ss = lambda_ss
-        I_ss = delta * alpha * Y_ss / r_ss
-        K_ss = alpha * Y_ss / r_ss
-        L_ss = (1 - alpha) * Y_ss / w_ss
-        P_ss = (w_ss / (1 - alpha)) ** (1 - alpha) * (r_ss / alpha) ** alpha
-
-        U_ss = (
-            1
-            / (1 - beta)
-            * (
-                C_ss ** (1 - sigma_C) / (1 - sigma_C)
-                - L_ss ** (1 + sigma_L) / (1 + sigma_L)
-            )
-        )
-
-        TC_ss = -(r_ss * K_ss + w_ss * L_ss)
-
-        ss_var = [x.to_ss() for x in self.model.variables]
-        answers = [
-            A_ss,
-            C_ss,
-            I_ss,
-            K_ss,
-            L_ss,
-            TC_ss,
-            U_ss,
-            Y_ss,
-            lambda_ss,
-            q_ss,
-            r_ss,
-            w_ss,
-        ]
-        ss_dict = {k: v.subs(param_dict) for k, v in zip(ss_var, answers)}
-
-        for k in ss_dict:
-            self.assertAlmostEqual(
-                ss_dict[k], self.model.steady_state_dict[k.name], places=5
-            )
-
-        self.assertAlmostEqual(P_ss.subs(param_dict), 1.0)
-
-
-class SteadyStateModelFour(unittest.TestCase):
-    def setUp(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/Full_New_Keyensian.gcn"), verbose=False
-        )
-        self.model.steady_state(verbose=False)
-
-    def test_numerical_solvers_succeed_and_agree(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/Full_New_Keyensian.gcn"), verbose=False
-        )
-        self.model.steady_state(method="root", verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        ss_root = self.model.steady_state_dict.copy()
-
-        self.model.steady_state(method="minimize", verbose=False)
-        self.assertTrue(self.model.steady_state_solved)
-        ss_minimize = self.model.steady_state_dict.copy()
-
-        for k in ss_root.keys():
-            self.assertAlmostEqual(ss_root[k], ss_minimize[k], places=6, msg=k)
-
-    def test_steady_state_matches_analytic(self):
-        param_dict = self.model.free_param_dict.to_sympy()
-        (
-            alpha,
-            beta,
-            delta,
-            eta_p,
-            eta_w,
-            gamma_I,
-            gamma_R,
-            gamma_Y,
-            gamma_pi,
-            phi_H,
-            psi_p,
-            psi_w,
-            rho_pi_dot,
-            rho_preference,
-            rho_technology,
-            sigma_C,
-            sigma_L,
-        ) = list(param_dict.keys())
-
-        shock_technology_ss = sp.Float(1)
-        shock_preference_ss = sp.Float(1)
-        pi_ss = sp.Float(1)
-        pi_star_ss = sp.Float(1)
-        pi_obj_ss = sp.Float(1)
-        # B_ss = sp.Float(0)
-
-        r_ss = 1 / beta - (1 - delta)
-        r_G_ss = 1 / beta
-
-        mc_ss = 1 / (1 + psi_p)
-        w_ss = (
-            (1 - alpha)
-            * mc_ss ** (1 / (1 - alpha))
-            * (alpha / r_ss) ** (alpha / (1 - alpha))
-        )
-
-        w_star_ss = w_ss
-
-        Y_ss = (
-            w_ss ** ((sigma_L + 1) / (sigma_C + sigma_L))
-            * ((-beta * phi_H + 1) / (psi_w + 1)) ** (1 / (sigma_C + sigma_L))
-            * (r_ss / ((1 - phi_H) * (-alpha * delta * mc_ss + r_ss)))
-            ** (sigma_C / (sigma_C + sigma_L))
-            / (mc_ss * (1 - alpha)) ** (sigma_L / (sigma_C + sigma_L))
-        )
-
-        C_ss = (
-            w_ss ** ((1 + sigma_L) / sigma_C)
-            * (1 / (1 - phi_H))
-            * ((1 - beta * phi_H) / (1 + psi_w)) ** (1 / sigma_C)
-            * ((1 - alpha) * mc_ss) ** (-sigma_L / sigma_C)
-            * Y_ss ** (-sigma_L / sigma_C)
-        )
-
-        lambda_ss = (1 - beta * phi_H) * ((1 - phi_H) * C_ss) ** (-sigma_C)
-        q_ss = lambda_ss
-        I_ss = delta * alpha * mc_ss * Y_ss / r_ss
-        K_ss = alpha * mc_ss * Y_ss / r_ss
-        L_ss = (1 - alpha) * Y_ss * mc_ss / w_ss
-
-        U_ss = (
-            1
-            / (1 - beta)
-            * (
-                ((1 - phi_H) * C_ss) ** (1 - sigma_C) / (1 - sigma_C)
-                - L_ss ** (1 + sigma_L) / (1 + sigma_L)
-            )
-        )
-
-        TC_ss = -(r_ss * K_ss + w_ss * L_ss)
-        Div_ss = Y_ss + TC_ss
-
-        LHS_ss = (
-            1
-            / (1 - beta * eta_p * pi_ss ** (1 / psi_p))
-            * lambda_ss
-            * Y_ss
-            * pi_star_ss
-        )
-
-        RHS_ss = 1 / (1 + psi_p) * LHS_ss
-
-        LHS_w_ss = (
-            1 / (1 - beta * eta_w) * 1 / (1 + psi_w) * w_star_ss * lambda_ss * L_ss
-        )
-
-        RHS_w_ss = LHS_w_ss
-
-        ss_var = [x.to_ss() for x in self.model.variables]
-        answers = [
-            C_ss,
-            Div_ss,
-            I_ss,
-            K_ss,
-            LHS_ss,
-            LHS_w_ss,
-            L_ss,
-            RHS_ss,
-            RHS_w_ss,
-            TC_ss,
-            U_ss,
-            Y_ss,
-            lambda_ss,
-            mc_ss,
-            pi_obj_ss,
