From d50512c180adeb3b95d2a8aad4228df584439e33 Mon Sep 17 00:00:00 2001 From: sidd3888 Date: Wed, 22 Nov 2023 18:50:43 +0530 Subject: [PATCH 1/4] Update KinkedRconsumerType.ipynb --- .../KinkedRconsumerType.ipynb | 116 +++++++++--------- 1 file changed, 59 insertions(+), 57 deletions(-) diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb index d98d74d20..ce423c351 100644 --- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb +++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb @@ -8,7 +8,7 @@ "source": [ "# Consumption-Saving model with Idiosyncratic Income Shocks and Different Interest Rates on Borrowing and Saving\n", "\n", - "**The `KinkedRconsumerType` class**" + "**The** `KinkedRconsumerType` **class**" ] }, { @@ -57,25 +57,27 @@ "source": [ "## Statement of \"kinked R\" model\n", "\n", - "Consider a small extension to the model faced by `IndShockConsumerType`s: that the interest rate on borrowing $a_t < 0$ is greater than the interest rate on saving $a_t > 0$. Consumers who face this kind of problem are represented by the $\\texttt{KinkedRconsumerType}$ class.\n", + "Consider a small extension to the model faced by the `IndShockConsumerType`: that the interest rate on borrowing ($a_t < 0$) is greater than the interest rate on saving ($a_t > 0$). Consumers who face this kind of problem are represented by the `KinkedRconsumerType` class.\n", "\n", "For a full theoretical treatment, this model analyzed in [A Theory of the Consumption Function, With\n", - "and Without Liquidity Constraints](https://www.econ2.jhu.edu/people/ccarroll/ATheoryv3JEP.pdf)\n", - "and its [expanded edition](https://www.econ2.jhu.edu/people/ccarroll/ATheoryv3NBER.pdf).\n", + "and Without Liquidity Constraints](https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.15.3.23)\n", + "and its [expanded edition](https://www.nber.org/system/files/working_papers/w8387/w8387.pdf).\n", "\n", "Continuing to work with *normalized* variables (e.g. $m_t$ represents the level of market resources divided by permanent income), the \"kinked R\" model can be stated as:\n", "\n", - "\\begin{eqnarray*}\n", - "v_t(m_t) &=& \\max_{c_t} {~} U(c_t) + \\DiscFac (1-\\DiePrb_{t+1}) \\mathbb{E}_{t} \\left[ (\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(m_{t+1}) \\right], \\\\\n", - "a_t &=& m_t - c_t, \\\\\n", - "a_t &\\geq& \\underline{a}, \\\\\n", - "m_{t+1} &=& \\Rfree_t/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", - "\\Rfree_t &=& \\cases{\\Rfree_{boro} \\texttt{ if } a_t < 0 \\\\\n", - "\\,\\! \\Rfree_{save} \\texttt{ if } a_t \\geq 0},\\\\\n", - "\\Rfree_{boro} &>& \\Rfree_{save}, \\\\\n", - "(\\psi_{t+1},\\theta_{t+1}) &\\sim& F_{t+1}, \\\\\n", - "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &=& 1.\n", - "\\end{eqnarray*}" + "\\begin{align*}\n", + "v_t(m_t) &= \\max_{c_t} u(c_t) + \\DiscFac (1-\\DiePrb_{t+1}) \\mathbb{E}_{t} \\left[(\\PermGroFac_{t+1}\\psi_{t+1})^{1-\\CRRA} v_{t+1}(m_{t+1}) \\right], \\\\\n", + "a_t &= m_t - c_t, \\\\\n", + "a_t &\\geq \\underline{a}, \\\\\n", + "m_{t+1} &= \\Rfree_t/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", + "\\Rfree_t &= \\begin{cases}\n", + " \\Rfree_{boro} & \\text{if } a_t < 0\\\\\n", + " \\Rfree_{save} & \\text{if } a_t \\geq 0,\n", + "\\end{cases}\\\\\n", + "\\Rfree_{boro} &> \\Rfree_{save}, \\\\\n", + "(\\psi_{t+1},\\theta_{t+1}) &\\sim F_{t+1}, \\\\\n", + "\\mathbb{E}[\\psi]=\\mathbb{E}[\\theta] &= 1.\n", + "\\end{align*}" ] }, { @@ -95,37 +97,37 @@ "source": [ "## Example parameter values to construct an instance of KinkedRconsumerType\n", "\n", - "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`. The only difference is that the parameter $\\texttt{Rfree}$ is replaced with $\\texttt{Rboro}$ and $\\texttt{Rsave}$.\n", + "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`. The only difference is that the parameter `Rfree` is replaced with `Rboro` and `Rsave`.\n", "\n", - "While the parameter $\\texttt{CubicBool}$ is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model. In the future, this restriction will be lifted.\n", + "While the parameter `CubicBool` is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model. In the future, this restriction will be lifted.