diff --git a/code/do_all.py b/code/do_all.py index 0d75360..f511286 100644 --- a/code/do_all.py +++ b/code/do_all.py @@ -99,15 +99,15 @@ def run_replication(): replication_specs = {} if which_model == "1" or which_model == "": - replication_specs["init_agent_name"] = "IndShock" + replication_specs["agent_name"] = "IndShock" elif which_model == "2": - replication_specs["init_agent_name"] = "Portfolio" + replication_specs["agent_name"] = "Portfolio" elif which_model == "3": - replication_specs["init_agent_name"] = "WarmGlow" + replication_specs["agent_name"] = "WarmGlow" elif which_model == "4": - replication_specs["init_agent_name"] = "WarmGlowPortfolio" + replication_specs["agent_name"] = "WarmGlowPortfolio" elif which_model == "5": - replication_specs["init_agent_name"] = "WealthPortfolio" + replication_specs["agent_name"] = "WealthPortfolio" else: print("Invalid model choice.") return diff --git a/code/estimark/chris_parameters.py b/code/estimark/chris_parameters.py index 4a2764c..b9dc107 100644 --- a/code/estimark/chris_parameters.py +++ b/code/estimark/chris_parameters.py @@ -55,7 +55,6 @@ bootstrap_size = 50 # Number of re-estimations to do during bootstrap seed = 1132023 # Just an integer to seed the estimation - params_to_estimate = ["CRRA"] # Initial guess of the coefficient of relative risk aversion during estimation (rho) init_CRRA = 5.0 @@ -69,7 +68,6 @@ init_WealthShare = 0.5 # Initial guess of the wealth share parameter bounds_WealthShare = [0.0, 1.0] # Bounds for the wealth share parameter - ###################################################################### # Constructed parameters ###################################################################### @@ -114,7 +112,6 @@ retirement_age + age_interval + 1, ) # only match ages 71 and older - init_params_options = { "init_guess": { "CRRA": init_CRRA, @@ -133,7 +130,6 @@ }, } - # Survival probabilities over the lifecycle liv_prb = parse_ssa_life_table( female=False, diff --git a/code/estimark/estimation.py b/code/estimark/estimation.py index 5a77f12..dde09bb 100644 --- a/code/estimark/estimation.py +++ b/code/estimark/estimation.py @@ -34,6 +34,7 @@ # Parameters for the consumer type and the estimation from estimark.parameters import ( age_mapping, + bootstrap_options, init_calibration, init_params_options, init_subjective_labor, @@ -46,40 +47,32 @@ from estimark.scf import scf_data from estimark.snp import snp_data - # ===================================================== # Define objects and functions used for the estimation # ===================================================== +agent_types = { + "IndShock": IndShkLifeCycleConsumerType, + "Portfolio": PortfolioLifeCycleConsumerType, + "WarmGlow": BequestWarmGlowLifeCycleConsumerType, + "WarmGlowPortfolio": BequestWarmGlowLifeCyclePortfolioType, + "WealthPortfolio": WealthPortfolioLifeCycleConsumerType, +} -def make_agent( - init_agent_name, - subjective_stock=False, - subjective_labor=False, -): - if init_agent_name == "IndShock": - agent_type = IndShkLifeCycleConsumerType - elif init_agent_name == "Portfolio": - agent_type = PortfolioLifeCycleConsumerType - elif init_agent_name == "WarmGlow": - agent_type = BequestWarmGlowLifeCycleConsumerType - elif init_agent_name == "WarmGlowPortfolio": - agent_type = BequestWarmGlowLifeCyclePortfolioType - elif init_agent_name == "WealthPortfolio": - agent_type = WealthPortfolioLifeCycleConsumerType + +def make_agent(agent_name): + for key, value in agent_types.items(): + if key in agent_name: + agent_type = value + break calibration = init_calibration.copy() - agent_name = init_agent_name - if subjective_stock or subjective_labor: - agent_name += "Sub" - if subjective_stock: - agent_name += "(Stock)" + if "Sub" in agent_name: + if "(Stock)" in agent_name: calibration.update(init_subjective_stock) - if subjective_labor: - agent_name += "(Labor)" + if "(Labor)" in agent_name: calibration.update(init_subjective_labor) - agent_name += "Market" # Make a lifecycle consumer to be used for estimation, including simulated # shocks (plus an initial distribution of wealth) @@ -111,7 +104,7 @@ def weighted_median(values, weights): def get_weighted_moments( data, - variable, + variable=None, weights=None, groups=None, mapping=None, @@ -154,7 +147,6 @@ def get_moments_cov(agent_name, emp_moments): ) if "Port" in agent_name: - for key1 in emp_moments: # Check if key1 exists in moments_cov dictionary if key1 not in moments_cov: @@ -201,10 +193,9 @@ def get_empirical_moments(agent_name): return emp_moments -def get_initial_guess(agent_name, params_to_estimate, dir): +def get_initial_guess(agent_name, params_to_estimate, save_dir): # start from previous estimation results if available - csv_file_path = f"{dir}{agent_name}_estimate_results.csv" - initial_guess = {} + csv_file_path = save_dir / (agent_name + "_estimate_results.csv") try: res = pd.read_csv(csv_file_path, header=None) @@ -213,9 +204,8 @@ def get_initial_guess(agent_name, params_to_estimate, dir): temp_dict = init_params_options.get("init_guess", {}) initial_guess = { - key: float(value) - for key, value in temp_dict.items() - if key in params_to_estimate + key: float(temp_dict.get(key, init_params_options["init_guess"][key])) + for key in params_to_estimate } return initial_guess @@ -285,7 +275,7 @@ def simulate_moments(params, agent=None, emp_moments=None): return sim_moments -def smm_obj_func(params, agent, emp_moments): +def msm_criterion(params, agent=None, emp_moments=None): """The objective function for the SMM estimation. Given values of discount factor adjuster DiscFac, coeffecient of relative risk aversion CRRA, a base consumer agent type, empirical data, and calibrated parameters, this function calculates @@ -422,7 +412,7 @@ def calculate_se_bootstrap( # Estimate the model with the bootstrap data and add to list of estimates this_estimate = em.minimize( - smm_obj_func, + msm_criterion, initial_estimate, criterion_kwargs={"agent": agent, "emp_moments": bootstrap_moments}, **minimize_options, @@ -450,7 +440,13 @@ def calculate_se_bootstrap( def do_estimate_model( - agent, emp_moments, initial_guess, minimize_options=None, tables_dir=None + agent, + initial_guess, + estimate_method="min", + emp_moments=None, + moments_cov=None, + minimize_options=None, + save_dir=None, ): fmt_init_guess = [f"{key} = {value:.3f}" for key, value in initial_guess.items()] multistart_text = " with multistart" if minimize_options.get("multistart") else "" @@ -468,58 +464,51 @@ def do_estimate_model( print(statement2) print(dash_line) - t_start_estimate = time() - res = em.minimize( - smm_obj_func, - initial_guess, - criterion_kwargs={"agent": agent, "emp_moments": emp_moments}, - upper_bounds={ - key: value - for key, value in init_params_options["upper_bounds"].items() - if key in initial_guess - }, - lower_bounds={ - key: value - for key, value in init_params_options["lower_bounds"].items() - if key in initial_guess - }, - **minimize_options, - ) - t_end_estimate = time() - time_to_estimate = t_end_estimate - t_start_estimate + upper_bounds = { + key: value + for key, value in init_params_options["upper_bounds"].items() + if key in initial_guess + } - keys_to_save = [ - key - for key in vars(res) - if key - not in ["history", "convergence_report", "multistart_info", "algorithm_output"] - ] + lower_bounds = { + key: value + for key, value in init_params_options["lower_bounds"].items() + if key in initial_guess + } - model_estimate = save_results( - res, - agent.name, - time_to_estimate, - tables_dir, - params_key="params", - keys_to_save=keys_to_save, - ) + estimagic_options = {"upper_bounds": upper_bounds, "lower_bounds": lower_bounds} - return model_estimate, time_to_estimate + if estimate_method == "min": + res, time_to_estimate = estimate_min( + agent, + msm_criterion, + initial_guess, + emp_moments, + minimize_options, + estimagic_options=estimagic_options, + ) + model_estimate = res.params + + elif estimate_method == "msm": + res, time_to_estimate = estimate_msm( + agent, + simulate_moments, + emp_moments, + moments_cov, + initial_guess, + minimize_options, + estimagic_options=estimagic_options, + ) -def save_results( - res, - agent_name, - time_to_estimate, - dir, - params_key=None, - keys_to_save=None, -): - model_estimate = getattr(res, params_key) + model_estimate = res._params + + else: + raise ValueError(f"Invalid estimate_method: {estimate_method}") # Calculate minutes and remaining seconds minutes, seconds = divmod(time_to_estimate, 60) - statement1 = f"Estimated model: {agent_name}" + statement1 = f"Estimated model: {agent.name}" statement2 = f"Time to estimate: {int(minutes)} min, {int(seconds)} sec." estimates = [f"{key} = {value:.3f}" for key, value in model_estimate.items()] statement3 = "Estimated values: " + ", ".join(estimates) @@ -530,7 +519,14 @@ def save_results( print("-" * dash_len) # Create the simple estimate table - estimate_results_file = dir + agent_name + "_estimate_results.csv" + estimate_results_file = save_dir / (agent.name + "_estimate_results.csv") + + keys_to_save = [ + key + for key in vars(res) + if key + not in ["history", "convergence_report", "multistart_info", "algorithm_output"] + ] with open(estimate_results_file, "w") as f: writer = csv.writer(f) @@ -544,7 +540,7 @@ def save_results( for key in keys_to_save: writer.writerow([key, getattr(res, key)]) - return model_estimate + return model_estimate, res, time_to_estimate def do_compute_se_boostrap( @@ -553,7 +549,7 @@ def do_compute_se_boostrap( time_to_estimate, bootstrap_size=50, seed=0, - tables_dir=None, + save_dir=None, ): # Estimate the model: print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~") @@ -584,7 +580,7 @@ def do_compute_se_boostrap( print(f"Standard errors: DiscFac--> {std_errors[0]}, CRRA--> {std_errors[1]}") # Create the simple bootstrap table - bootstrap_results_file = tables_dir + agent.name + "_bootstrap_results.csv" + bootstrap_results_file = save_dir + agent.name + "_bootstrap_results.csv" with open(bootstrap_results_file, "w") as f: writer = csv.writer(f) @@ -601,7 +597,7 @@ def do_compute_se_boostrap( ) -def do_compute_sensitivity(agent, model_estimate, emp_moments, figures_dir=None): +def do_compute_sensitivity(agent, model_estimate, emp_moments, save_dir=None): print("``````````````````````````````````````````````````````````````````````") print("Computing sensitivity measure.") print("``````````````````````````````````````````````````````````````````````") @@ -640,14 +636,14 @@ def do_compute_sensitivity(agent, model_estimate, emp_moments, figures_dir=None) axs[1].set_ylabel("Sensitivity") axs[1].set_xlabel("Median W/Y Ratio") - plt.savefig(figures_dir + agent.name + "Sensitivity.pdf") - plt.savefig(figures_dir + agent.name + "Sensitivity.png") - plt.savefig(figures_dir + agent.name + "Sensitivity.svg") + plt.savefig(save_dir + agent.name + "Sensitivity.pdf") + plt.savefig(save_dir + agent.name + "Sensitivity.png") + plt.savefig(save_dir + agent.name + "Sensitivity.svg") plt.show() -def do_make_contour_plot(agent, model_estimate, emp_moments, figures_dir=None): +def do_make_contour_plot(agent, model_estimate, emp_moments, save_dir=None): print("``````````````````````````````````````````````````````````````````````") print("Creating the contour plot.") print("``````````````````````````````````````````````````````````````````````") @@ -667,7 +663,7 @@ def do_make_contour_plot(agent, model_estimate, emp_moments, figures_dir=None): DiscFac = DiscFac_list[j] for k in range(grid_density): CRRA = CRRA_list[k] - smm_obj_levels[j, k] = smm_obj_func( + smm_obj_levels[j, k] = msm_criterion( np.array([DiscFac, CRRA]), agent=agent, emp_moments=emp_moments, @@ -684,7 +680,201 @@ def do_make_contour_plot(agent, model_estimate, emp_moments, figures_dir=None): plt.plot(model_estimate[1], model_estimate[0], "*r", ms=15) plt.xlabel(r"coefficient of relative risk aversion $\rho$", fontsize=14) plt.ylabel(r"discount factor adjustment $\beth$", fontsize=14) - plt.savefig(figures_dir + agent.name + "SMMcontour.pdf") - plt.savefig(figures_dir + agent.name + "SMMcontour.png") - plt.savefig(figures_dir + agent.name + "SMMcontour.svg") + plt.savefig(save_dir + agent.name + "SMMcontour.pdf") + plt.savefig(save_dir + agent.name + "SMMcontour.png") + plt.savefig(save_dir + agent.name + "SMMcontour.svg") plt.show() + + +def estimate_msm( + agent, + simulate_moments=None, + emp_moments=None, + moments_cov=None, + initial_params=None, + minimize_options=None, + simulate_moments_kwargs=None, + estimagic_options=None, +): + t0 = time() + + simulate_moments_kwargs = simulate_moments_kwargs or {} + simulate_moments_kwargs.setdefault("agent", agent) + simulate_moments_kwargs.setdefault("emp_moments", emp_moments) + + res = em.estimate_msm( + simulate_moments, + emp_moments, + moments_cov, + initial_params, + optimize_options=minimize_options, + simulate_moments_kwargs=simulate_moments_kwargs, + **estimagic_options, + ) + + run_time = time() - t0 + + return res, run_time + + +def estimate_min( + agent, + criterion=None, + initial_params=None, + emp_moments=None, + minimize_options={}, + criterion_kwargs=None, + estimagic_options=None, +): + t0 = time() + + criterion_kwargs = criterion_kwargs or {} + criterion_kwargs.setdefault("agent", agent) + criterion_kwargs.setdefault("emp_moments", emp_moments) + + res = em.minimize( + criterion, + initial_params, + criterion_kwargs=criterion_kwargs, + **minimize_options, + **estimagic_options, + ) + + run_time = time() - t0 + + return res, run_time + + +def estimate( + agent_name, + params_to_estimate, + estimate_model=True, + estimate_method="min", + compute_se_bootstrap=False, + compute_sensitivity=False, + make_contour_plot=False, + save_dir=None, + emp_moments=None, + moments_cov=None, +): + """Run the main estimation procedure for EstimatingMicroDSOPs. + + Parameters + ---------- + estimate_model : bool + Whether to estimate the model using Nelder-Mead. When True, this is a low-time, low-memory operation. + + compute_standard_errors : bool + Whether to compute standard errors on the estiamtion of the model. + + make_contour_plot : bool + Whether to make the contour plot associate with the estiamte. + + Returns + ------- + None + + """ + save_dir = Path(save_dir).resolve() if save_dir is not None else Path.cwd() + + ############################################################ + # Make agent + ############################################################ + + agent = make_agent(agent_name) + + ############################################################ + # Get initial guess + ############################################################ + + initial_guess = get_initial_guess( + agent_name, + params_to_estimate, + save_dir, + ) + + ############################################################ + # Get empirical moments + ############################################################ + + if emp_moments is None: + emp_moments = get_empirical_moments(agent_name) + + print("Calculated empirical moments.") + + ############################################################ + # Get moments covariance matrix + ############################################################ + + if moments_cov is None and estimate_method == "msm": + moments_cov = get_moments_cov(agent_name, emp_moments) + + print("Calculated moments covariance matrix.") + + ############################################################ + # Estimate model + ############################################################ + + if estimate_model: + model_estimate, res, time_to_estimate = do_estimate_model( + agent, + initial_guess, + estimate_method=estimate_method, + emp_moments=emp_moments, + moments_cov=moments_cov, + minimize_options=minimize_options, + save_dir=save_dir, + ) + + # Compute standard errors by bootstrap + if compute_se_bootstrap: + do_compute_se_boostrap( + agent, + model_estimate, + time_to_estimate, + save_dir=save_dir, + **bootstrap_options, + ) + + # Compute sensitivity measure + if compute_sensitivity: + do_compute_sensitivity( + agent, + model_estimate, + initial_guess, + save_dir=save_dir, + ) + + # Make a contour plot of the objective function + if make_contour_plot: + do_make_contour_plot( + agent, + model_estimate, + emp_moments, + save_dir=save_dir, + ) + + +if __name__ == "__main__": + # Set booleans to determine which tasks should be done + # Which agent type to estimate ("IndShock" or "Portfolio") + local_agent_name = "IndShock" + local_params_to_estimate = ["CRRA", "DiscFac"] + local_estimate_model = True # Whether to estimate the model + # Whether to get standard errors via bootstrap + local_compute_se_bootstrap = False + # Whether to compute a measure of estimates' sensitivity to moments + local_compute_sensitivity = False + # Whether to make a contour map of the objective function + local_make_contour_plot = False + local_save_dir = "content/tables/min" + + estimate( + agent_name=local_agent_name, + params_to_estimate=local_params_to_estimate, + estimate_model=local_estimate_model, + compute_se_bootstrap=local_compute_se_bootstrap, + compute_sensitivity=local_compute_sensitivity, + make_contour_plot=local_make_contour_plot, + save_dir=local_save_dir, + ) diff --git a/code/estimark/min.py b/code/estimark/min.py deleted file mode 100644 index f6adea7..0000000 --- a/code/estimark/min.py +++ /dev/null @@ -1,144 +0,0 @@ -from estimark.estimation import ( - do_compute_se_boostrap, - do_compute_sensitivity, - do_estimate_model, - do_make_contour_plot, - get_empirical_moments, - get_initial_guess, - make_agent, -) -from estimark.parameters import bootstrap_options, minimize_options -from pathlib import Path - - -# Pathnames to the other files: -# Relative directory for primitive parameter files -tables_dir = "content/tables/min/" -Path(tables_dir).mkdir(parents=True, exist_ok=True) -# Relative directory for primitive parameter files -figures_dir = "content/figures/min/" -Path(figures_dir).mkdir(parents=True, exist_ok=True) - - -def estimate_min( - init_agent_name, - params_to_estimate, - estimate_model=True, - compute_se_bootstrap=False, - compute_sensitivity=False, - make_contour_plot=False, - subjective_stock=False, - subjective_labor=False, -): - """Run the main estimation procedure for SolvingMicroDSOP. - - Parameters - ---------- - estimate_model : bool - Whether to estimate the model using Nelder-Mead. When True, this is a low-time, low-memory operation. - - compute_standard_errors : bool - Whether to compute standard errors on the estiamtion of the model. - - make_contour_plot : bool - Whether to make the contour plot associate with the estiamte. - - Returns - ------- - None - - """ - ############################################################ - # Make agent - ############################################################ - - agent = make_agent( - init_agent_name=init_agent_name, - subjective_stock=subjective_stock, - subjective_labor=subjective_labor, - ) - - ############################################################ - # Get empirical moments - ############################################################ - - emp_moments = get_empirical_moments(agent.name) - - ############################################################ - # Get initial guess - ############################################################ - - initial_guess = get_initial_guess( - agent.name, - params_to_estimate, - tables_dir, - ) - - ############################################################ - # Estimate model - ############################################################ - - if estimate_model: - model_estimate, time_to_estimate = do_estimate_model( - agent, - emp_moments, - initial_guess, - minimize_options=minimize_options, - tables_dir=tables_dir, - ) - - # Compute standard errors by bootstrap - if compute_se_bootstrap: - do_compute_se_boostrap( - agent, - model_estimate, - time_to_estimate, - tables_dir=tables_dir, - **bootstrap_options, - ) - - # Compute sensitivity measure - if compute_sensitivity: - do_compute_sensitivity( - agent, - model_estimate, - initial_guess, - figures_dir=figures_dir, - ) - - # Make a contour plot of the objective function - if make_contour_plot: - do_make_contour_plot( - agent, - model_estimate, - emp_moments, - figures_dir=figures_dir, - ) - - -if __name__ == "__main__": - # Set booleans to determine which tasks should be done - # Which agent type to estimate ("IndShock" or "Portfolio") - local_agent_name = "Portfolio" - local_params_to_estimate = ["CRRA", "DiscFac"] - local_estimate_model = True # Whether to estimate the model - # Whether to get standard errors via bootstrap - local_compute_se_bootstrap = False - # Whether to compute a measure of estimates' sensitivity to moments - local_compute_sensitivity = False - # Whether to make a contour map of the objective function - local_make_contour_plot = False - # Whether to use subjective beliefs - local_subjective_stock = False - local_subjective_labor = False - - estimate_min( - init_agent_name=local_agent_name, - params_to_estimate=local_params_to_estimate, - estimate_model=local_estimate_model, - compute_se_bootstrap=local_compute_se_bootstrap, - compute_sensitivity=local_compute_sensitivity, - make_contour_plot=local_make_contour_plot, - subjective_stock=local_subjective_stock, - subjective_labor=local_subjective_labor, - ) diff --git a/code/estimark/msm.py b/code/estimark/msm.py deleted file mode 100644 index 0e1cbe3..0000000 --- a/code/estimark/msm.py +++ /dev/null @@ -1,130 +0,0 @@ -from pathlib import Path -from time import time - -import estimagic as em - -from estimark.estimation import ( - get_empirical_moments, - get_initial_guess, - get_moments_cov, - make_agent, - save_results, - simulate_moments, -) - -# Parameters for the consumer type and the estimation -from estimark.parameters import ( - init_params_options, - minimize_options, -) - -# SCF 2004 data on household wealth - -# Pathnames to the other files: -# Relative directory for primitive parameter files -tables_dir = "content/tables/msm/" -Path(tables_dir).mkdir(parents=True, exist_ok=True) -# Relative directory for primitive parameter files -figures_dir = "content/figures/msm/" -Path(figures_dir).mkdir(parents=True, exist_ok=True) - - -def estimate_msm( - init_agent_name, - params_to_estimate=None, - subjective_stock=False, - subjective_labor=False, - emp_moments=None, - moments_cov=None, -): - ############################################################ - # Make agent - ############################################################ - - agent = make_agent( - init_agent_name=init_agent_name, - subjective_stock=subjective_stock, - subjective_labor=subjective_labor, - ) - - print("Agent created: ", agent.name) - - ############################################################ - # Get initial guess - ############################################################ - - initial_guess = get_initial_guess(agent.name, params_to_estimate, tables_dir) - - print(initial_guess) - - ############################################################ - # Get empirical moments - ############################################################ - - if emp_moments is None: - - emp_moments = get_empirical_moments(agent.name) - - print("Calculated empirical moments.") - - ############################################################ - # Get moments covariance matrix - ############################################################ - - if moments_cov is None: - - moments_cov = get_moments_cov(agent.name, emp_moments) - - print("Calculated moments covariance matrix.") - - print("Estimating MSM...") - - upper_bounds = { - key: value - for key, value in init_params_options["upper_bounds"].items() - if key in initial_guess - } - - lower_bounds = { - key: value - for key, value in init_params_options["lower_bounds"].items() - if key in initial_guess - } - - t0 = time() - - res = em.estimate_msm( - simulate_moments, - emp_moments, - moments_cov, - initial_guess, - upper_bounds=upper_bounds, - lower_bounds=lower_bounds, - optimize_options=minimize_options, - simulate_moments_kwargs={"agent": agent, "emp_moments": emp_moments}, - ) - - time_to_estimate = time() - t0 - - print("MSM estimation complete.") - - save_results(res, agent.name, time_to_estimate, tables_dir, "_params") - - -if __name__ == "__main__": - # Set booleans to determine which tasks should be done - # Which agent type to estimate ("IndShock" or "Portfolio") - local_agent_name = "WealthPortfolio" - - # Whether to use subjective beliefs - local_subjective_stock = False - local_subjective_labor = False - - local_params_to_estimate = ["CRRA", "DiscFac", "WealthShare"] - - estimate_msm( - init_agent_name=local_agent_name, - params_to_estimate=local_params_to_estimate, - subjective_stock=local_subjective_stock, - subjective_labor=local_subjective_labor, - ) diff --git a/code/estimark/options.py b/code/estimark/options.py index 617796e..2d9ed9c 100644 --- a/code/estimark/options.py +++ b/code/estimark/options.py @@ -1,7 +1,7 @@ # Define settings for "main()" function in StructuralEstiamtion.py based on # resource requirements: -params_to_estimate = ["CRRA"] +params_to_estimate = ["CRRA", "DiscFac"] low_resource = { "estimate_model": True, @@ -9,8 +9,6 @@ "make_contour_plot": False, "compute_se_bootstrap": False, "compute_sensitivity": False, - "subjective_stock": False, - "subjective_labor": False, } # Author note: # This takes approximately 90 seconds on a laptop with the following specs: @@ -22,8 +20,6 @@ "make_contour_plot": True, "compute_se_bootstrap": False, "compute_sensitivity": True, - "subjective_stock": False, - "subjective_labor": False, } # Author note: # This takes approximately 7 minutes on a laptop with the