-            pi_star_ss,
-            pi_ss,
-            q_ss,
-            r_G_ss,
-            r_ss,
-            shock_preference_ss,
-            shock_technology_ss,
-            w_star_ss,
-            w_ss,
-        ]
-
-        ss_dict = {k: v.subs(param_dict) for k, v in zip(ss_var, answers)}
-
-        for k in ss_dict:
-            self.assertAlmostEqual(
-                ss_dict[k], self.model.steady_state_dict[k.name], places=5, msg=k
-            )
-
-
-class SteadyStateWithUserError(unittest.TestCase):
-    def setUp(self):
-        self.model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1_ss_Error.gcn"),
-            verbose=False,
-        )
-
-    def test_raises_on_nonzero_resids(self):
-        self.assertRaises(
-            ValueError,
-            self.model.steady_state,
-            apply_user_simplifications=True,
-            verbose=False,
-        )
 
+from numpy.testing import assert_allclose
+from scipy import optimize
 
-class FullyUserDefinedSteadyState(unittest.TestCase):
-    def test_ss_solves_from_user_definition(self):
-        model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1_w_Steady_State.gcn"),
-            verbose=False,
-        )
+from gEconpy import model_from_gcn
+from gEconpy.model.model import Model
+from gEconpy.model.steady_state import print_steady_state
+from tests.test_model_loaders import JAX_INSTALLED
+from tests.utilities.shared_fixtures import load_and_cache_model
+
+
+def root_and_min_agree_helper(model: Model, **kwargs):
+    verbose = kwargs.pop("verbose", False)
+    progressbar = kwargs.pop("progressbar", True)
+    root_method = kwargs.pop("root_method", None)
+    minimize_method = kwargs.pop("minimize_method", None)
+    optimizer_kwargs = kwargs.pop("optimizer_kwargs", {})
 
-        for method in ["root", "minimize"]:
-            model.steady_state(
-                apply_user_simplifications=True, verbose=False, method=method
-            )
-            self.assertTrue(model.steady_state_solved, msg=method)
+    _ = kwargs.pop("how", None)
 
-    def test_ss_solves_when_ignoring_user_definition(self):
-        model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1_w_Steady_State.gcn"),
-            verbose=False,
+    if root_method:
+        optimizer_kwargs["method"] = root_method
+
+    ss_root = model.steady_state(
+        how="root",
+        verbose=verbose,
+        progressbar=progressbar,
+        optimizer_kwargs=optimizer_kwargs,
+        **kwargs,
+    )
+
+    if minimize_method:
+        optimizer_kwargs["method"] = minimize_method
+    ss_minimize = model.steady_state(
+        how="minimize",
+        verbose=verbose,
+        progressbar=progressbar,
+        optimizer_kwargs=optimizer_kwargs,
+        **kwargs,
+    )
+
+    assert ss_root.success
+    assert ss_minimize.success
+
+    for k in ss_root.keys():
+        assert_allclose(ss_root[k], ss_minimize[k], err_msg=k)
+
+
+def test_solve_ss_with_partial_user_solution():
+    model_1 = load_and_cache_model(
+        "one_block_1.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    res = model_1.steady_state()
+    assert res.success
+
+
+def test_wrong_user_solutions_raises():
+    model_1 = load_and_cache_model(
+        "one_block_1.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+
+    expected_msg = (
+        "User-provide steady state is not valid. The following equations had non-zero residuals "
+        "after subsitution:\n(rho - 1)*log(A_ss)"
+    )
+
+    with pytest.raises(ValueError, match=re.escape(expected_msg)):
+        model_1.steady_state(fixed_values={"A_ss": 3.0})
+
+
+def test_print_steady_state_report_solver_successful(caplog):
+    model_1 = load_and_cache_model(
+        "one_block_1.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    res = model_1.steady_state(verbose=False, progressbar=False)
+
+    expected_output = """A_ss               1.000
+                         C_ss               4.119
+                         K_ss              74.553
+                         U_ss             101.458
+                         lambda_ss          0.120"""
+
+    expected_output = re.sub("[\t\n]", " ", expected_output)
+    expected_output = re.sub(" +", " ", expected_output)
+
+    print_steady_state(res)
+    emitted_message = caplog.messages[-1]
+
+    emitted_message = re.sub("[\t\n]", " ", emitted_message)
+    emitted_message = re.sub(" +", " ", emitted_message)
+
+    assert emitted_message == expected_output
+
+
+def test_print_steady_state_report_solver_fails(caplog):
+    model_1 = load_and_cache_model(
+        "one_block_1.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    result = model_1.steady_state(verbose=False, progressbar=False)
+
+    # Spoof a failed solving attempt
+    result.success = False
+    print_steady_state(result)
+    expected_output = """Values come from the latest solver iteration but are NOT a valid steady state.