\n", "\n", "| Parameter | Description | Code | Example value | Time-varying? |\n", "| :---: | --- | --- | --- | --- |\n", - "| $\\DiscFac$ |Intertemporal discount factor | $\\texttt{DiscFac}$ | $0.96$ | |\n", - "| $\\CRRA$ |Coefficient of relative risk aversion | $\\texttt{CRRA}$ | $2.0$ | |\n", - "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\texttt{Rboro}$ | $1.20$ | |\n", - "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\texttt{Rsave}$ | $1.01$ | |\n", - "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\texttt{LivPrb}$ | $[0.98]$ | $\\surd$ |\n", - "|$\\PermGroFac_{t+1}$|Permanent income growth factor|$\\texttt{PermGroFac}$| $[1.01]$ | $\\surd$ |\n", - "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | $\\texttt{PermShkStd}$ | $[0.1]$ |$\\surd$ |\n", - "| $N_\\psi$ | Number of discrete permanent income shocks | $\\texttt{PermShkCount}$ | $7$ | |\n", - "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | $\\texttt{TranShkStd}$ | $[0.2]$ | $\\surd$ |\n", - "| $N_\\theta$ | Number of discrete transitory income shocks | $\\texttt{TranShkCount}$ | $7$ | |\n", - "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\texttt{UnempPrb}$ | $0.05$ | |\n", - "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\texttt{IncUnemp}$ | $0.3$ | |\n", - "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\texttt{UnempPrb}$ | $0.0005$ | |\n", - "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | $\\texttt{IncUnemp}$ | $0.0$ | |\n", - "| $(none)$ | Period of the lifecycle model when retirement begins | $\\texttt{T_retire}$ | $0$ | |\n", - "| $(none)$ | Minimum value in assets-above-minimum grid | $\\texttt{aXtraMin}$ | $0.001$ | |\n", - "| $(none)$ | Maximum value in assets-above-minimum grid | $\\texttt{aXtraMax}$ | $20.0$ | |\n", - "| $(none)$ | Number of points in base assets-above-minimum grid | $\\texttt{aXtraCount}$ | $48$ | |\n", - "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\texttt{aXtraNestFac}$ | $3$ | |\n", - "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\texttt{aXtraExtra}$ | $None$ | |\n", - "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | $\\texttt{BoroCnstArt}$ | $None$ | |\n", - "| $(none)$ |Indicator for whether $\\texttt{vFunc}$ should be computed | $\\texttt{vFuncBool}$ | $True$ | |\n", - "| $(none)$ |Indicator for whether $\\texttt{cFunc}$ should use cubic splines | $\\texttt{CubicBool}$ | $False$ | |\n", - "|$T$| Number of periods in this type's \"cycle\" |$\\texttt{T_cycle}$| $1$ | |\n", - "|(none)| Number of times the \"cycle\" occurs |$\\texttt{cycles}$| $0$ | |\n", + "| $\\DiscFac$ |Intertemporal discount factor | `DiscFac` | $0.96$ | |\n", + "| $\\CRRA$ |Coefficient of relative risk aversion | `CRRA` | $2.0$ | |\n", + "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | `Rboro` | $1.20$ | |\n", + "| $\\Rfree_{save}$ | Risk free interest factor for saving | `Rsave` | $1.01$ | |\n", + "| $1 - \\DiePrb_{t+1}$ |Survival probability | `LivPrb` | $[0.98]$ | $\\surd$ |\n", + "|$\\PermGroFac_{t+1}$|Permanent income growth factor|`PermGroFac`| $[1.01]$ | $\\surd$ |\n", + "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | `PermShkStd` | $[0.1]$ |$\\surd$ |\n", + "| $N_\\psi$ | Number of discrete permanent income shocks | `PermShkCount` | $7$ | |\n", + "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | `TranShkStd` | $[0.2]$ | $\\surd$ |\n", + "| $N_\\theta$ | Number of discrete transitory income shocks | `TranShkCount` | $7$ | |\n", + "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | `UnempPrb` | $0.05$ | |\n", + "| $\\underline{\\theta}$ | Transitory shock when unemployed | `IncUnemp` | $0.3$ | |\n", + "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | `UnempPrbRet` | $0.0005$ | |\n", + "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | `IncUnempRet` | $0.0$ | |\n", + "| $(none)$ | Period of the lifecycle model when retirement begins | `T_retire` | $0$ | |\n", + "| $(none)$ | Minimum value in assets-above-minimum grid | `aXtraMin` | $0.001$ | |\n", + "| $(none)$ | Maximum value in assets-above-minimum grid | `aXtraMax` | $20.0$ | |\n", + "| $(none)$ | Number of points in base assets-above-minimum grid | `aXtraCount` | $48$ | |\n", + "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | `aXtraNestFac` | $3$ | |\n", + "| $(none)$ | Additional values to add to assets-above-minimum grid | `aXtraExtra` | $None$ | |\n", + "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | `BoroCnstArt` | $None$ | |\n", + "| $(none)$ |Indicator for whether `vFunc` should be computed | `vFuncBool` | $True$ | |\n", + "| $(none)$ |Indicator for whether `cFunc` should use cubic splines | `CubicBool` | $False$ | |\n", + "|$T$| Number of periods in this type's \"cycle\" |`T_cycle`| $1$ | |\n", + "|(none)| Number of times the \"cycle\" occurs |`cycles`| $0$ | |\n", "\n", "These example parameters are almost identical to those used for `IndShockExample` in the prior notebook, except that the interest rate on borrowing is 20% (like a credit card), and the interest rate on saving is 1%. Moreover, the artificial borrowing constraint has been set to `None`. The cell below defines a parameter dictionary with these example values." ] @@ -226,7 +228,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -270,16 +272,16 @@ "\n", "| Description | Code | Example value |\n", "| :---: | --- | --- |\n", - "| Number of consumers of this type | $\\texttt{AgentCount}$ | $10000$ |\n", - "| Number of periods to simulate | $\\texttt{T_sim}$ | $500$ |\n", - "| Mean of initial log (normalized) assets | $\\texttt{aNrmInitMean}$ | $-6.0$ |\n", - "| Stdev of initial log (normalized) assets | $\\texttt{aNrmInitStd}$ | $1.0$ |\n", - "| Mean of initial log permanent income | $\\texttt{pLvlInitMean}$ | $0.0$ |\n", - "| Stdev of initial log permanent income | $\\texttt{pLvlInitStd}$ | $0.0$ |\n", - "| Aggregrate productivity growth factor | $\\texttt{PermGroFacAgg}$ | $1.0$ |\n", - "| Age after which consumers are automatically killed | $\\texttt{T_age}$ | $None$ |\n", + "| Number of consumers of this type | `AgentCount` | $10000$ |\n", + "| Number of periods to simulate | `T_sim` | $500$ |\n", + "| Mean of initial log (normalized) assets | `aNrmInitMean` | $-6.0$ |\n", + "| Stdev of initial log (normalized) assets | `aNrmInitStd` | $1.0$ |\n", + "| Mean of initial log permanent income | `pLvlInitMean` | $0.0$ |\n", + "| Stdev of initial log permanent income | `pLvlInitStd` | $0.0$ |\n", + "| Aggregrate productivity growth factor | `PermGroFacAgg` | $1.0$ |\n", + "| Age after which consumers are automatically killed | `T_age` | $None$ |\n", "\n", - "Here, we will simulate 10,000 consumers for 500 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\texttt{pLvlInitMean})$, as $\\texttt{pLvlInitStd}$ has been set to zero; they will have essentially zero assets at birth, as $\\texttt{aNrmInitMean}$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", + "Here, we will simulate 10,000 consumers for 500 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp($ `pLvlInitMean` $)$, as `pLvlInitStd` has been set to zero; they will have essentially zero assets at birth, as `aNrmInitMean` is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", "\n", "These example parameter values were already passed as part of the parameter dictionary that we used to create `KinkyExample`, so it is ready to simulate. We need to set the `track_vars` attribute to indicate the variables for which we want to record a *history*." ] @@ -358,7 +360,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -388,7 +390,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -414,7 +416,7 @@ "source": [ "We can see there's a significant point mass of consumers with *exactly* $a_t=0$; these are consumers who do not find it worthwhile to give up a bit of consumption to begin saving (because $\\Rfree_{save}$ is too low), and also are not willing to finance additional consumption by borrowing (because $\\Rfree_{boro}$ is too high).