following specs: @@ -35,8 +31,6 @@ "make_contour_plot": False, "compute_se_bootstrap": True, "compute_sensitivity": True, - "subjective_stock": False, - "subjective_labor": False, } # Author note: # This takes approximately 30 minutes on a laptop with the following specs: @@ -48,8 +42,6 @@ "make_contour_plot": True, "compute_se_bootstrap": True, "compute_sensitivity": True, - "subjective_stock": False, - "subjective_labor": False, } # Author note: # This takes approximately 40 minutes on a laptop with the following specs: diff --git a/code/estimark/parameters.py b/code/estimark/parameters.py index 5f79f78..e14a88e 100644 --- a/code/estimark/parameters.py +++ b/code/estimark/parameters.py @@ -40,7 +40,7 @@ # Population age parameters final_age = 120 # Age at which the problem ends (die with certainty) -retirement_age = 125 # Age at which the consumer retires +retirement_age = 65 # Age at which the consumer retires initial_age = 25 # Age at which the consumer enters the model final_age_data = 95 # Age at which the data ends age_interval = 5 # Interval between age groups @@ -55,21 +55,21 @@ bootstrap_size = 50 # Number of re-estimations to do during bootstrap seed = 1132023 # Just an integer to seed the estimation - params_to_estimate = ["CRRA", "DiscFac"] + # Initial guess of the coefficient of relative risk aversion during estimation (rho) init_CRRA = 5.0 +# Bounds for rho; if violated, objective function returns "penalty value" +bounds_CRRA = [1.1, 20.0] + # Initial guess of the adjustment to the discount factor during estimation (beth) init_DiscFac = 1.0 # Bounds for beth; if violated, objective function returns "penalty value" bounds_DiscFac = [0.5, 1.1] -# Bounds for rho; if violated, objective function returns "penalty value" -bounds_CRRA = [1.1, 20.0] init_WealthShare = 0.5 # Initial guess of the wealth share parameter bounds_WealthShare = [0.0, 1.0] # Bounds for the wealth share parameter - ###################################################################### # Constructed parameters ###################################################################### @@ -114,7 +114,6 @@ retirement_age + age_interval + 1, ) # only match ages 71 and older - init_params_options = { "init_guess": { "CRRA": init_CRRA, @@ -133,7 +132,6 @@ }, } - # Survival probabilities over the lifecycle liv_prb = parse_ssa_life_table( female=False, @@ -155,13 +153,13 @@ minimize_options = { "algorithm": "scipy_neldermead", - "multistart": False, + "multistart": True, "error_handling": "continue", "algo_options": { "convergence.absolute_params_tolerance": 1e-3, "convergence.absolute_criterion_tolerance": 1e-3, - "stopping.max_iterations": 100, - "stopping.max_criterion_evaluations": 200, + "stopping.max_iterations": 50, + "stopping.max_criterion_evaluations": 100, # "n_cores": 12, }, # "numdiff_options": {"n_cores": 12}, @@ -204,8 +202,6 @@ "aNrmInit": aNrmInit, } -print(init_calibration["DiscFac"]) - # from Mateo's JMP for College Educated ElnR = 0.020 VlnR = 0.424**2 diff --git a/code/run_all.py b/code/run_all.py index b122acf..66e8ef9 100644 --- a/code/run_all.py +++ b/code/run_all.py @@ -1,12 +1,17 @@ +import itertools + import dask from dask.distributed import Client -from estimark.min import estimate_min -from estimark.options import ( - all_replications, - high_resource, - low_resource, - medium_resource, -) +from estimark.estimation import estimate +from estimark.options import low_resource + +agent_names = [ + "IndShock", + "Portfolio", + "WarmGlow", + "WarmGlowPortfolio", + "WealthPortfolio", +] # Ask the user which replication to run, and run it: @@ -15,62 +20,24 @@ def run_replication(): lazy_results = [] - for i in range(1, 6): - which_model = str(i) - which_replication = "1" - for k in range(1, 5): - subjective_markets = str(k) - - replication_specs = {} - - if which_model == "1" or which_model == "": - replication_specs["init_agent_name"] = "IndShock" - elif which_model == "2": - replication_specs["init_agent_name"] = "Portfolio" - elif which_model == "3": - replication_specs["init_agent_name"] = "WarmGlow" - elif which_model == "4": - replication_specs["init_agent_name"] = "WarmGlowPortfolio" - elif which_model == "5": - replication_specs["init_agent_name"] = "WealthPortfolio" - else: - print("Invalid model choice.") - return - - print("Model: ", replication_specs["init_agent_name"]) - - if which_replication == "q": - return - - elif which_replication == "1" or which_replication == "": - print("Running low-resource replication...") - replication_specs.update(**low_resource) - - elif which_replication == "2": - print("Running medium-resource replication...") - replication_specs.update(**medium_resource) - - elif which_replication == "3": - print("Running high-resource replication...") - replication_specs.update(**high_resource) - - elif which_replication == "4": - print("Running all replications...") - replication_specs.update(**all_replications) - - else: - print("Invalid replication choice.") - return + for agent_name in agent_names: + for sub_stock, sub_labor in itertools.product(range(2), repeat=2): + temp_agent_name = agent_name + if sub_stock or sub_labor: + temp_agent_name += "Sub" + if sub_stock: + temp_agent_name += "(Stock)" + if sub_labor: + temp_agent_name += "(Labor)" + temp_agent_name += "Market" - if subjective_markets == "2" or subjective_markets == "4": - replication_specs["subjective_stock"] = True - print("Adding subjective stock market beliefs...") + replication_specs = low_resource.copy() + replication_specs["agent_name"] = temp_agent_name + replication_specs["save_dir"] = "content/tables/min" - if subjective_markets == "3" or subjective_markets == "4": - replication_specs["subjective_labor"] = True - print("Adding subjective labor market beliefs...") + print("Model: ", replication_specs["agent_name"]) - lazy_result = dask.delayed(estimate_min)(**replication_specs) + lazy_result = dask.delayed(estimate)(**replication_specs) lazy_results.append(lazy_result) dask.compute(*lazy_results) diff --git a/code/run_all_msm.py b/code/run_all_msm.py index 8f42563..8e48a66 100644 --- a/code/run_all_msm.py +++ b/code/run_all_msm.py @@ -1,52 +1,63 @@ -from estimark.msm import estimate_msm -from estimark.estimation import get_empirical_moments, get_moments_cov +import itertools +import dask +from dask.distributed import Client +from estimark.estimation import estimate, get_empirical_moments, get_moments_cov +from estimark.options import low_resource + +agent_names = [ + "IndShock", + "Portfolio", + "WarmGlow", + "WarmGlowPortfolio", + "WealthPortfolio", +] -if __name__ == "__main__": - emp_moments = get_empirical_moments("") +def run_replication(): + inds_emp_moments = get_empirical_moments("IndShock") port_emp_moments = get_empirical_moments("Porfolio") - moments_cov = get_moments_cov("", emp_moments) + inds_moments_cov = get_moments_cov("IndShock", inds_emp_moments) port_moments_cov = get_moments_cov("Porfolio", port_emp_moments) - for i in range(1, 6): - which_model = str(i) - for k in range(1, 5): - subjective_markets = str(k) - - replication_specs = {} + client = Client(threads_per_worker=10, n_workers=20) - if which_model == "1" or which_model == "": - replication_specs["init_agent_name"] = "IndShock" - elif which_model == "2": - replication_specs["init_agent_name"] = "Portfolio" - elif which_model == "3": - replication_specs["init_agent_name"] = "WarmGlow" - elif which_model == "4": - replication_specs["init_agent_name"] = "WarmGlowPortfolio" - elif which_model == "5": - replication_specs["init_agent_name"] = "WealthPortfolio" + lazy_results = [] - print("Model: ", replication_specs["init_agent_name"]) + for agent_name in agent_names: + for sub_stock, sub_labor in itertools.product(range(2), repeat=2): + temp_agent_name = agent_name + if sub_stock or sub_labor: + temp_agent_name += "Sub" + if sub_stock: + temp_agent_name += "(Stock)" + if sub_labor: + temp_agent_name += "(Labor)" + temp_agent_name += "Market" - replication_specs["params_to_estimate"] = ["CRRA", "DiscFac"] + replication_specs = low_resource.copy() + replication_specs["agent_name"] = temp_agent_name + replication_specs["save_dir"] = "content/tables/msm" - if subjective_markets == "2" or subjective_markets == "4": - replication_specs["subjective_stock"] = True - print("Adding subjective stock market beliefs...") + print("Model: ", replication_specs["agent_name"]) - if subjective_markets == "3" or subjective_markets == "4": - replication_specs["subjective_labor"] = True - print("Adding subjective labor market beliefs...") - - if "Portfolio" in replication_specs["init_agent_name"]: + if "Portfolio" in replication_specs["agent_name"]: replication_specs["emp_moments"] = port_emp_moments replication_specs["moments_cov"] = port_moments_cov else: - replication_specs["emp_moments"] = emp_moments - replication_specs["moments_cov"] = moments_cov + replication_specs["emp_moments"] = inds_emp_moments + replication_specs["moments_cov"] = inds_moments_cov + + replication_specs["estimate_method"] = "msm" - estimate_msm(**replication_specs) + lazy_result = dask.delayed(estimate)(**replication_specs) + lazy_results.append(lazy_result) - print("All replications complete.") + dask.compute(*lazy_results) + + client.close() + + +if __name__ == "__main__": + run_replication() diff --git a/code/tests.py b/code/tests.py index bdf5425..5b6fc26 100644 --- a/code/tests.py +++ b/code/tests.py @@ -14,19 +14,19 @@ def test_medium_resource(): def test_portfolio_low_resource(): print("Running medium-resource replication...") - estimate_min(**low_resource, init_agent_name="Portfolio") + estimate_min(**low_resource, agent_name="Portfolio") def test_warmglow_low_resource(): print("Running medium-resource replication...") - estimate_min(**low_resource, init_agent_name="WarmGlow") + estimate_min(**low_resource, agent_name="WarmGlow") def test_warmglowportfolio_low_resource(): print("Running medium-resource replication...") - estimate_min(**low_resource, init_agent_name="WarmGlowPortfolio") + estimate_min(**low_resource, agent_name="WarmGlowPortfolio") def test_wealthportfolio_low_resource(): print("Running medium-resource replication...") - estimate_min(**low_resource, init_agent_name="WealthPortfolio") + estimate_min(**low_resource, agent_name="WealthPortfolio") diff --git a/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv b/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv index d228d73..d4a2d5e 100644 --- a/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/IndShockSub(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,20.0 -time_to_estimate,11.020751476287842 -params,{'CRRA': 20.0} -criterion,544.5282150454914 +CRRA,6.99535023621492 +DiscFac,1.1 +time_to_estimate,409.7684407234192 +params,"{'CRRA': 6.99535023621492, 'DiscFac': 1.1}" +criterion,328.72324824413397 start_criterion,544.7352756884602 -start_params,{'CRRA': 20.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 20.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Optimization terminated successfully. success,True -n_criterion_evaluations,2 +n_criterion_evaluations,95 n_derivative_evaluations, -n_iterations,1 +n_iterations,48 diff --git a/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv index e0b21a6..a6ef586 100644 --- a/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/min/IndShockSub(Stock)(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,20.0 -time_to_estimate,11.078348636627197 -params,{'CRRA': 20.0} -criterion,557.4368802957166 +CRRA,6.039403381347643 +DiscFac,1.1 +time_to_estimate,404.7218601703644 +params,"{'CRRA': 6.039403381347643, 'DiscFac': 1.1}" +criterion,327.9839822607935 start_criterion,557.6869633061931 -start_params,{'CRRA': 20.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 20.