+                         A_ss               1.000
+                         C_ss               4.119
+                         K_ss              74.553
+                         U_ss             101.458
+                         lambda_ss          0.120"""
+    expected_output = re.sub("[\t\n]", " ", expected_output)
+    expected_output = re.sub(" +", " ", expected_output)
+
+    emitted_message = caplog.messages[-1]
+    emitted_message = re.sub("[\t\n]", " ", emitted_message)
+    emitted_message = re.sub(" +", " ", emitted_message)
+
+    assert emitted_message == expected_output
+
+
+def test_incomplete_ss_relationship_raises_with_root():
+    model_1 = load_and_cache_model(
+        "one_block_1.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    expected_msg = (
+        'Solving a partially provided steady state with how = "root" is only allowed if applying the given '
+        "values results in a new square system.\n"
+        "Found: 1 provided steady state value\n"
+        "Eliminated: 0 equations."
+    )
+    with pytest.raises(
+        ValueError,
+        match=expected_msg,
+    ):
+        model_1.steady_state(how="root", fixed_values={"K_ss": 3.0})
+
+
+def test_wrong_and_incomplete_ss_relationship_fails_with_minimize():
+    model_1 = load_and_cache_model(
+        "one_block_1.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    res = model_1.steady_state(
+        verbose=False, progressbar=False, fixed_values={"K_ss": 3.0}
+    )
+    assert not res.success
+
+
+def test_numerical_solvers_suceed_and_agree():
+    model_1 = load_and_cache_model(
+        "one_block_1.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    root_and_min_agree_helper(model_1)
+
+
+def test_steady_state_matches_analytic():
+    model_1 = load_and_cache_model(
+        "one_block_1.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    param_dict = model_1.parameters().to_sympy()
+    alpha, beta, delta, gamma, rho = list(param_dict.keys())
+
+    A_ss = sp.Float(1.0)
+    K_ss = ((alpha * beta) / (1 - beta + beta * delta)) ** (1 / (1 - alpha))
+    C_ss = K_ss**alpha - delta * K_ss
+    lambda_ss = C_ss ** (-gamma)
+    U_ss = 1 / (1 - beta) * (C_ss ** (1 - gamma) - 1) / (1 - gamma)
+
+    ss_var = [x.to_ss().name for x in model_1.variables]
+    ss_dict = {
+        k: float(v.subs(param_dict))
+        for k, v in zip(ss_var, [A_ss, C_ss, K_ss, U_ss, lambda_ss])
+    }
+
+    root_ss_dict = model_1.steady_state(verbose=False, progressbar=False, how="root")
+    assert root_ss_dict.success
+
+    minimize_ss_dict = model_1.steady_state(
+        verbose=False, progressbar=False, how="minimize"
+    )
+    assert minimize_ss_dict.success
+
+    for k in ss_dict:
+        assert_allclose(ss_dict[k], root_ss_dict[k])
+        assert_allclose(ss_dict[k], minimize_ss_dict[k])
+
+
+def test_numerical_solvers_succeed_and_agree_w_calibrated_params():
+    model_2 = load_and_cache_model(
+        "one_block_2_no_extra.gcn",
+        backend="numpy",
+        use_jax=JAX_INSTALLED,
+    )
+    root_and_min_agree_helper(model_2)
+
+
+def test_steady_state_matches_analytic_w_calibrated_params():
+    model_2 = load_and_cache_model(
+        "one_block_2_no_extra.gcn",
+        backend="numpy",
+        use_jax=JAX_INSTALLED,
+    )
+    param_dict = model_2.parameters().to_sympy()
+    calib_params = model_2.calibrated_params
+
+    beta, delta, rho, tau, theta = list(param_dict.keys())
+    (alpha,) = calib_params
+
+    term_1 = theta * (1 - alpha) / (1 - theta)
+    term_2 = alpha / (1 - beta + beta * delta)
+    a_exp = alpha / (1 - alpha)
+
+    A_ss = sp.Float(1.0)
+    Y_ss = term_1 * term_2**a_exp / (1 + term_1 - delta * term_2)
+    K_ss = term_2 * Y_ss
+    L_ss = term_2 ** (-a_exp) * Y_ss
+    C_ss = term_1 * term_2**a_exp - term_1 * Y_ss
+    I_ss = delta * K_ss
+
+    lambda_ss = theta * (C_ss**theta * (1 - L_ss) ** (1 - theta)) ** (1 - tau) / C_ss
+    q_ss = lambda_ss
+
+    U_ss = (
+        1
+        / (1 - beta)
+        * (C_ss**theta * (1 - L_ss) ** (1 - theta)) ** (1 - tau)
+        / (1 - tau)
+    )
+
+    f = sp.lambdify(alpha, (L_ss / K_ss - 0.36).simplify().subs(param_dict))
+    res = optimize.root_scalar(f, bracket=[1e-4, 0.99])
+
+    calib_solution = {alpha: res.root}
+    all_params = param_dict | calib_solution
+
+    answer_dict = {
+        "A_ss": A_ss,
+        "C_ss": C_ss,
+        "I_ss": I_ss,
+        "K_ss": K_ss,
+        "L_ss": L_ss,
+        "U_ss": U_ss,
+        "Y_ss": Y_ss,
+        "lambda_ss": lambda_ss,
+        "q_ss": q_ss,
+        "alpha": res.root,
+    }
+
+    numerical_ss_dict = model_2.steady_state(verbose=False, progressbar=False)
+    assert numerical_ss_dict.success
+
+    # Test calibration of alpha --> L_ss / K_ss = 0.36