\n", "\n", - "The smaller point masses in this distribution are due to $\\texttt{HARK}$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks. For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$. You can verify this by changing $\\texttt{TranShkCount}$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye." + "The smaller point masses in this distribution are due to `HARK` drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks. For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$. You can verify this by changing `TranShkCount` to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye." ] } ], @@ -439,7 +441,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.18" } }, "nbformat": 4, From fd573293edcd7f3d7b87588c3bbef2f048390729 Mon Sep 17 00:00:00 2001 From: sidd3888 Date: Thu, 23 Nov 2023 12:56:56 +0530 Subject: [PATCH 2/4] indentation fix --- examples/ConsIndShockModel/KinkedRconsumerType.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb index ce423c351..9b51a6e89 100644 --- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb +++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb @@ -71,8 +71,8 @@ "a_t &\\geq \\underline{a}, \\\\\n", "m_{t+1} &= \\Rfree_t/(\\PermGroFac_{t+1} \\psi_{t+1}) a_t + \\theta_{t+1}, \\\\\n", "\\Rfree_t &= \\begin{cases}\n", - " \\Rfree_{boro} & \\text{if } a_t < 0\\\\\n", - " \\Rfree_{save} & \\text{if } a_t \\geq 0,\n", + "\\Rfree_{boro} & \\text{if } a_t < 0\\\\\n", + "\\Rfree_{save} & \\text{if } a_t \\geq 0,\n", "\\end{cases}\\\\\n", "\\Rfree_{boro} &> \\Rfree_{save}, \\\\\n", "(\\psi_{t+1},\\theta_{t+1}) &\\sim F_{t+1}, \\\\\n", From 7c99c8ac86ba1ee75803ee76d21c9c3aa67366fc Mon Sep 17 00:00:00 2001 From: sidd3888 Date: Thu, 23 Nov 2023 13:16:19 +0530 Subject: [PATCH 3/4] parameter formatting --- .../KinkedRconsumerType.ipynb | 74 +++++++++---------- 1 file changed, 37 insertions(+), 37 deletions(-) diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb index 9b51a6e89..a0cb080de 100644 --- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb +++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb @@ -97,37 +97,37 @@ "source": [ "## Example parameter values to construct an instance of KinkedRconsumerType\n", "\n", - "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`. The only difference is that the parameter `Rfree` is replaced with `Rboro` and `Rsave`.\n", + "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`. The only difference is that the parameter $\\verb|Rfree|$ is replaced with $\\verb|Rboro|$ and $\\verb|Rsave|$.\n", "\n", - "While the parameter `CubicBool` is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model. In the future, this restriction will be lifted.\n", + "While the parameter $\\verb|CubicBool|$ is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model. In the future, this restriction will be lifted.\n", "\n", "| Parameter | Description | Code | Example value | Time-varying? |\n", "| :---: | --- | --- | --- | --- |\n", - "| $\\DiscFac$ |Intertemporal discount factor | `DiscFac` | $0.96$ | |\n", - "| $\\CRRA$ |Coefficient of relative risk aversion | `CRRA` | $2.0$ | |\n", - "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | `Rboro` | $1.20$ | |\n", - "| $\\Rfree_{save}$ | Risk free interest factor for saving | `Rsave` | $1.01$ | |\n", - "| $1 - \\DiePrb_{t+1}$ |Survival probability | `LivPrb` | $[0.98]$ | $\\surd$ |\n", - "|$\\PermGroFac_{t+1}$|Permanent income growth factor|`PermGroFac`| $[1.01]$ | $\\surd$ |\n", - "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | `PermShkStd` | $[0.1]$ |$\\surd$ |\n", - "| $N_\\psi$ | Number of discrete permanent income shocks | `PermShkCount` | $7$ | |\n", - "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | `TranShkStd` | $[0.2]$ | $\\surd$ |\n", - "| $N_\\theta$ | Number of discrete transitory income shocks | `TranShkCount` | $7$ | |\n", - "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | `UnempPrb` | $0.05$ | |\n", - "| $\\underline{\\theta}$ | Transitory shock when unemployed | `IncUnemp` | $0.3$ | |\n", - "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | `UnempPrbRet` | $0.0005$ | |\n", - "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | `IncUnempRet` | $0.0$ | |\n", - "| $(none)$ | Period of the lifecycle model when retirement begins | `T_retire` | $0$ | |\n", - "| $(none)$ | Minimum value in assets-above-minimum grid | `aXtraMin` | $0.001$ | |\n", - "| $(none)$ | Maximum value in