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Optimization terminated successfully. success,True -n_criterion_evaluations,2 +n_criterion_evaluations,95 n_derivative_evaluations, -n_iterations,1 +n_iterations,51 diff --git a/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv b/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv index 52417b7..1a1d9f1 100644 --- a/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/IndShockSub(Stock)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.18701171875 -time_to_estimate,70.9310359954834 -params,{'CRRA': 5.18701171875} -criterion,534.3030034631943 +CRRA,6.128384676686723 +DiscFac,0.9858020243819363 +time_to_estimate,417.7602050304413 +params,"{'CRRA': 6.128384676686723, 'DiscFac': 0.9858020243819363}" +criterion,532.5287199600286 start_criterion,534.3030034631943 -start_params,{'CRRA': 5.18701171875} -algorithm,scipy_neldermead +start_params,"{'CRRA': 5.18701171875, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Optimization terminated successfully. success,True -n_criterion_evaluations,24 +n_criterion_evaluations,135 n_derivative_evaluations, -n_iterations,11 +n_iterations,60 diff --git a/content/tables/min/IndShock_estimate_results.csv b/content/tables/min/IndShock_estimate_results.csv index 134ba2a..5afd63e 100644 --- a/content/tables/min/IndShock_estimate_results.csv +++ b/content/tables/min/IndShock_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,4.480079650878906 -time_to_estimate,77.10341334342957 -params,{'CRRA': 4.480079650878906} -criterion,509.2359519070857 -start_criterion,509.2479689674243 -start_params,{'CRRA': 4.533203125} -algorithm,scipy_neldermead +CRRA,6.554924826658689 +DiscFac,0.973698285183154 +time_to_estimate,372.32231283187866 +params,"{'CRRA': 6.554924826658689, 'DiscFac': 0.973698285183154}" +criterion,501.1282648355349 +start_criterion,501.13337610997075 +start_params,"{'CRRA': 6.4774383682596195, 'DiscFac': 0.9738533545285482}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Optimization terminated successfully. success,True -n_criterion_evaluations,26 +n_criterion_evaluations,118 n_derivative_evaluations, -n_iterations,11 +n_iterations,53 diff --git a/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv index d311280..faf3973 100644 --- a/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/PortfolioSub(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ CRRA,5.0 -time_to_estimate,303.5230276584625 -params,{'CRRA': 5.0} -criterion,299.21003076569866 +DiscFac,1.0 +time_to_estimate,699.401106595993 +params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +criterion,1.357812612728672e+23 start_criterion,27.654229740170432 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,33 +n_iterations,50 diff --git a/content/tables/min/PortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/PortfolioSub(Stock)(Labor)Market_estimate_results.csv index 61514cd..f7f847e 100644 --- a/content/tables/min/PortfolioSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/min/PortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ CRRA,5.0 -time_to_estimate,303.72291469573975 -params,{'CRRA': 5.0} -criterion,188.582512648356 +DiscFac,1.0 +time_to_estimate,705.3598313331604 +params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +criterion,1.3578126127559742e+23 start_criterion,2.7065370680320946 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,33 +n_iterations,50 diff --git a/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv index 3a85660..4103fc9 100644 --- a/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/PortfolioSub(Stock)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,555.7875609397888 -params,{'CRRA': 5.0} -criterion,1437.251591459825 +CRRA,10.549999999999999 +DiscFac,0.8 +time_to_estimate,560.1380150318146 +params,"{'CRRA': 10.549999999999999, 'DiscFac': 0.8}" +criterion,1.3578126127721387e+23 start_criterion,1.9914814501483995 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,67 +n_iterations,50 diff --git a/content/tables/min/Portfolio_estimate_results.csv b/content/tables/min/Portfolio_estimate_results.csv index 0efc253..7535df8 100644 --- a/content/tables/min/Portfolio_estimate_results.csv +++ b/content/tables/min/Portfolio_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,554.6812260150909 -params,{'CRRA': 5.0} -criterion,2697.8131842097837 +CRRA,10.549999999999999 +DiscFac,0.8 +time_to_estimate,558.4334774017334 +params,"{'CRRA': 10.549999999999999, 'DiscFac': 0.8}" +criterion,1.357812612759542e+23 start_criterion,9.958770649461421 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,67 +n_iterations,50 diff --git a/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv index 6e1d2df..7acade2 100644 --- a/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ CRRA,5.0 -time_to_estimate,304.4725878238678 -params,{'CRRA': 5.0} -criterion,299.2107613039726 -start_criterion,27.654233098944356 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +DiscFac,1.0 +time_to_estimate,706.459222316742 +params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +criterion,1.357812612728672e+23 +start_criterion,27.654229740170432 +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,33 +n_iterations,50 diff --git a/content/tables/min/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv index bfe0b80..9ded056 100644 --- a/content/tables/min/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ CRRA,5.0 -time_to_estimate,304.65295696258545 -params,{'CRRA': 5.0} -criterion,188.58326356937928 -start_criterion,2.706536796979954 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +DiscFac,1.0 +time_to_estimate,706.1407573223114 +params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +criterion,1.3578126127559742e+23 +start_criterion,2.7065370680320946 +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,33 +n_iterations,50 diff --git a/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv index e25b6a8..4566a72 100644 --- a/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,563.1510374546051 -params,{'CRRA': 5.0} -criterion,1437.3200070200442 -start_criterion,1.9915366998843695 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +CRRA,10.549999999999999 +DiscFac,0.8 +time_to_estimate,566.8561675548553 +params,"{'CRRA': 10.549999999999999, 'DiscFac': 0.8}" +criterion,1.3578126127721387e+23 +start_criterion,1.9914814501483995 +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,67 +n_iterations,50 diff --git a/content/tables/min/WarmGlowPortfolio_estimate_results.csv b/content/tables/min/WarmGlowPortfolio_estimate_results.csv index b7d1992..0fa58bf 100644 --- a/content/tables/min/WarmGlowPortfolio_estimate_results.csv +++ b/content/tables/min/WarmGlowPortfolio_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,556.5587425231934 -params,{'CRRA': 5.0} -criterion,2697.9261553147708 -start_criterion,9.958831069266239 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +CRRA,10.549999999999999 +DiscFac,0.8 +time_to_estimate,562.9282176494598 +params,"{'CRRA': 10.549999999999999, 'DiscFac': 0.8}" +criterion,1.357812612759542e+23 +start_criterion,9.958770649461421 +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,67 +n_iterations,50 diff --git a/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv index cf46f06..143c27d 100644 --- a/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowSub(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,296.93684434890747 -params,{'CRRA': 5.0} -criterion,nan +CRRA,3.941456064122032 +DiscFac,1.0541189928539083 +time_to_estimate,665.4215857982635 +params,"{'CRRA': 3.941456064122032, 'DiscFac': 1.0541189928539083}" +criterion,329.69378666903975 start_criterion,708.2306785941182 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 -message,Maximum number of function evaluations has been exceeded. +n_free,2 +message,Maximum number of iterations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,190 n_derivative_evaluations, -n_iterations,33 +n_iterations,75 diff --git a/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv index 0509061..b431ab5 100644 --- a/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,6.385883745923638 -time_to_estimate,161.79646944999695 -params,{'CRRA': 6.385883745923638} -criterion,774.1717356893752 -start_criterion,773.9992999152189 -start_params,{'CRRA': 6.3858642578125} -algorithm,scipy_neldermead +CRRA,4.713707808457082 +DiscFac,1.0766448766196575 +time_to_estimate,683.8176023960114 +params,"{'CRRA': 4.713707808457082, 'DiscFac': 1.0766448766196575}" +criterion,324.2044841951629 +start_criterion,773.998905502008 +start_params,"{'CRRA': 6.385883745923638, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Optimization terminated successfully. success,True -n_criterion_evaluations,44 +n_criterion_evaluations,198 n_derivative_evaluations, -n_iterations,15 +n_iterations,74 diff --git a/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv b/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv index 491f98c..fbf038b 100644 --- a/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/WarmGlowSub(Stock)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,567.985330581665 -params,{'CRRA': 5.0} -criterion,nan +CRRA,3.6329187987104508 +DiscFac,1.0045109098544347 +time_to_estimate,466.4377808570862 +params,"{'CRRA': 3.6329187987104508, 'DiscFac': 1.0045109098544347}" +criterion,506.5322132229072 start_criterion,nan -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 -message,Maximum number of function evaluations has been exceeded. -success,False -n_criterion_evaluations,200 +n_free,2 +message,Optimization terminated successfully. +success,True +n_criterion_evaluations,152 n_derivative_evaluations, -n_iterations,67 +n_iterations,79 diff --git a/content/tables/min/WarmGlow_estimate_results.csv b/content/tables/min/WarmGlow_estimate_results.csv index e62604e..4a8785a 100644 --- a/content/tables/min/WarmGlow_estimate_results.csv +++ b/content/tables/min/WarmGlow_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,256.98922204971313 -params,{'CRRA': 5.0} -criterion,nan +CRRA,3.2407994059482403 +DiscFac,0.9964624990493332 +time_to_estimate,566.9156475067139 +params,"{'CRRA': 3.2407994059482403, 'DiscFac': 0.9964624990493332}" +criterion,482.57751767636466 start_criterion,nan -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 -message,Maximum number of function evaluations has been exceeded. +n_free,2 +message,Maximum number of iterations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,194 n_derivative_evaluations, -n_iterations,33 +n_iterations,100 diff --git a/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv index 4e0cd70..42218fa 100644 --- a/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/min/WealthPortfolioSub(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ CRRA,5.0 -time_to_estimate,363.6690049171448 -params,{'CRRA': 5.0} -criterion,872.6935198057116 -start_criterion,7.4693353828103115 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +DiscFac,1.0 +time_to_estimate,708.9287040233612 +params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +criterion,1.357812612728672e+23 +start_criterion,27.654229740170432 +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,33 +n_iterations,50 diff --git a/content/tables/min/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/min/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv index 13af3e3..657b01d 100644 --- a/content/tables/min/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/min/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -1,14 +1,15 @@ CRRA,5.0 -time_to_estimate,363.36753010749817 -params,{'CRRA': 5.0} -criterion,673.7835424026754 -start_criterion,7.534427217351783 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +DiscFac,1.0 +time_to_estimate,705.6201221942902 +params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +criterion,1.3578126127559742e+23 +start_criterion,2.7065370680320946 +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,33 +n_iterations,50 diff --git a/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv index e8d4d0a..71b5b3f 100644 --- a/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/min/WealthPortfolioSub(Stock)Market_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,303.79462242126465 -params,{'CRRA': 5.0} -criterion,1612.169215974723 -start_criterion,7.821382166019953 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +CRRA,10.549999999999999 +DiscFac,0.8 +time_to_estimate,562.5260219573975 +params,"{'CRRA': 10.549999999999999, 'DiscFac': 0.8}" +criterion,1.3578126127721387e+23 +start_criterion,1.9914814501483995 +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,33 +n_iterations,50 diff --git a/content/tables/min/WealthPortfolio_estimate_results.csv b/content/tables/min/WealthPortfolio_estimate_results.csv index 990a27b..813bf4d 100644 --- a/content/tables/min/WealthPortfolio_estimate_results.csv +++ b/content/tables/min/WealthPortfolio_estimate_results.csv @@ -1,14 +1,15 @@ -CRRA,5.0 -time_to_estimate,303.2821366786957 -params,{'CRRA': 5.0} -criterion,1991.864236303542 -start_criterion,8.117351980855833 -start_params,{'CRRA': 5.0} -algorithm,scipy_neldermead +CRRA,10.549999999999999 +DiscFac,0.8 +time_to_estimate,561.0738818645477 +params,"{'CRRA': 10.549999999999999, 'DiscFac': 0.8}" +criterion,1.357812612759542e+23 +start_criterion,9.958770649461421 +start_params,"{'CRRA': 5.0, 'DiscFac': 1.0}" +algorithm,multistart_scipy_neldermead direction,minimize -n_free,1 +n_free,2 message,Maximum number of function evaluations has been exceeded. success,False -n_criterion_evaluations,100 +n_criterion_evaluations,200 n_derivative_evaluations, -n_iterations,33 +n_iterations,50 diff --git a/content/tables/msm/IndShockSub(Labor)Market_estimate_results.csv b/content/tables/msm/IndShockSub(Labor)Market_estimate_results.csv index 0779a00..9c11bab 100644 --- a/content/tables/msm/IndShockSub(Labor)Market_estimate_results.csv +++ b/content/tables/msm/IndShockSub(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,10.120079274781991 -DiscFac,1.072981483284301 -time_to_estimate,259.41752099990845 +CRRA,10.120536495469759 +DiscFac,1.072981697525726 +time_to_estimate,531.695109128952 +_params,"{'CRRA': 10.120536495469759, 'DiscFac': 1.072981697525726}" +_internal_estimates,"InternalParams(values=array([10.1205365, 1.0729817]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.1205365, 1.0729817]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': 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a/content/tables/msm/IndShockSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/msm/IndShockSub(Stock)(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,10.586402570396377 -DiscFac,1.075381873831886 -time_to_estimate,234.07507586479187 +CRRA,10.637291414951322 +DiscFac,1.0751692278351839 +time_to_estimate,516.5506954193115 +_params,"{'CRRA': 10.637291414951322, 'DiscFac': 1.0751692278351839}" +_internal_estimates,"InternalParams(values=array([10.63729141, 1.07516923]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.63729141, 1.07516923]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': 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array(-0.03203695), 'DiscFac': array(16.37311659)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/IndShockSub(Stock)Market_estimate_results.csv b/content/tables/msm/IndShockSub(Stock)Market_estimate_results.csv index 2493c63..7c8248a 100644 --- a/content/tables/msm/IndShockSub(Stock)Market_estimate_results.csv +++ b/content/tables/msm/IndShockSub(Stock)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,1.6899110768594872 -DiscFac,0.9795093574387763 -time_to_estimate,245.7254936695099 +CRRA,1.6899139270900547 +DiscFac,0.9795091538349516 +time_to_estimate,515.8025386333466 +_params,"{'CRRA': 1.6899139270900547, 'DiscFac': 0.9795091538349516}" +_internal_estimates,"InternalParams(values=array([1.68991393, 0.97950915]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([1.68991393, 0.97950915]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(518.82380586), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': 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1.10371975e-04 2.29478720e-04 + -1.74583374e-04 9.18609805e-05 -9.28899552e-04 -1.50543778e-04] + [ 2.66601431e-05 1.99863137e-03 8.85890234e-05 9.17921130e-05 + -9.23337837e-05 -2.83576297e-04 1.48966314e-04 1.80715547e-04 + -3.10963397e-04 3.47758778e-04 5.28959433e-04 5.19875724e-04] + [ 2.79286974e-05 8.85890234e-05 1.92743661e-03 4.84543237e-05 + 8.64878494e-05 -1.21376982e-04 -2.12383559e-04 -3.48906145e-05 + 4.13284621e-04 3.32697621e-04 -4.47332935e-04 5.68592373e-04] + [ 7.46706608e-06 9.17921130e-05 4.84543237e-05 1.69014402e-03 + -1.64277002e-05 -4.40250202e-05 -1.69566236e-04 -7.30430953e-04 + 6.17674185e-05 -3.77973815e-04 -1.25740378e-03 -1.67437857e-03] + [-3.57660861e-05 -9.23337837e-05 8.64878494e-05 -1.64277002e-05 + 3.40044865e-03 2.31358468e-04 -1.98388371e-04 -9.90302591e-04 + 3.40008787e-04 -3.99570271e-04 -8.07415962e-04 -6.44898143e-03] + [ 2.67259515e-05 -2.83576297e-04 -1.21376982e-04 -4.40250202e-05 + 2.31358468e-04 8.59065022e-03 -5.28504370e-04 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7.46109287e-03 -5.50753961e-03 5.26149129e-01 -2.79455237e-02] + [-1.50543778e-04 5.19875724e-04 5.68592373e-04 -1.67437857e-03 + -6.44898143e-03 -3.14657898e-03 7.40307679e-03 -1.42343182e-02 + 1.60977374e-02 1.82934775e-03 -2.79455237e-02 1.79476773e+00]]" +_internal_weights,"[[2.01914916e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.00342392e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.18823806e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 5.91665554e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.94078842e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.16405624e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.98762089e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.64677360e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.67051754e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 7.88396527e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90060184e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.57175161e-01]]" +_internal_jacobian,"[[ 0.5733547 23.30015286] + [ 0.86204412 47.50225817] + [ 1.51241309 88.42767678] + [ 1.83439217 117.05231415] + [ 2.69013815 170.64007089] + [ 2.22069696 142.56818509] + [ 3.24635465 203.45744555] + [ 3.22798763 192.51516047] + [ 2.95270076 168.86416448] + [ 2.56772368 122.84053365] + [ 1.68657561 75.4871039 ] + [ 0.81142169 47.51667733]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, 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{'CRRA': array(2.56772368), 'DiscFac': array(122.84053365)}, '(85,90]': {'CRRA': array(1.68657561), 'DiscFac': array(75.4871039)}, '(90,95]': {'CRRA': array(0.81142169), 'DiscFac': array(47.51667733)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/IndShock_estimate_results.csv b/content/tables/msm/IndShock_estimate_results.csv index 12f859a..9ed5730 100644 --- a/content/tables/msm/IndShock_estimate_results.csv +++ b/content/tables/msm/IndShock_estimate_results.csv @@ -1,3 +1,97 @@ CRRA,1.7276875282656314 -DiscFac,0.9707284652948671 -time_to_estimate,240.64905190467834 +DiscFac,0.9707284652948673 +time_to_estimate,521.7721037864685 +_params,"{'CRRA': 1.7276875282656314, 'DiscFac': 0.9707284652948673}" +_internal_estimates,"InternalParams(values=array([1.72768753, 0.97072847]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([1.72768753, 0.97072847]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(518.82380586), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), 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array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(69.87620894), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(70,75]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(16.46773596), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(75,80]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(16.70517538), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(80,85]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(7.88396527), '(85,90]': array(0.), '(90,95]': array(0.)}, '(85,90]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(1.90060184), '(90,95]': array(0.)}, '(90,95]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.55717516)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7f311b630c10>, params_from_internal=._params_from_internal at 0x7f311b632cb0>, derivative_to_internal=._derivative_to_internal at 0x7f311b631cf0>, func_to_internal=._func_to_internal at 0x7f311b6312d0>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.95258112e-04 2.66601431e-05 2.79286974e-05 7.46706608e-06 + -3.57660861e-05 2.67259515e-05 1.10371975e-04 2.29478720e-04 + -1.74583374e-04 9.18609805e-05 -9.28899552e-04 -1.50543778e-04] + [ 2.66601431e-05 1.99863137e-03 8.85890234e-05 9.17921130e-05 + -9.23337837e-05 -2.83576297e-04 1.48966314e-04 1.80715547e-04 + -3.10963397e-04 3.47758778e-04 5.28959433e-04 5.19875724e-04] + [ 2.79286974e-05 8.85890234e-05 1.92743661e-03 4.84543237e-05 + 8.64878494e-05 -1.21376982e-04 -2.12383559e-04 -3.48906145e-05 + 4.13284621e-04 3.32697621e-04 -4.47332935e-04 5.68592373e-04] + [ 7.46706608e-06 9.17921130e-05 4.84543237e-05 1.69014402e-03 + -1.64277002e-05 -4.40250202e-05 -1.69566236e-04 -7.30430953e-04 + 6.17674185e-05 -3.77973815e-04 -1.25740378e-03 -1.67437857e-03] + [-3.57660861e-05 -9.23337837e-05 8.64878494e-05 -1.64277002e-05 + 3.40044865e-03 2.31358468e-04 -1.98388371e-04 -9.90302591e-04 + 3.40008787e-04 -3.99570271e-04 -8.07415962e-04 -6.44898143e-03] + [ 2.67259515e-05 -2.83576297e-04 -1.21376982e-04 -4.40250202e-05 + 2.31358468e-04 8.59065022e-03 -5.28504370e-04 9.67901647e-04 + -6.38169315e-04 -4.22552263e-04 3.24001952e-03 -3.14657898e-03] + [ 1.10371975e-04 1.48966314e-04 -2.12383559e-04 -1.69566236e-04 + -1.98388371e-04 -5.28504370e-04 1.43110225e-02 2.57065708e-04 + -1.09391854e-03 1.90605756e-03 -6.99390334e-04 7.40307679e-03] + [ 2.29478720e-04 1.80715547e-04 -3.48906145e-05 -7.30430953e-04 + -9.90302591e-04 9.67901647e-04 2.57065708e-04 6.07248017e-02 + 2.56350149e-03 6.21389768e-04 -2.22168627e-03 -1.42343182e-02] + [-1.74583374e-04 -3.10963397e-04 4.13284621e-04 6.17674185e-05 + 3.40008787e-04 -6.38169315e-04 -1.09391854e-03 2.56350149e-03 + 5.98616882e-02 -9.63153128e-04 7.46109287e-03 1.60977374e-02] + [ 9.18609805e-05 3.47758778e-04 3.32697621e-04 -3.77973815e-04 + -3.99570271e-04 -4.22552263e-04 1.90605756e-03 6.21389768e-04 + -9.63153128e-04 1.26839727e-01 -5.50753961e-03 1.82934775e-03] + [-9.28899552e-04 5.28959433e-04 -4.47332935e-04 -1.25740378e-03 + -8.07415962e-04 3.24001952e-03 -6.99390334e-04 -2.22168627e-03 + 7.46109287e-03 -5.50753961e-03 5.26149129e-01 -2.79455237e-02] + [-1.50543778e-04 5.19875724e-04 5.68592373e-04 -1.67437857e-03 + -6.44898143e-03 -3.14657898e-03 7.40307679e-03 -1.42343182e-02 + 1.60977374e-02 1.82934775e-03 -2.79455237e-02 1.79476773e+00]]" +_internal_weights,"[[2.01914916e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.00342392e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.18823806e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 5.91665554e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.94078842e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.16405624e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.98762089e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.64677360e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.67051754e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 7.88396527e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90060184e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.57175161e-01]]" +_internal_jacobian,"[[ 0.25091248 12.18909018] + [ 0.75488419 42.79812654] + [ 0.96405127 62.05600204] + [ 1.3439964 92.13445197] + [ 2.02520225 142.30890094] + [ 2.439634 172.36113605] + [ 3.41365907 230.75908878] + [ 3.19943685 203.86511346] + [ 1.08118883 74.62108544] + [ 1.99009032 110.88288514] + [ 0.95896302 57.33195063] + [ 0.85359993 51.8164894 ]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.25091248), 'DiscFac': array(12.18909018)}, '(30,35]': {'CRRA': array(0.75488419), 'DiscFac': array(42.79812654)}, '(35,40]': {'CRRA': array(0.96405127), 'DiscFac': array(62.05600204)}, '(40,45]': {'CRRA': array(1.3439964), 'DiscFac': array(92.13445197)}, '(45,50]': {'CRRA': array(2.02520225), 'DiscFac': array(142.30890094)}, '(50,55]': {'CRRA': array(2.439634), 'DiscFac': array(172.36113605)}, '(55,60]': {'CRRA': array(3.41365907), 'DiscFac': array(230.75908878)}, '(70,75]': {'CRRA': array(3.19943685), 'DiscFac': array(203.86511346)}, '(75,80]': {'CRRA': array(1.08118883), 'DiscFac': array(74.62108544)}, '(80,85]': {'CRRA': array(1.99009032), 'DiscFac': array(110.88288514)}, '(85,90]': {'CRRA': array(0.95896302), 'DiscFac': array(57.33195063)}, '(90,95]': {'CRRA': array(0.85359993), 'DiscFac': array(51.8164894)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/PortfolioSub(Labor)Market_estimate_results.csv b/content/tables/msm/PortfolioSub(Labor)Market_estimate_results.csv index e53149d..ba56efd 100644 --- a/content/tables/msm/PortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/msm/PortfolioSub(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,16.12347532696331 -DiscFac,1.00990962959361 -time_to_estimate,395.8874897956848 +CRRA,16.035365501911656 +DiscFac,1.0173759669425746 +time_to_estimate,719.5181894302368 +_params,"{'CRRA': 16.035365501911656, 'DiscFac': 1.0173759669425746}" +_internal_estimates,"InternalParams(values=array([16.0353655 , 1.01737597]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([16.0353655 , 1.01737597]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(605.53346513), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(45,50]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(279.92614085), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(50,55]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(130.94785271), '(55,60]': array(0.), '(70,75]': array(0.), 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derivative_to_internal=._derivative_to_internal at 0x7fe67e87add0>, func_to_internal=._func_to_internal at 0x7fe67e87b400>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 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4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 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array(22130200.88671936)}, '(45,50]': {'CRRA': array(1410590.19535983), 'DiscFac': array(56529569.89179237)}, '(50,55]': {'CRRA': array(2645170.35358017), 'DiscFac': array(75786801.04619929)}, '(55,60]': {'CRRA': array(5394943.80771548), 