+    assert_allclose(numerical_ss_dict["L_ss"] / numerical_ss_dict["K_ss"], 0.36)
+
+    ss_vars = [x.to_ss() for x in model_2.variables]
+    for k in ss_vars:
+        answer = float(answer_dict[k.name].subs(all_params))
+        assert_allclose(answer, numerical_ss_dict[k.name], err_msg=k.name)
+
+
+def test_numerical_solvers_succeed_and_agree_RBC():
+    model_3 = load_and_cache_model(
+        "rbc_2_block.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    root_and_min_agree_helper(model_3)
+
+
+def test_RBC_steady_state_matches_analytic():
+    model_3 = load_and_cache_model(
+        "rbc_2_block.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+    param_dict = model_3.parameters().to_sympy()
+
+    alpha, beta, delta, rho_A, sigma_C, sigma_L = list(param_dict.keys())
+    A_ss = sp.Float(1.0)
+    r_ss = 1 / beta - (1 - delta)
+    w_ss = (1 - alpha) * (alpha / r_ss) ** (alpha / (1 - alpha))
+    Y_ss = (
+        w_ss ** (1 / (sigma_L + sigma_C))
+        * (w_ss / (1 - alpha)) ** (sigma_L / (sigma_L + sigma_C))
+        * (r_ss / (r_ss - delta * alpha)) ** (sigma_C / (sigma_L + sigma_C))
+    )
+
+    C_ss = (w_ss) ** (1 / sigma_C) * (w_ss / (1 - alpha) / Y_ss) ** (sigma_L / sigma_C)
+
+    lambda_ss = C_ss ** (-sigma_C)
+    q_ss = lambda_ss
+    I_ss = delta * alpha * Y_ss / r_ss
+    K_ss = alpha * Y_ss / r_ss
+    L_ss = (1 - alpha) * Y_ss / w_ss
+    P_ss = (w_ss / (1 - alpha)) ** (1 - alpha) * (r_ss / alpha) ** alpha
+
+    U_ss = (
+        1
+        / (1 - beta)
+        * (
+            C_ss ** (1 - sigma_C) / (1 - sigma_C)
+            - L_ss ** (1 + sigma_L) / (1 + sigma_L)
         )
-
-        for method in ["root", "minimize"]:
-            model.steady_state(
-                apply_user_simplifications=False, verbose=False, method=method
-            )
-            self.assertTrue(model.steady_state_solved, msg=method)
-
-    def test_solver_matches_user_solution(self):
-        model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1_w_Steady_State.gcn"),
-            verbose=False,
+    )
+
+    TC_ss = -(r_ss * K_ss + w_ss * L_ss)
+
+    answer_dict = {
+        "A_ss": A_ss,
+        "C_ss": C_ss,
+        "I_ss": I_ss,
+        "K_ss": K_ss,
+        "L_ss": L_ss,
+        "TC_ss": TC_ss,
+        "U_ss": U_ss,
+        "Y_ss": Y_ss,
+        "lambda_ss": lambda_ss,
+        "q_ss": q_ss,
+        "r_ss": r_ss,
+        "w_ss": w_ss,
+    }
+
+    numerical_ss_dict = model_3.steady_state(verbose=False, progressbar=False)
+    ss_vars = [x.to_ss() for x in model_3.variables]
+
+    for k in ss_vars:
+        answer = float(answer_dict[k.name].subs(param_dict))
+        assert_allclose(answer, numerical_ss_dict[k.name], err_msg=k.name)
+
+
+def test_numerical_solvers_succeed_and_agree_NK():
+    model_4 = load_and_cache_model(
+        "full_nk_no_ss.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+
+    # This model's SS can't be solved without some help, so we provide the "obvious" solutions
+    # This is almost equivalent to the full_nk_partial_ss.gcn, with a bit less info
+    # (No solution for mc_ss, r_G, and r)
+    root_and_min_agree_helper(
+        model_4,
+        verbose=False,
+        progressbar=False,
+        optimizer_kwargs={"maxiter": 50_000},
+        fixed_values={
+            "shock_technology_ss": 1.0,
+            "shock_preference_ss": 1.0,
+            "pi_ss": 1.0,
+            "pi_star_ss": 1.0,
+            "pi_obj_ss": 1.0,
+        },
+    )
+
+
+def test_steady_state_matches_analytic_NK():
+    model_4 = load_and_cache_model(
+        "full_nk_no_ss.gcn", backend="numpy", use_jax=JAX_INSTALLED
+    )
+
+    param_dict = model_4.parameters().to_sympy()
+    (
+        alpha,
+        beta,
+        delta,
+        eta_p,
+        eta_w,
+        gamma_I,
+        gamma_R,
+        gamma_Y,
+        gamma_pi,
+        phi_H,
+        phi_pi_obj,
+        psi_p,
+        psi_w,
+        rho_pi_dot,
+        rho_preference,
+        rho_technology,
+        sigma_C,
+        sigma_L,
+    ) = list(param_dict.keys())
+
+    shock_technology_ss = sp.Float(1)
+    shock_preference_ss = sp.Float(1)
+    pi_ss = sp.Float(1)
+    pi_star_ss = sp.Float(1)
+    pi_obj_ss = sp.Float(1)
+
+    r_ss = 1 / beta - (1 - delta)
+    r_G_ss = 1 / beta
+
+    mc_ss = 1 / (1 + psi_p)
+    w_ss = (
+        (1 - alpha)
+        * mc_ss ** (1 / (1 - alpha))
+        * (alpha / r_ss) ** (alpha / (1 - alpha))
+    )
+    w_star_ss = w_ss
+
+    Y_ss = (
+        w_ss ** ((sigma_L + 1) / (sigma_C + sigma_L))
+        * ((-beta * phi_H + 1) / (psi_w + 1)) ** (1 / (sigma_C + sigma_L))
+        * (r_ss / ((1 - phi_H) * (-alpha * delta * mc_ss + r_ss)))
+        ** (sigma_C / (sigma_C + sigma_L))
+        / (mc_ss * (1 - alpha)) ** (sigma_L / (sigma_C + sigma_L))
+    )
+
+    C_ss = (
+        w_ss ** ((1 + sigma_L) / sigma_C)