assets-above-minimum grid | `aXtraMax` | $20.0$ | |\n", - "| $(none)$ | Number of points in base assets-above-minimum grid | `aXtraCount` | $48$ | |\n", - "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | `aXtraNestFac` | $3$ | |\n", - "| $(none)$ | Additional values to add to assets-above-minimum grid | `aXtraExtra` | $None$ | |\n", - "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | `BoroCnstArt` | $None$ | |\n", - "| $(none)$ |Indicator for whether `vFunc` should be computed | `vFuncBool` | $True$ | |\n", - "| $(none)$ |Indicator for whether `cFunc` should use cubic splines | `CubicBool` | $False$ | |\n", - "|$T$| Number of periods in this type's \"cycle\" |`T_cycle`| $1$ | |\n", - "|(none)| Number of times the \"cycle\" occurs |`cycles`| $0$ | |\n", + "| $\\DiscFac$ |Intertemporal discount factor | $\\verb|DiscFac|$ | $0.96$ | |\n", + "| $\\CRRA$ |Coefficient of relative risk aversion | $\\verb|CRRA|$ | $2.0$ | |\n", + "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\verb|Rboro|$ | $1.20$ | |\n", + "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\verb|Rsave|$ | $1.01$ | |\n", + "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\verb|LivPrb|$ | $[0.98]$ | $\\surd$ |\n", + "|$\\PermGroFac_{t+1}$|Permanent income growth factor| $\\verb|PermGroFac|$ | $[1.01]$ | $\\surd$ |\n", + "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | $\\verb|PermShkStd|$ | $[0.1]$ |$\\surd$ |\n", + "| $N_\\psi$ | Number of discrete permanent income shocks | $\\verb|PermShkCount|$ | $7$ | |\n", + "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | $\\verb|TranShkStd|$ | $[0.2]$ | $\\surd$ |\n", + "| $N_\\theta$ | Number of discrete transitory income shocks | $\\verb|TranShkCount|$ | $7$ | |\n", + "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\verb|UnempPrb|$ | $0.05$ | |\n", + "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\verb|IncUnemp|$ | $0.3$ | |\n", + "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\verb|UnempPrbRet|$ | $0.0005$ | |\n", + "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | $\\verb|IncUnempRet|$ | $0.0$ | |\n", + "| $(none)$ | Period of the lifecycle model when retirement begins | $\\verb|T_retire|$ | $0$ | |\n", + "| $(none)$ | Minimum value in assets-above-minimum grid | $\\verb|aXtraMin|$ | $0.001$ | |\n", + "| $(none)$ | Maximum value in assets-above-minimum grid | $\\verb|aXtraMax|$ | $20.0$ | |\n", + "| $(none)$ | Number of points in base assets-above-minimum grid | $\\verb|aXtraCount|$ | $48$ | |\n", + "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\verb|aXtraNestFac|$ | $3$ | |\n", + "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\verb|aXtraExtra|$ | $None$ | |\n", + "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | $\\verb|BoroCnstArt|$ | $None$ | |\n", + "| $(none)$ |Indicator for whether $\\verb|vFunc|$ should be computed | $\\verb|vFuncBool|$ | $True$ | |\n", + "| $(none)$ |Indicator for whether $\\verb|cFunc|$ should use cubic splines | $\\verb|CubicBool|$ | $False$ | |\n", + "|$T$| Number of periods in this type's \"cycle\" | $\\verb|T_cycle|$ | $1$ | |\n", + "|(none)| Number of times the \"cycle\" occurs | $\\verb|cycles|$ | $0$ | |\n", "\n", "These example parameters are almost identical to those used for `IndShockExample` in the prior notebook, except that the interest rate on borrowing is 20% (like a credit card), and the interest rate on saving is 1%. Moreover, the artificial borrowing constraint has been set to `None`. The cell below defines a parameter dictionary with these example values." ] @@ -272,16 +272,16 @@ "\n", "| Description | Code | Example value |\n", "| :---: | --- | --- |\n", - "| Number of consumers of this type | `AgentCount` | $10000$ |\n", - "| Number of periods to simulate | `T_sim` | $500$ |\n", - "| Mean of initial log (normalized) assets | `aNrmInitMean` | $-6.0$ |\n", - "| Stdev of initial log (normalized) assets | `aNrmInitStd` | $1.0$ |\n", - "| Mean of initial log permanent income | `pLvlInitMean` | $0.0$ |\n", - "| Stdev of initial log permanent income | `pLvlInitStd` | $0.0$ |\n", - "| Aggregrate productivity growth factor | `PermGroFacAgg` | $1.0$ |\n", - "| Age