'DiscFac': array(1.07998281e+08)}, '(70,75]': {'CRRA': array(5736878.11204669), 'DiscFac': array(73912514.35359986)}, '(75,80]': {'CRRA': array(4259250.72813986), 'DiscFac': array(68056291.28537746)}, '(80,85]': {'CRRA': array(2539210.40317956), 'DiscFac': array(75344142.78431329)}, '(85,90]': {'CRRA': array(358479.52323137), 'DiscFac': array(53066479.86708646)}, '(90,95]': {'CRRA': array(823300.55903608), 'DiscFac': array(10886822.51310813)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/PortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/msm/PortfolioSub(Stock)(Labor)Market_estimate_results.csv index 8c582b5..55b19d7 100644 --- a/content/tables/msm/PortfolioSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/msm/PortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,10.443929906985552 -DiscFac,1.0472124784409744 -time_to_estimate,280.2252173423767 +CRRA,10.415127453154128 +DiscFac,1.0468359228606232 +time_to_estimate,581.8424572944641 +_params,"{'CRRA': 10.415127453154128, 'DiscFac': 1.0468359228606232}" +_internal_estimates,"InternalParams(values=array([10.41512745, 1.04683592]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.41512745, 1.04683592]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), 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array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.56618589)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7f9853e03370>, params_from_internal=._params_from_internal at 0x7f9853e03250>, derivative_to_internal=._derivative_to_internal at 0x7f9853e03760>, func_to_internal=._func_to_internal at 0x7f98462eacb0>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 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15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.0245508), 'DiscFac': array(0.91917325)}, '(30,35]': {'CRRA': array(0.02528639), 'DiscFac': array(4.55307829)}, '(35,40]': {'CRRA': array(-0.01582544), 'DiscFac': array(14.07700567)}, '(40,45]': {'CRRA': array(-0.02527712), 'DiscFac': array(22.50566702)}, '(45,50]': {'CRRA': array(-0.06771321), 'DiscFac': array(27.94719174)}, '(50,55]': {'CRRA': array(-0.08785909), 'DiscFac': array(40.48453928)}, '(55,60]': {'CRRA': array(-0.15779242), 'DiscFac': array(51.39618903)}, '(70,75]': {'CRRA': array(-0.06865905), 'DiscFac': array(63.62055351)}, '(75,80]': {'CRRA': array(-0.02501022), 'DiscFac': array(62.28277428)}, '(80,85]': {'CRRA': array(-0.01895297), 'DiscFac': array(62.73390727)}, '(85,90]': {'CRRA': array(-0.13706825), 'DiscFac': array(41.98395098)}, '(90,95]': {'CRRA': array(-0.13312547), 'DiscFac': array(26.73692707)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/PortfolioSub(Stock)Market_estimate_results.csv b/content/tables/msm/PortfolioSub(Stock)Market_estimate_results.csv index b4b51da..5525f0d 100644 --- a/content/tables/msm/PortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/msm/PortfolioSub(Stock)Market_estimate_results.csv @@ -1,3 +1,97 @@ CRRA,2.0278733469356074 DiscFac,0.947507160328444 -time_to_estimate,229.49117970466614 +time_to_estimate,485.0004813671112 +_params,"{'CRRA': 2.0278733469356074, 'DiscFac': 0.947507160328444}" +_internal_estimates,"InternalParams(values=array([2.02787335, 0.94750716]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([2.02787335, 0.94750716]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), 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1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 -1.26483119e-03 2.21734482e-03] + [-1.59011871e-04 -3.48865507e-04 1.21619646e-04 -2.00215176e-06 + -6.06222612e-04 -6.91727595e-04 4.49609135e-04 6.10812312e-02 + -7.03144448e-04 9.30021499e-04 1.02943264e-02 1.34912486e-02] + [ 6.46025432e-05 -4.45091936e-04 2.42370531e-04 -8.96273332e-05 + -8.16268084e-04 -8.41433602e-04 3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 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+ [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[1.58426391e-01 9.30633336e+00] + [4.79799720e-01 3.19459570e+01] + [9.83545418e-01 7.15172001e+01] + [9.28554113e-01 6.92450715e+01] + [1.69530355e+00 1.07555921e+02] + [2.24775452e+00 1.54922589e+02] + [1.85099046e+00 1.61364165e+02] + [1.67126975e+00 1.16881362e+02] + [1.53980004e+00 9.38780251e+01] + [8.53797174e-01 7.65865839e+01] + [8.89426878e-01 6.89719139e+01] + [0.00000000e+00 0.00000000e+00]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.15842639), 'DiscFac': array(9.30633336)}, '(30,35]': {'CRRA': array(0.47979972), 'DiscFac': array(31.94595695)}, '(35,40]': {'CRRA': array(0.98354542), 'DiscFac': array(71.5172001)}, '(40,45]': {'CRRA': array(0.92855411), 'DiscFac': array(69.24507154)}, '(45,50]': {'CRRA': array(1.69530355), 'DiscFac': array(107.55592114)}, '(50,55]': {'CRRA': array(2.24775452), 'DiscFac': array(154.92258895)}, '(55,60]': {'CRRA': array(1.85099046), 'DiscFac': array(161.36416494)}, '(70,75]': {'CRRA': array(1.67126975), 'DiscFac': array(116.88136164)}, '(75,80]': {'CRRA': array(1.53980004), 'DiscFac': array(93.87802506)}, '(80,85]': {'CRRA': array(0.85379717), 'DiscFac': array(76.58658385)}, '(85,90]': {'CRRA': array(0.88942688), 'DiscFac': array(68.97191388)}, '(90,95]': {'CRRA': array(0.), 'DiscFac': array(0.)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/Portfolio_estimate_results.csv b/content/tables/msm/Portfolio_estimate_results.csv index 5a69bad..6b90a19 100644 --- a/content/tables/msm/Portfolio_estimate_results.csv +++ b/content/tables/msm/Portfolio_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,7.887497180692697 -DiscFac,0.7363646100858712 -time_to_estimate,284.09565591812134 +CRRA,7.83791147453966 +DiscFac,0.7386432774278726 +time_to_estimate,583.6027400493622 +_params,"{'CRRA': 7.83791147453966, 'DiscFac': 0.7386432774278726}" +_internal_estimates,"InternalParams(values=array([7.83791147, 0.73864328]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([7.83791147, 0.73864328]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), 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6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 -1.21385564e-03 -1.39621761e-03] + [ 3.51434321e-05 3.24097529e-05 -7.20625366e-05 -1.19096295e-04 + 1.96792266e-04 7.63662771e-03 1.87644341e-04 -6.91727595e-04 + -8.41433602e-04 7.97137838e-04 -3.67034802e-03 7.47381063e-05] + [ 4.95550858e-05 -9.09560608e-06 1.23099100e-04 5.24085619e-05 + 1.02791978e-04 1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 -1.26483119e-03 2.21734482e-03] + [-1.59011871e-04 -3.48865507e-04 1.21619646e-04 -2.00215176e-06 + -6.06222612e-04 -6.91727595e-04 4.49609135e-04 6.10812312e-02 + -7.03144448e-04 9.30021499e-04 1.02943264e-02 1.34912486e-02] + [ 6.46025432e-05 -4.45091936e-04 2.42370531e-04 -8.96273332e-05 + -8.16268084e-04 -8.41433602e-04 3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[-2.34705020e+05 -1.00528703e+05] + [ 2.44463627e+05 -5.55678286e+06] + [ 2.41929927e+06 1.12304799e+07] + [ 2.34569133e+06 3.01456612e+07] + [ 2.65563058e+06 6.97066525e+07] + [ 5.09401864e+06 8.36824455e+07] + [ 9.60353716e+06 1.12967147e+08] + [ 3.09730899e+06 1.24552119e+07] + [ 1.30019256e+06 1.34424316e+07] + [ 6.40281296e+05 3.09376942e+07] + [ 8.82875621e+05 3.69373103e+07] + [ 1.48141627e+06 1.61413417e+07]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, 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'DiscFac': array(12455211.88175488)}, '(75,80]': {'CRRA': array(1300192.55714479), 'DiscFac': array(13442431.61321701)}, '(80,85]': {'CRRA': array(640281.29624133), 'DiscFac': array(30937694.18392828)}, '(85,90]': {'CRRA': array(882875.62144124), 'DiscFac': array(36937310.30523588)}, '(90,95]': {'CRRA': array(1481416.26824558), 'DiscFac': array(16141341.69088265)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/msm/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv index 88b0882..a8039a0 100644 --- a/content/tables/msm/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/msm/WarmGlowPortfolioSub(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,15.8080273511146 -DiscFac,1.016098474811927 -time_to_estimate,390.0300633907318 +CRRA,16.108511883223933 +DiscFac,1.0084067716800273 +time_to_estimate,715.5001871585846 +_params,"{'CRRA': 16.108511883223933, 'DiscFac': 1.0084067716800273}" +_internal_estimates,"InternalParams(values=array([16.10851188, 1.00840677]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([16.10851188, 1.00840677]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': 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params_from_internal=._params_from_internal at 0x7f68b65370a0>, derivative_to_internal=._derivative_to_internal at 0x7f68b6537370>, func_to_internal=._func_to_internal at 0x7f68b6536320>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 -1.21385564e-03 -1.39621761e-03] + [ 3.51434321e-05 3.24097529e-05 -7.20625366e-05 -1.19096295e-04 + 1.96792266e-04 7.63662771e-03 1.87644341e-04 -6.91727595e-04 + -8.41433602e-04 7.97137838e-04 -3.67034802e-03 7.47381063e-05] + [ 4.95550858e-05 -9.09560608e-06 1.23099100e-04 5.24085619e-05 + 1.02791978e-04 1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 -1.26483119e-03 2.21734482e-03] + [-1.59011871e-04 -3.48865507e-04 1.21619646e-04 -2.00215176e-06 + -6.06222612e-04 -6.91727595e-04 4.49609135e-04 6.10812312e-02 + -7.03144448e-04 9.30021499e-04 1.02943264e-02 1.34912486e-02] + [ 6.46025432e-05 -4.45091936e-04 2.42370531e-04 -8.96273332e-05 + -8.16268084e-04 -8.41433602e-04 3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[-6.66945202e+04 -5.65512534e+05] + [ 6.03851628e+04 -3.47902728e+05] + [ 6.70099918e+05 7.03193003e+06] + [ 9.90796713e+05 2.13007843e+07] + [ 1.32534381e+06 5.49819123e+07] + [ 2.52322927e+06 7.36830733e+07] + [ 5.17062111e+06 1.04985588e+08] + [ 5.42306783e+06 7.05251398e+07] + [ 3.97954327e+06 6.46908411e+07] + [ 2.34747254e+06 7.21476499e+07] + [ 3.61285478e+05 5.17026380e+07] + [ 7.73627749e+05 9.96169823e+06]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(-66694.52024247), 'DiscFac': array(-565512.5335048)}, '(30,35]': {'CRRA': array(60385.16275463), 'DiscFac': array(-347902.72760634)}, '(35,40]': {'CRRA': array(670099.91772833), 'DiscFac': array(7031930.02942947)}, '(40,45]': {'CRRA': array(990796.71302322), 'DiscFac': array(21300784.27947865)}, '(45,50]': {'CRRA': array(1325343.81337981), 'DiscFac': array(54981912.31926441)}, '(50,55]': {'CRRA': array(2523229.267605), 'DiscFac': array(73683073.26230405)}, '(55,60]': {'CRRA': array(5170621.11464229), 'DiscFac': array(1.04985588e+08)}, '(70,75]': {'CRRA': array(5423067.83271693), 'DiscFac': array(70525139.75333104)}, '(75,80]': {'CRRA': array(3979543.27149752), 'DiscFac': array(64690841.07529136)}, '(80,85]': {'CRRA': array(2347472.53970107), 'DiscFac': array(72147649.90878029)}, '(85,90]': {'CRRA': array(361285.47766543), 'DiscFac': array(51702637.98911797)}, '(90,95]': {'CRRA': array(773627.74936255), 'DiscFac': array(9961698.22756919)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/msm/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv index 3c527d4..4a7c91c 100644 --- a/content/tables/msm/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/msm/WarmGlowPortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,10.483034325648617 -DiscFac,1.0468898907855713 -time_to_estimate,274.92318987846375 +CRRA,10.469331918692177 +DiscFac,1.046944960051022 +time_to_estimate,585.425628900528 +_params,"{'CRRA': 10.469331918692177, 'DiscFac': 1.046944960051022}" +_internal_estimates,"InternalParams(values=array([10.46933192, 1.04694496]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.46933192, 1.04694496]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), 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array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(16.37164118), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(75,80]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(16.68863935), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(80,85]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(8.15237029), '(85,90]': array(0.), '(90,95]': array(0.)}, '(85,90]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(1.90282463), '(90,95]': array(0.)