+        * (1 / (1 - phi_H))
+        * ((1 - beta * phi_H) / (1 + psi_w)) ** (1 / sigma_C)
+        * ((1 - alpha) * mc_ss) ** (-sigma_L / sigma_C)
+        * Y_ss ** (-sigma_L / sigma_C)
+    )
+
+    lambda_ss = (1 - beta * phi_H) * ((1 - phi_H) * C_ss) ** (-sigma_C)
+    q_ss = lambda_ss
+    I_ss = delta * alpha * mc_ss * Y_ss / r_ss
+    K_ss = alpha * mc_ss * Y_ss / r_ss
+    L_ss = (1 - alpha) * Y_ss * mc_ss / w_ss
+
+    U_ss = (
+        1
+        / (1 - beta)
+        * (
+            ((1 - phi_H) * C_ss) ** (1 - sigma_C) / (1 - sigma_C)
+            - L_ss ** (1 + sigma_L) / (1 + sigma_L)
         )
-        model.steady_state(apply_user_simplifications=False, verbose=False)
-        ss_dict_numeric = model.steady_state_dict.copy()
-
-        model = gEconModel(
-            os.path.join(ROOT, "Test GCNs/One_Block_Simple_1_w_Steady_State.gcn"),
-            verbose=False,
-        )
-        model.steady_state(apply_user_simplifications=True, verbose=False)
-        ss_dict_user = model.steady_state_dict.copy()
-
-        for k in ss_dict_user:
-            self.assertAlmostEqual(ss_dict_numeric[k], ss_dict_user[k], msg=k)
-
-
-if __name__ == "__main__":
-    unittest.main()
+    )
+
+    TC_ss = -(r_ss * K_ss + w_ss * L_ss)
+    Div_ss = Y_ss + TC_ss
+
+    LHS_ss = (
+        1 / (1 - beta * eta_p * pi_ss ** (1 / psi_p)) * lambda_ss * Y_ss * pi_star_ss
+    )
+
+    RHS_ss = 1 / (1 + psi_p) * LHS_ss
+
+    LHS_w_ss = 1 / (1 - beta * eta_w) * 1 / (1 + psi_w) * w_star_ss * lambda_ss * L_ss
+
+    RHS_w_ss = LHS_w_ss
+
+    answer_dict = {
+        "C_ss": C_ss,
+        "Div_ss": Div_ss,
+        "I_ss": I_ss,
+        "K_ss": K_ss,
+        "LHS_ss": LHS_ss,
+        "LHS_w_ss": LHS_w_ss,
+        "L_ss": L_ss,
+        "RHS_ss": RHS_ss,
+        "RHS_w_ss": RHS_w_ss,
+        "TC_ss": TC_ss,
+        "U_ss": U_ss,
+        "Y_ss": Y_ss,
+        "lambda_ss": lambda_ss,
+        "mc_ss": mc_ss,
+        "pi_obj_ss": pi_obj_ss,
+        "pi_star_ss": pi_star_ss,
+        "pi_ss": pi_ss,
+        "q_ss": q_ss,
+        "r_G_ss": r_G_ss,
+        "r_ss": r_ss,
+        "shock_preference_ss": shock_preference_ss,
+        "shock_technology_ss": shock_technology_ss,
+        "w_star_ss": w_star_ss,
+        "w_ss": w_ss,
+    }
+
+    numerical_ss_dict = model_4.steady_state(
+        how="root",
+        fixed_values={
+            "shock_technology_ss": 1.0,
+            "shock_preference_ss": 1.0,
+            "pi_ss": 1.0,
+            "pi_star_ss": 1.0,
+            "pi_obj_ss": 1.0,
+        },
+        verbose=False,
+        progressbar=False,
+    )
+    assert numerical_ss_dict.success
+
+    ss_vars = [x.to_ss() for x in model_4.variables]
+    for k in ss_vars:
+        answer = float(answer_dict[k.name].subs(param_dict))
+        assert_allclose(answer, numerical_ss_dict[k.name], err_msg=k.name)
diff --git a/tests/test_time_aware_symbols.py b/tests/test_time_aware_symbols.py
index 9b7784b..94c3066 100644
--- a/tests/test_time_aware_symbols.py
+++ b/tests/test_time_aware_symbols.py
@@ -3,7 +3,7 @@
 import sympy as sp
 
 from gEconpy.classes.time_aware_symbol import TimeAwareSymbol
-from gEconpy.shared.utilities import (
+from gEconpy.utilities import (
     diff_through_time,
     step_equation_backward,
     step_equation_forward,
diff --git a/tests/test_transformers.py b/tests/test_transformers.py
deleted file mode 100644
index 5bfed92..0000000
--- a/tests/test_transformers.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import unittest
-
-from gEconpy.classes.transformers import (
-    IdentityTransformer,
-    IntervalTransformer,
-    PositiveTransformer,
-)
-
-
-class TestIdentityTransformer(unittest.TestCase):
-    def setUp(self):
-        self.transformer = IdentityTransformer()
-
-    def test_constrain(self):
-        cases = [-1, 1, 0.2]
-        for case in cases:
-            self.assertEqual(case, self.transformer.constrain(case), msg=f"{case}")
-
-    def test_unconstrain(self):
-        cases = [-1, 1, 0.2]
-        for case in cases:
-            self.assertAlmostEqual(
-                case,
-                self.transformer.unconstrain(self.transformer.constrain(case)),
-                msg=f"{case}",
-            )
-
-
-class TestPositiveTransformer(unittest.TestCase):
-    def setUp(self):
-        self.transformer = PositiveTransformer()
-
-    def test_constrain(self):
-        cases = [-1, 1, 0.2]
-        for case in cases:
-            self.assertTrue(self.transformer.constrain(case) >= 0, msg=f"{case}")
-
-    def test_unconstrain(self):
-        cases = [-1, 1, 0.2]
-        for case in cases:
-            self.assertAlmostEqual(
-                case,
-                self.transformer.unconstrain(self.transformer.constrain(case)),
-                msg=f"{case}",
-            )
-
-
-class TestIntervalTransformer(unittest.TestCase):
-    def setUp(self):
-        self.transformer = IntervalTransformer()
-
-    def test_constrain(self):
-        cases = [-5, 3, 0.2]
-        for case in cases:
-            constrained = self.transformer.constrain(case)
-            self.assertTrue((0 < constrained) & (constrained < 1), msg=f"{case}")
-
-    def test_unconstrain(self):
-        cases = [-1, 1, 0.2]
-        for case in cases:
-            self.assertAlmostEqual(
-                case,
-                self.transformer.unconstrain(self.transformer.constrain(case)),
-                msg=f"{case}",
-            )
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/test_utilities.py b/tests/test_utilities.py
index 3869368..03c3735 100644
--- a/tests/test_utilities.py
+++ b/tests/test_utilities.py
@@ -1,90 +1,15 @@
 import unittest
-from pathlib import Path
 
 import numpy as np
+
 from scipy import stats
 
+from gEconpy.model.model import autocovariance_matrix
 from gEconpy.parser.parse_distributions import CompositeDistribution
-from gEconpy.shared.utilities import (
-    build_Q_matrix,
-    compute_autocorrelation_matrix,
+from gEconpy.utilities import (
     get_shock_std_priors_from_hyperpriors,
 )
 
-ROOT = Path(__file__).parent.absolute()
-
-
-class TestBuildQMatrix(unittest.TestCase):
-    def setUp(self):
-        self.shocks = ["epsilon_A", "epsilon_B", "epsilon_C"]
-        self.shock_std_priors = {
-            "epsilon_A": stats.gamma(2, 1),
-            "epsilon_B": stats.gamma(2, 1),
-            "epsilon_C": stats.gamma(2, 1),
-        }
-
-    def test_passing_both_args_raises(self):
-        with self.assertRaises(ValueError):
-            build_Q_matrix(
-                model_shocks=self.shocks,
-                shock_dict={"epsilon_A": 3},
-                shock_cov_matrix=np.eye(3),
-                shock_std_priors=self.shock_std_priors,
-            )
-
-    def test_not_positive_semidef_raises(self):
-        cov_mat = np.random.normal(size=(3, 3))
-        with self.assertRaises(np.linalg.LinAlgError):
-            build_Q_matrix(model_shocks=self.shocks, shock_cov_matrix=cov_mat)
-
-    def test_cov_matrix_bad_shape_raises(self):
-        cov_mat = np.random.normal(size=(3, 2))
-        with self.assertRaises(ValueError):
-            build_Q_matrix(model_shocks=self.shocks, shock_cov_matrix=cov_mat)
-
-    def test_build_from_dictionary(self):
-        Q = build_Q_matrix(
-            model_shocks=self.shocks, shock_std_priors=None, shock_dict={"epsilon_A": 3}
-        )
-
-        expected_Q = np.array([[3, 0, 0], [0, 0.01, 0], [0, 0, 0.01]])
-
-        self.assertTrue(np.allclose(Q, expected_Q))
-
-    def test_build_from_priors(self):
-        Q = build_Q_matrix(
-            model_shocks=self.shocks, shock_std_priors=self.shock_std_priors
-        )
-        expected_Q = np.eye(3)
-        for i, shock_d in enumerate(self.shock_std_priors.values()):
-            expected_Q[i, i] = shock_d.mean()
-
-        self.assertTrue(np.allclose(Q, expected_Q))
-
-    def test_build_from_mixed(self):
-        Q = build_Q_matrix(
-            model_shocks=self.shocks,
-            shock_std_priors=self.shock_std_priors,
-            shock_dict={"epsilon_B": 100},
-        )
-
-        expected_Q = np.eye(3)
-        for i, shock_d in enumerate(self.shock_std_priors.values()):
-            expected_Q[i, i] = shock_d.mean()
-
-        expected_Q[1, 1] = 100
-        self.assertTrue(np.allclose(Q, expected_Q))
-
-
-class TestComputeAutocorrelation(unittest.TestCase):
-    def test_compute_autocorrelation_matrix(self):
-        A = np.eye(5)
-        L = np.random.normal(size=(5, 5))
-        Q = L @ L.T
-
-        acorr = compute_autocorrelation_matrix(A, Q, n_lags=10)
-        self.assertEqual(acorr.shape, (5, 10))
-
 
 class TestExtractShockStd(unittest.TestCase):
     def setUp(self):
diff --git a/tests/utilities/__init__.py b/tests/utilities/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/utilities/expected_matrices.py b/tests/utilities/expected_matrices.py
new file mode 100644
index 0000000..ca9c45e
--- /dev/null
+++ b/tests/utilities/expected_matrices.py
@@ -0,0 +1,2925 @@
+import numpy as np
+
+expected_linearization_result = {
+    "one_block_1_ss.gcn": {
+        "param_dict": {
+            "theta": 0.357,
+            "beta": 0.99,
+            "delta": 0.02,
+            "tau": 2,
+            "rho": 0.95,
+        },
+        "P": np.array([[0.950, 0.0000], [0.2710273, 0.8916969]]),
+        "Q": np.array([[1.000], [0.2852917]]),
+        # TODO: Bug? When the SS value is negative, the sign of the S and R matrix entries are flipped relative to
+        #   those of gEcon (row 4 -- Utility). This code flips the sign on my values to make the comparison.