}, '(90,95]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.56618589)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7f3b77f90550>, params_from_internal=._params_from_internal at 0x7f3b77f91000>, derivative_to_internal=._derivative_to_internal at 0x7f3b77f930a0>, func_to_internal=._func_to_internal at 0x7f3b77f93010>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 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array(-0.10374346), 'DiscFac': array(25.17865248)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/msm/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv index 3b84f2e..3bf08da 100644 --- a/content/tables/msm/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/msm/WarmGlowPortfolioSub(Stock)Market_estimate_results.csv @@ -1,3 +1,97 @@ CRRA,2.0278733469356074 DiscFac,0.947507160328444 -time_to_estimate,228.53914856910706 +time_to_estimate,482.8548994064331 +_params,"{'CRRA': 2.0278733469356074, 'DiscFac': 0.947507160328444}" +_internal_estimates,"InternalParams(values=array([2.02787335, 0.94750716]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([2.02787335, 0.94750716]), free_mask=array([ True, True]), 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0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.15842639), 'DiscFac': array(9.30633336)}, '(30,35]': {'CRRA': array(0.47979972), 'DiscFac': array(31.94595695)}, '(35,40]': {'CRRA': array(0.98354542), 'DiscFac': array(71.5172001)}, '(40,45]': {'CRRA': array(0.92855411), 'DiscFac': array(69.24507154)}, '(45,50]': {'CRRA': array(1.69530355), 'DiscFac': array(107.55592114)}, '(50,55]': {'CRRA': array(2.24775452), 'DiscFac': array(154.92258895)}, '(55,60]': {'CRRA': array(1.85099046), 'DiscFac': array(161.36416494)}, '(70,75]': {'CRRA': array(1.67126975), 'DiscFac': array(116.88136164)}, '(75,80]': {'CRRA': array(1.53980004), 'DiscFac': array(93.87802506)}, '(80,85]': {'CRRA': array(0.85379717), 'DiscFac': array(76.58658385)}, '(85,90]': {'CRRA': array(0.88942688), 'DiscFac': array(68.97191388)}, '(90,95]': {'CRRA': array(0.), 'DiscFac': array(0.)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowPortfolio_estimate_results.csv b/content/tables/msm/WarmGlowPortfolio_estimate_results.csv index 86c0777..a6e337c 100644 --- a/content/tables/msm/WarmGlowPortfolio_estimate_results.csv +++ b/content/tables/msm/WarmGlowPortfolio_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,8.08488016272895 -DiscFac,0.7294939419822766 -time_to_estimate,277.5549011230469 +CRRA,8.0340535845575 +DiscFac,0.7317513481626051 +time_to_estimate,586.0827894210815 +_params,"{'CRRA': 8.0340535845575, 'DiscFac': 0.7317513481626051}" +_internal_estimates,"InternalParams(values=array([8.03405358, 0.73175135]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([8.03405358, 0.73175135]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': 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+_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 -1.21385564e-03 -1.39621761e-03] + [ 3.51434321e-05 3.24097529e-05 -7.20625366e-05 -1.19096295e-04 + 1.96792266e-04 7.63662771e-03 1.87644341e-04 -6.91727595e-04 + -8.41433602e-04 7.97137838e-04 -3.67034802e-03 7.47381063e-05] + [ 4.95550858e-05 -9.09560608e-06 1.23099100e-04 5.24085619e-05 + 1.02791978e-04 1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 -1.26483119e-03 2.21734482e-03] + [-1.59011871e-04 -3.48865507e-04 1.21619646e-04 -2.00215176e-06 + -6.06222612e-04 -6.91727595e-04 4.49609135e-04 6.10812312e-02 + -7.03144448e-04 9.30021499e-04 1.02943264e-02 1.34912486e-02] + [ 6.46025432e-05 -4.45091936e-04 2.42370531e-04 -8.96273332e-05 + -8.16268084e-04 -8.41433602e-04 3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[-2.24861441e+05 -9.19876626e+04] + [ 2.29511065e+05 -5.51183873e+06] + [ 2.29414984e+06 1.10058949e+07] + [ 2.21899778e+06 2.95875343e+07] + [ 2.50110785e+06 6.83594978e+07] + [ 4.81342966e+06 8.18434528e+07] + [ 9.09069022e+06 1.10398653e+08] + [ 2.90636497e+06 1.18896647e+07] + [ 1.21547935e+06 1.28005273e+07] + [ 6.07281973e+05 2.99218101e+07] + [ 8.44129542e+05 3.57320413e+07] + [ 1.41162350e+06 1.57444923e+07]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(-224861.44085805), 'DiscFac': array(-91987.66264161)}, '(30,35]': {'CRRA': array(229511.06510046), 'DiscFac': array(-5511838.72570578)}, '(35,40]': {'CRRA': array(2294149.84114426), 'DiscFac': array(11005894.92512332)}, '(40,45]': {'CRRA': array(2218997.78277701), 'DiscFac': array(29587534.34875737)}, '(45,50]': {'CRRA': array(2501107.84565421), 'DiscFac': array(68359497.84423001)}, '(50,55]': {'CRRA': array(4813429.65804379), 'DiscFac': array(81843452.7696178)}, '(55,60]': {'CRRA': array(9090690.21960198), 'DiscFac': array(1.10398653e+08)}, '(70,75]': {'CRRA': array(2906364.96737909), 'DiscFac': array(11889664.73844956)}, '(75,80]': {'CRRA': array(1215479.35301099), 'DiscFac': array(12800527.31245555)}, '(80,85]': {'CRRA': array(607281.97284519), 'DiscFac': array(29921810.06221741)}, '(85,90]': {'CRRA': array(844129.54174849), 'DiscFac': array(35732041.34837789)}, '(90,95]': {'CRRA': array(1411623.49829079), 'DiscFac': array(15744492.29182508)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowSub(Labor)Market_estimate_results.csv b/content/tables/msm/WarmGlowSub(Labor)Market_estimate_results.csv index 06d6db9..da9d748 100644 --- a/content/tables/msm/WarmGlowSub(Labor)Market_estimate_results.csv +++ b/content/tables/msm/WarmGlowSub(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,4.351904055162223 -DiscFac,1.0300700325903462 -time_to_estimate,344.99290680885315 +CRRA,4.35190478146457 +DiscFac,1.0300700571491297 +time_to_estimate,680.600419998169 +_params,"{'CRRA': 4.35190478146457, 'DiscFac': 1.0300700571491297}" +_internal_estimates,"InternalParams(values=array([4.35190478, 1.03007006]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([4.35190478, 1.03007006]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), 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0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.18823806e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 5.91665554e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.94078842e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.16405624e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.98762089e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.64677360e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.67051754e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 7.88396527e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90060184e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.57175161e-01]]" +_internal_jacobian,"[[-2.05889979e-02 5.46821737e+00] + [-1.48984593e-01 1.73095762e+01] + [-3.32137256e-01 3.77074936e+01] + [-4.10694675e-01 4.64643477e+01] + [-6.14472208e-01 7.02622568e+01] + [-7.17288463e-01 8.69613333e+01] + [-9.54007210e-01 1.20685290e+02] + [-9.20646677e-01 1.57186283e+02] + [-7.81608685e-01 1.63331193e+02] + [-5.39639473e-01 1.18362110e+02] + [-2.64166642e-01 8.14630720e+01] + [-4.53193824e-01 5.12915092e+01]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(-0.020589), 'DiscFac': array(5.46821737)}, '(30,35]': {'CRRA': array(-0.14898459), 'DiscFac': array(17.30957622)}, '(35,40]': {'CRRA': array(-0.33213726), 'DiscFac': array(37.70749355)}, '(40,45]': {'CRRA': array(-0.41069467), 'DiscFac': array(46.46434766)}, '(45,50]': {'CRRA': array(-0.61447221), 'DiscFac': array(70.26225682)}, '(50,55]': {'CRRA': array(-0.71728846), 'DiscFac': array(86.96133331)}, '(55,60]': {'CRRA': array(-0.95400721), 'DiscFac': array(120.68529036)}, '(70,75]': {'CRRA': array(-0.92064668), 'DiscFac': array(157.18628335)}, '(75,80]': {'CRRA': array(-0.78160869), 'DiscFac': array(163.33119252)}, '(80,85]': {'CRRA': array(-0.53963947), 'DiscFac': array(118.36211026)}, '(85,90]': {'CRRA': array(-0.26416664), 'DiscFac': array(81.46307204)}, '(90,95]': {'CRRA': array(-0.45319382), 'DiscFac': array(51.29150922)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/msm/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv index 95d92b0..83850d9 100644 --- a/content/tables/msm/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/msm/WarmGlowSub(Stock)(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,5.377015778911543 -DiscFac,1.0436200866981737 -time_to_estimate,359.6061761379242 +CRRA,5.377015810961057 +DiscFac,1.0436200866981735 +time_to_estimate,703.9654734134674 +_params,"{'CRRA': 5.377015810961057, 'DiscFac': 1.0436200866981735}" +_internal_estimates,"InternalParams(values=array([5.37701581, 1.04362009]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([5.37701581, 1.04362009]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), 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5.19875724e-04] + [ 2.79286974e-05 8.85890234e-05 1.92743661e-03 4.84543237e-05 + 8.64878494e-05 -1.21376982e-04 -2.12383559e-04 -3.48906145e-05 + 4.13284621e-04 3.32697621e-04 -4.47332935e-04 5.68592373e-04] + [ 7.46706608e-06 9.17921130e-05 4.84543237e-05 1.69014402e-03 + -1.64277002e-05 -4.40250202e-05 -1.69566236e-04 -7.30430953e-04 + 6.17674185e-05 -3.77973815e-04 -1.25740378e-03 -1.67437857e-03] + [-3.57660861e-05 -9.23337837e-05 8.64878494e-05 -1.64277002e-05 + 3.40044865e-03 2.31358468e-04 -1.98388371e-04 -9.90302591e-04 + 3.40008787e-04 -3.99570271e-04 -8.07415962e-04 -6.44898143e-03] + [ 2.67259515e-05 -2.83576297e-04 -1.21376982e-04 -4.40250202e-05 + 2.31358468e-04 8.59065022e-03 -5.28504370e-04 9.67901647e-04 + -6.38169315e-04 -4.22552263e-04 3.24001952e-03 -3.14657898e-03] + [ 1.10371975e-04 1.48966314e-04 -2.12383559e-04 -1.69566236e-04 + -1.98388371e-04 -5.28504370e-04 1.43110225e-02 2.57065708e-04 + -1.09391854e-03 1.90605756e-03 -6.99390334e-04 7.40307679e-03] + [ 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+_internal_weights,"[[2.01914916e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.00342392e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.18823806e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 5.91665554e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.94078842e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 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array(-0.21844516), 'DiscFac': array(31.540465)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlowSub(Stock)Market_estimate_results.csv b/content/tables/msm/WarmGlowSub(Stock)Market_estimate_results.csv index dfd047e..b3fb863 100644 --- a/content/tables/msm/WarmGlowSub(Stock)Market_estimate_results.csv +++ b/content/tables/msm/WarmGlowSub(Stock)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,1.6065655986698695 +CRRA,1.6065655986698697 DiscFac,0.9790270061791481 -time_to_estimate,233.5930769443512 +time_to_estimate,472.92487931251526 +_params,"{'CRRA': 1.6065655986698697, 'DiscFac': 0.9790270061791481}" +_internal_estimates,"InternalParams(values=array([1.6065656 , 0.97902701]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([1.6065656 , 0.97902701]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(518.82380586), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), 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array(84.94395126)}, '(80,85]': {'CRRA': array(1.78923564), 'DiscFac': array(99.33538543)}, '(85,90]': {'CRRA': array(0.97924059), 'DiscFac': array(51.09853381)}, '(90,95]': {'CRRA': array(1.00519672), 'DiscFac': array(60.38450023)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WarmGlow_estimate_results.csv b/content/tables/msm/WarmGlow_estimate_results.csv index a1e2e0b..df445a7 100644 --- a/content/tables/msm/WarmGlow_estimate_results.csv +++ b/content/tables/msm/WarmGlow_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,1.6965301440096874 -DiscFac,0.9694670056908945 -time_to_estimate,226.57716917991638 +CRRA,1.7007109953786266 +DiscFac,0.9696762813125013 +time_to_estimate,519.7153921127319 +_params,"{'CRRA': 1.7007109953786266, 'DiscFac': 0.9696762813125013}" +_internal_estimates,"InternalParams(values=array([1.700711 , 0.96967628]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([1.700711 , 0.96967628]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2019.14915854), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(500.34239184), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(518.82380586), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), 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+ [1.01635111e+00 5.88717801e+01] + [1.61912191e+00 8.49557372e+01] + [1.15628495e+00 7.67281456e+01]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.18679331), 'DiscFac': array(9.09662924)}, '(30,35]': {'CRRA': array(0.65408587), 'DiscFac': array(37.28341216)}, '(35,40]': {'CRRA': array(0.7337333), 'DiscFac': array(52.40300867)}, '(40,45]': {'CRRA': array(1.55055969), 'DiscFac': array(106.67163877)}, '(45,50]': {'CRRA': array(1.91030507), 'DiscFac': array(138.8923129)}, '(50,55]': {'CRRA': array(2.11666937), 'DiscFac': array(151.93791981)}, '(55,60]': {'CRRA': array(2.79916155), 'DiscFac': array(190.91553947)}, '(70,75]': {'CRRA': array(3.0759432), 'DiscFac': array(211.25909383)}, '(75,80]': {'CRRA': array(1.62745814), 'DiscFac': array(106.07835094)}, '(80,85]': {'CRRA': array(1.01635111), 'DiscFac': array(58.87178007)}, '(85,90]': {'CRRA': array(1.61912191), 'DiscFac': array(84.95573723)}, '(90,95]': {'CRRA': array(1.15628495), 'DiscFac': array(76.72814562)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WealthPortfolioSub(Labor)Market_estimate_results.csv