+        #   Check Dynare.
+        "R": np.array(
+            [
+                [0.70641931, 0.162459910],
+                [13.55135517, -4.415155354],
+                [0.42838971, -0.152667442],
+                [-0.06008706, -0.009473984],
+                [1.36634369, -0.072720705],
+                [-0.80973441, -0.273514035],
+                [-0.80973441, -0.273514035],
+            ]
+        ),
+        "S": np.array(
+            [
+                [0.74359928],
+                [14.26458439],
+                [0.45093654],
+                [-0.06324954],
+                [1.43825652],
+                [-0.85235201],
+                [-0.85235201],
+            ]
+        ),
+        "A": np.array(
+            [
+                [0.95, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.41949221, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 13.65742769, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.43677274, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            ]
+        ),
+        "B": np.array(
+            [
+                [
+                    -1.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    1.19854918e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    7.79056967e-01,
+                    0.00000000e00,
+                    -1.19854918e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    -9.19826166e-01,
+                    -2.78723014e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    1.19854918e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    2.78723014e-01,
+                    -1.39361507e01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    4.71255169e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -3.99134789e-01,
+                    1.32004249e02,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    -6.95232739e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    1.54910922e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -5.12330684e-01,
+                    0.00000000e00,
+                ],
+                [
+                    1.24792211e00,
+                    4.45508193e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -1.40092526e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    1.24792211e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -5.12330684e-01,
+                    5.12330684e-01,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -9.92384535e-03,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -5.12330684e-01,
+                ],
+            ]
+        ),
+        "C": np.array(
+            [
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -1.30684207e02,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    1.52674544e-02,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    9.92384535e-03,
+                    0.00000000e00,
+                    0.00000000e00,
+                    1.52674544e-02,
+                    4.97063230e-01,
+                ],
+            ]
+        ),
+        "D": np.array([[1.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]),
+    },
+    "rbc_2_block_ss.gcn": {
+        "param_dict": {
+            "beta": 0.985,
+            "delta": 0.025,
+            "sigma_C": 2,
+            "sigma_L": 1.5,
+            "alpha": 0.35,
+            "rho_A": 0.95,
+        },
+        "P": np.array([[0.95000000, 0.0000000], [0.08887552, 0.9614003]]),
+        "Q": np.array([[1.00000000], [0.09355318]]),
+        # TODO: Investigate sign flip on row 5, 6 (TC, U)
+        "R": np.array(
+            [
+                [0.3437521, 0.3981261],
+                [3.5550207, -0.5439888],
+                [0.1418896, -0.2412174],
+                [1.0422283, 0.1932087],
+                [-0.2127497, -0.1270917],
+                [1.0422282, 0.1932087],
+                [-0.6875042, -0.7962522],
+                [-0.6875042, -0.7962522],
+                [1.0422284, -0.8067914],
+                [0.9003386, 0.4344261],
+            ]
+        ),
+        "S": np.array(
+            [
+                [0.3618443],
+                [3.7421271],
+                [0.1493575],
+                [1.0970824],
+                [-0.2239471],
+                [1.0970823],
+                [-0.7236886],
+                [-0.7236886],
+                [1.0970825],
+                [0.9477249],
+            ]
+        ),
+        "A": np.array(
+            [
+                [0.0, 0.0, 0.0, 0.81816881, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 19.82962462, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.95, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.81816881, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, -0.81816881, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, -0.02614848, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.72926415, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            ]
+        ),
+        "B": np.array(
+            [
+                [
+                    0.00000000e00,
+                    -1.82917327e00,
+                    -5.08451913e-01,
+                    0.00000000e00,
+                    1.51945637e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    8.18168815e-01,
+                    1.51945637e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    5.08451913e-01,
+                    -2.03380765e01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    5.46695065e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -4.54128273e-01,
+                    0.00000000e00,
+                    4.85564249e01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -2.98875494e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    -5.97750987e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -2.98875494e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -9.34110779e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    6.22740519e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    6.22740519e-01,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -2.98875494e-01,
+                    2.98875494e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    -1.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    2.33762519e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    1.51945637e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -2.33762519e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -1.51945637e00,
+                    2.33762519e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -8.18168815e-01,
+                    -1.51945637e00,
+                ],
+                [
+                    4.02284264e-02,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    2.61484772e-02,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -4.02284264e-02,
+                    0.00000000e00,
+                ],
+                [
+                    2.08361185e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -7.29264146e-01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -2.08361185e00,
+                ],
+            ]
+        ),
+        "C": np.array(
+            [
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    -4.78280785e01,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    1.18429414e-02,
+                    2.87032552e-01,
+                    1.18429414e-02,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+            ]
+        ),
+        "D": np.array(
+            [
+                [0.0],
+                [0.0],
+                [0.0],
+                [0.0],
+                [0.0],
+                [0.0],
+                [0.0],
+                [1.0],
+                [0.0],
+                [0.0],
+                [0.0],
+                [0.0],
+            ]
+        ),
+    },
+    "full_nk.gcn": {
+        "param_dict": {
+            "delta": 0.025,
+            "beta": 0.99,
+            "sigma_C": 2,
+            "sigma_L": 1.5,
+            "gamma_I": 10,
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+                ],
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+                ],
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+                    0.00000000e00,
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+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+                [
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+                    0.00000000e00,
+                    0.00000000e00,
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+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                    0.00000000e00,
+                ],
+            ]
+        ),
+        "D": np.array(
+            [
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 1.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 1.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0],
+                [1.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 1.0, 0.0],
+            ]
+        ),
+    },
+}
diff --git a/tests/utilities/load_dynare.py b/tests/utilities/load_dynare.py
new file mode 100644
index 0000000..9005806
--- /dev/null
+++ b/tests/utilities/load_dynare.py
@@ -0,0 +1,84 @@
+import os
+
+from collections.abc import Sequence
+
+import numpy as np
+import pandas as pd
+import scipy.io as sio
+
+
+def squeeze_record(x):
+    if hasattr(x, "__len__") and len(x) == 1:
+        try:
+            return squeeze_record(x[0])
+        except (IndexError, TypeError):
+            pass
+    return x
+
+
+def record_to_dict(x):
+    if x.dtype.names is not None:
+        return dict(zip(x.dtype.names, x))
+    return x
+
+
+def get_available_models():
+    dynare_output_dir = "tests/dynare_outputs"
+    mat_files = os.listdir(dynare_output_dir)
+    models = [x.replace("_results.mat", "") for x in mat_files]
+    return {
+        model: os.path.join(dynare_output_dir, fname)
+        for model, fname in zip(models, mat_files)
+    }
+
+
+def read_dynare_output(
+    model_name,
+) -> tuple[dict[str, np.ndarray], dict[str, np.ndarray]]:
+    models = get_available_models()
+    path = models[model_name]
+
+    dynare_data = sio.loadmat(path)
+
+    oo = record_to_dict(squeeze_record(dynare_data["oo_"]))
+    for key, value in oo.items():
+        oo[key] = record_to_dict(squeeze_record(value))
+
+    M = record_to_dict(squeeze_record(dynare_data["M_"]))
+    for k, v in M.items():
+        M[k] = squeeze_record(v)
+
+    return oo, M
+
+
+def extract_policy_matrices(oo, M) -> tuple[pd.DataFrame, pd.DataFrame]:
+    var_names = np.concatenate([x.item() for x in M["endo_names"]])
+    shock_names = np.concatenate(
+        [np.atleast_1d(x.item()) for x in np.atleast_1d(M["exo_names"])]
+    )
+    state_idx = M["state_var"] - 1
+    dynare_order = oo["dr"]["order_var"].ravel() - 1
+
+    dr_state_idx = np.array([x for x in dynare_order if x in state_idx])
+
+    dynare_T = pd.DataFrame(
+        oo["dr"]["ghx"], index=var_names[dynare_order], columns=var_names[dr_state_idx]
+    )
+    dynare_R = pd.DataFrame(
+        oo["dr"]["ghu"], index=var_names[dynare_order], columns=shock_names
+    )
+
+    return dynare_T, dynare_R
+
+
+def load_dynare_outputs(model_name) -> dict[str, pd.DataFrame]:
+    models = get_available_models()
+    if model_name not in models:
+        raise ValueError(
+            f"Model {model_name} not found. Available models are {models.keys()}"
+        )
+
+    oo, M = read_dynare_output(model_name)
+    T, R = extract_policy_matrices(oo, M)
+
+    return {"T": T, "R": R}
diff --git a/tests/utilities/shared_fixtures.py b/tests/utilities/shared_fixtures.py
new file mode 100644
index 0000000..2979545
--- /dev/null
+++ b/tests/utilities/shared_fixtures.py
@@ -0,0 +1,30 @@
+import os
+
+from functools import cache
+
+from gEconpy import model_from_gcn, statespace_from_gcn
+
+
+@cache
+def load_and_cache_model(gcn_file, backend, use_jax=False):
+    compile_kwargs = {}
+    if backend == "pytensor" and use_jax:
+        compile_kwargs["mode"] = "JAX"
+
+    gcn_path = os.path.join("tests", "Test GCNs", gcn_file)
+    model = model_from_gcn(
+        gcn_path,
+        verbose=False,
+        backend=backend,
+        **compile_kwargs,
+    )
+
+    return model
+
+
+@cache
+def load_and_cache_statespace(gcn_file):
+    gcn_path = os.path.join("tests", "Test GCNs", gcn_file)
+    statespace = statespace_from_gcn(gcn_path, verbose=False)
+
+    return statespace