b/content/tables/msm/WealthPortfolioSub(Labor)Market_estimate_results.csv index 8719159..db4ccf6 100644 --- a/content/tables/msm/WealthPortfolioSub(Labor)Market_estimate_results.csv +++ b/content/tables/msm/WealthPortfolioSub(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,7.957913916963904 -DiscFac,0.993877086590123 -time_to_estimate,302.8197329044342 +CRRA,15.796329213228365 +DiscFac,1.01881362645317 +time_to_estimate,720.8615710735321 +_params,"{'CRRA': 15.796329213228365, 'DiscFac': 1.01881362645317}" +_internal_estimates,"InternalParams(values=array([15.79632921, 1.01881363]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([15.79632921, 1.01881363]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(605.53346513), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(45,50]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(279.92614085), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(50,55]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), 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params_from_internal=._params_from_internal at 0x7fcf32d41480>, derivative_to_internal=._derivative_to_internal at 0x7fcf32d41090>, func_to_internal=._func_to_internal at 0x7fcf32d405e0>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 -1.21385564e-03 -1.39621761e-03] + [ 3.51434321e-05 3.24097529e-05 -7.20625366e-05 -1.19096295e-04 + 1.96792266e-04 7.63662771e-03 1.87644341e-04 -6.91727595e-04 + -8.41433602e-04 7.97137838e-04 -3.67034802e-03 7.47381063e-05] + [ 4.95550858e-05 -9.09560608e-06 1.23099100e-04 5.24085619e-05 + 1.02791978e-04 1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 -1.26483119e-03 2.21734482e-03] + [-1.59011871e-04 -3.48865507e-04 1.21619646e-04 -2.00215176e-06 + -6.06222612e-04 -6.91727595e-04 4.49609135e-04 6.10812312e-02 + -7.03144448e-04 9.30021499e-04 1.02943264e-02 1.34912486e-02] + [ 6.46025432e-05 -4.45091936e-04 2.42370531e-04 -8.96273332e-05 + -8.16268084e-04 -8.41433602e-04 3.74504692e-04 -7.03144448e-04 + 5.99210025e-02 1.09220053e-03 -1.79592505e-03 -1.15555069e-02] + [-7.75378886e-06 -9.40023292e-04 -4.53895101e-04 4.13045513e-05 + 1.18356570e-03 7.97137838e-04 -4.32072461e-04 9.30021499e-04 + 1.09220053e-03 1.22663712e-01 -1.08608497e-02 1.60128201e-02] + [-9.90751500e-04 4.15494682e-04 4.77856997e-04 3.57275514e-04 + -1.21385564e-03 -3.67034802e-03 -1.26483119e-03 1.02943264e-02 + -1.79592505e-03 -1.08608497e-02 5.25534506e-01 -2.55742896e-02] + [ 1.05939616e-03 -4.44444208e-03 -2.15951955e-03 -2.29200628e-05 + -1.39621761e-03 7.47381063e-05 2.21734482e-03 1.34912486e-02 + -1.15555069e-02 1.60128201e-02 -2.55742896e-02 1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 1.90282463e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.66185892e-01]]" +_internal_jacobian,"[[-6.87525158e+04 -5.74393611e+05] + [ 6.52482168e+04 -3.32957696e+05] + [ 7.37672823e+05 7.57380924e+06] + [ 1.09869909e+06 2.26145746e+07] + [ 1.46705120e+06 5.76436287e+07] + [ 2.74639787e+06 7.73200724e+07] + [ 5.59448326e+06 1.10187439e+08] + [ 5.96323375e+06 7.56565489e+07] + [ 4.43512321e+06 6.97784356e+07] + [ 2.65389276e+06 7.71788123e+07] + [ 3.66575867e+05 5.42830376e+07] + [ 8.52088408e+05 1.12601560e+07]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(-68752.51579378), 'DiscFac': array(-574393.6108013)}, '(30,35]': {'CRRA': array(65248.21682618), 'DiscFac': array(-332957.69632734)}, '(35,40]': {'CRRA': array(737672.82315978), 'DiscFac': array(7573809.23941928)}, '(40,45]': {'CRRA': array(1098699.08952011), 'DiscFac': array(22614574.62081938)}, '(45,50]': {'CRRA': array(1467051.20099021), 'DiscFac': array(57643628.72897539)}, '(50,55]': {'CRRA': array(2746397.87222362), 'DiscFac': array(77320072.38929991)}, '(55,60]': {'CRRA': array(5594483.26115322), 'DiscFac': array(1.10187439e+08)}, '(70,75]': {'CRRA': array(5963233.74826386), 'DiscFac': array(75656548.91590396)}, '(75,80]': {'CRRA': array(4435123.21199968), 'DiscFac': array(69778435.57071303)}, '(80,85]': {'CRRA': array(2653892.76373984), 'DiscFac': array(77178812.33691593)}, '(85,90]': {'CRRA': array(366575.86700859), 'DiscFac': array(54283037.56225435)}, '(90,95]': {'CRRA': array(852088.40803855), 'DiscFac': array(11260155.96864601)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv b/content/tables/msm/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv index 44f9329..93b052a 100644 --- a/content/tables/msm/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv +++ b/content/tables/msm/WealthPortfolioSub(Stock)(Labor)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,8.151823754063646 -DiscFac,1.0042349870035157 -time_to_estimate,334.42864179611206 +CRRA,10.516758257492437 +DiscFac,1.0471170203613893 +time_to_estimate,609.2074275016785 +_params,"{'CRRA': 10.516758257492437, 'DiscFac': 1.0471170203613893}" +_internal_estimates,"InternalParams(values=array([10.51675826, 1.04711702]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([10.51675826, 1.04711702]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), 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array(1.90282463), '(90,95]': array(0.)}, '(90,95]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.56618589)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7f999bba6d40>, params_from_internal=._params_from_internal at 0x7f999bba7be0>, derivative_to_internal=._derivative_to_internal at 0x7f999ba23130>, func_to_internal=._func_to_internal at 0x7f999ba21510>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 4.95550858e-05 -1.59011871e-04 + 6.46025432e-05 -7.75378886e-06 -9.90751500e-04 1.05939616e-03] + [-8.29455113e-06 1.94278530e-03 6.21427749e-05 1.33977556e-04 + 4.05035336e-05 3.24097529e-05 -9.09560608e-06 -3.48865507e-04 + -4.45091936e-04 -9.40023292e-04 4.15494682e-04 -4.44444208e-03] + [ 2.62343723e-06 6.21427749e-05 1.76211792e-03 -9.07845887e-05 + -3.11748414e-05 -7.20625366e-05 1.23099100e-04 1.21619646e-04 + 2.42370531e-04 -4.53895101e-04 4.77856997e-04 -2.15951955e-03] + [ 1.27222785e-05 1.33977556e-04 -9.07845887e-05 1.65143639e-03 + -2.53506647e-04 -1.19096295e-04 5.24085619e-05 -2.00215176e-06 + -8.96273332e-05 4.13045513e-05 3.57275514e-04 -2.29200628e-05] + [-6.70714064e-05 4.05035336e-05 -3.11748414e-05 -2.53506647e-04 + 3.57237090e-03 1.96792266e-04 1.02791978e-04 -6.06222612e-04 + -8.16268084e-04 1.18356570e-03 -1.21385564e-03 -1.39621761e-03] + [ 3.51434321e-05 3.24097529e-05 -7.20625366e-05 -1.19096295e-04 + 1.96792266e-04 7.63662771e-03 1.87644341e-04 -6.91727595e-04 + -8.41433602e-04 7.97137838e-04 -3.67034802e-03 7.47381063e-05] + [ 4.95550858e-05 -9.09560608e-06 1.23099100e-04 5.24085619e-05 + 1.02791978e-04 1.87644341e-04 1.63798336e-02 4.49609135e-04 + 3.74504692e-04 -4.32072461e-04 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1.76620438e+00]]" +_internal_weights,"[[2.15828168e+03 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 5.14724916e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 5.67498911e+02 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 6.05533465e+02 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 2.79926141e+02 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 1.30947853e+02 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 6.10506811e+01 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.63716412e+01 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 1.66886394e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 0.00000000e+00 8.15237029e+00 0.00000000e+00 0.00000000e+00] + [0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00 + 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13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(0.02753828), 'DiscFac': array(0.80254638)}, '(30,35]': {'CRRA': array(0.03013127), 'DiscFac': array(3.97638493)}, '(35,40]': {'CRRA': array(0.00289575), 'DiscFac': array(14.86849675)}, '(40,45]': {'CRRA': array(-0.06826327), 'DiscFac': array(26.95995037)}, '(45,50]': {'CRRA': array(-0.07040581), 'DiscFac': array(31.94202063)}, '(50,55]': {'CRRA': array(-0.04459966), 'DiscFac': array(35.69047234)}, '(55,60]': {'CRRA': array(-0.11011583), 'DiscFac': array(44.4026696)}, '(70,75]': {'CRRA': array(-0.02619741), 'DiscFac': array(65.72800577)}, '(75,80]': {'CRRA': array(-0.01522686), 'DiscFac': array(64.04343606)}, '(80,85]': {'CRRA': array(-0.04872984), 'DiscFac': array(61.09732165)}, '(85,90]': {'CRRA': array(-0.09142146), 'DiscFac': array(44.72242437)}, '(90,95]': {'CRRA': array(-0.12503161), 'DiscFac': array(23.85410053)}}" +_no_jacobian_reason, +_cache,{} diff --git a/content/tables/msm/WealthPortfolioSub(Stock)Market_estimate_results.csv b/content/tables/msm/WealthPortfolioSub(Stock)Market_estimate_results.csv index 3fd83f9..f1aa708 100644 --- a/content/tables/msm/WealthPortfolioSub(Stock)Market_estimate_results.csv +++ b/content/tables/msm/WealthPortfolioSub(Stock)Market_estimate_results.csv @@ -1,3 +1,97 @@ -CRRA,4.485551374454349 -DiscFac,0.8395292628478657 -time_to_estimate,247.60653281211853 +CRRA,6.628337898636361 +DiscFac,0.7815105267511713 +time_to_estimate,584.865697145462 +_params,"{'CRRA': 6.628337898636361, 'DiscFac': 0.7815105267511713}" +_internal_estimates,"InternalParams(values=array([6.6283379 , 0.78151053]), lower_bounds=array([1.1, 0.5]), upper_bounds=array([20. , 1.1]), soft_lower_bounds=None, soft_upper_bounds=None, names=['CRRA', 'DiscFac'], free_mask=array([ True, True]))" +_free_estimates,"FreeParams(values=array([6.6283379 , 0.78151053]), free_mask=array([ True, True]), free_names=['CRRA', 'DiscFac'], all_names=['CRRA', 'DiscFac'])" +_weights,"{'(25,30]': {'(25,30]': array(2158.28167807), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(30,35]': {'(25,30]': array(0.), '(30,35]': array(514.72491632), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(35,40]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(567.49891065), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(40,45]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(605.53346513), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(45,50]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(279.92614085), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(50,55]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(130.94785271), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.)}, '(55,60]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), 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'(85,90]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(1.90282463), '(90,95]': array(0.)}, '(90,95]': {'(25,30]': array(0.), '(30,35]': array(0.), '(35,40]': array(0.), '(40,45]': array(0.), '(45,50]': array(0.), '(50,55]': array(0.), '(55,60]': array(0.), '(70,75]': array(0.), '(75,80]': array(0.), '(80,85]': array(0.), '(85,90]': array(0.), '(90,95]': array(0.56618589)}}" +_converter,"Converter(params_to_internal=._params_to_internal at 0x7fb0d1d83520>, params_from_internal=._params_from_internal at 0x7fb0d1d828c0>, derivative_to_internal=._derivative_to_internal at 0x7fb0d1d83640>, func_to_internal=._func_to_internal at 0x7fb0d1d83be0>, has_transforming_constraints=False)" +_internal_moments_cov,"[[ 4.63331552e-04 -8.29455113e-06 2.62343723e-06 1.27222785e-05 + -6.70714064e-05 3.51434321e-05 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2.30949007e+06 7.37448130e+07] + [ 2.85707886e+06 2.58745006e+07]]" +_empirical_moments,"{'(25,30]': 0.5574998121910544, '(30,35]': 0.9675607353906764, '(35,40]': 2.168068545096722, '(40,45]': 3.234950933121561, '(45,50]': 4.688547995398578, '(50,55]': 6.142846523177507, '(55,60]': 8.209944114318054, '(70,75]': 13.905111816750804, '(75,80]': 14.562181663837016, '(80,85]': 14.358754447244063, '(85,90]': 15.71527224435591, '(90,95]': 17.99554082928189}" +_has_constraints,False +_jacobian,"{'(25,30]': {'CRRA': array(-346847.04597687), 'DiscFac': array(92597.28034004)}, '(30,35]': {'CRRA': array(402465.61683345), 'DiscFac': array(-4314013.08571286)}, '(35,40]': {'CRRA': array(4892905.77563128), 'DiscFac': array(11000918.07854996)}, '(40,45]': {'CRRA': array(6528866.41488908), 'DiscFac': array(34464670.39935617)}, '(45,50]': {'CRRA': array(8945769.11977064), 'DiscFac': array(94032602.0866464)}, '(50,55]': {'CRRA': array(17112886.48638538), 'DiscFac': array(1.28715022e+08)}, '(55,60]': {'CRRA': array(31596613.66590362), 'DiscFac': array(1.89704877e+08)}, '(70,75]': {'CRRA': array(13079150.10841221), 'DiscFac': array(33993316.77791537)}, '(75,80]': {'CRRA': array(5493466.22090993), 'DiscFac': array(39414797.73483838)}, '(80,85]': {'CRRA': array(2527103.49691757), 'DiscFac': array(67796062.74984673)}, '(85,90]': {'CRRA': array(2309490.06664654), 'DiscFac': array(73744812.9947672)}, '(90,95]': {'CRRA': array(2857078.85766507), 'DiscFac': array(25874500.62208251)}}" +_no_jacobian_reason, +_cache,{}