after which consumers are automatically killed | `T_age` | $None$ |\n", + "| Number of consumers of this type | $\\verb|AgentCount|$ | $10000$ |\n", + "| Number of periods to simulate | $\\verb|T_sim|$ | $500$ |\n", + "| Mean of initial log (normalized) assets | $\\verb|aNrmInitMean|$ | $-6.0$ |\n", + "| Stdev of initial log (normalized) assets | $\\verb|aNrmInitStd|$ | $1.0$ |\n", + "| Mean of initial log permanent income | $\\verb|pLvlInitMean|$ | $0.0$ |\n", + "| Stdev of initial log permanent income | $\\verb|pLvlInitStd|$ | $0.0$ |\n", + "| Aggregrate productivity growth factor | $\\verb|PermGroFacAgg|$ | $1.0$ |\n", + "| Age after which consumers are automatically killed | $\\verb|T_age|$ | $None$ |\n", "\n", - "Here, we will simulate 10,000 consumers for 500 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp($ `pLvlInitMean` $)$, as `pLvlInitStd` has been set to zero; they will have essentially zero assets at birth, as `aNrmInitMean` is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", + "Here, we will simulate 10,000 consumers for 500 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\verb|pLvlInitMean|)$, as $\\verb|pLvlInitStd|$ has been set to zero; they will have essentially zero assets at birth, as $\\verb|aNrmInitMean|$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", "\n", "These example parameter values were already passed as part of the parameter dictionary that we used to create `KinkyExample`, so it is ready to simulate. We need to set the `track_vars` attribute to indicate the variables for which we want to record a *history*." ] @@ -416,7 +416,7 @@ "source": [ "We can see there's a significant point mass of consumers with *exactly* $a_t=0$; these are consumers who do not find it worthwhile to give up a bit of consumption to begin saving (because $\\Rfree_{save}$ is too low), and also are not willing to finance additional consumption by borrowing (because $\\Rfree_{boro}$ is too high).\n", "\n", - "The smaller point masses in this distribution are due to `HARK` drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks. For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$. You can verify this by changing `TranShkCount` to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye." + "The smaller point masses in this distribution are due to $\\verb|HARK|$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks. For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$. You can verify this by changing $\\verb|TranShkCount|$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye." ] } ], From fc93dcce953d361a6af836e6b3d2dd9119fcea5f Mon Sep 17 00:00:00 2001 From: sidd3888 Date: Thu, 23 Nov 2023 13:33:20 +0530 Subject: [PATCH 4/4] verb compatibility fix --- .../KinkedRconsumerType.ipynb | 74 +++++++++---------- 1 file changed, 37 insertions(+), 37 deletions(-) diff --git a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb index a0cb080de..71dbdb9eb 100644 --- a/examples/ConsIndShockModel/KinkedRconsumerType.ipynb +++ b/examples/ConsIndShockModel/KinkedRconsumerType.ipynb @@ -97,37 +97,37 @@ "source": [ "## Example parameter values to construct an instance of KinkedRconsumerType\n", "\n", - "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`. The only difference is that the parameter $\\verb|Rfree|$ is replaced with $\\verb|Rboro|$ and $\\verb|Rsave|$.\n", + "The parameters required to create an instance of `KinkedRconsumerType` are nearly identical to those for `IndShockConsumerType`. The only difference is that the parameter $\\verb!Rfree!$ is replaced with $\\verb!Rboro!$ and $\\verb!Rsave!$.\n", "\n", - "While the parameter $\\verb|CubicBool|$ is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model. In the future, this restriction will be lifted.\n", + "While the parameter $\\verb!CubicBool!$ is required to create a valid `KinkedRconsumerType` instance, it must be set to `False`; cubic spline interpolation has not yet been implemented for this model. In the future, this restriction will be lifted.\n", "\n", "| Parameter | Description | Code | Example value | Time-varying? |\n", "| :---: | --- | --- | --- | --- |\n", - "| $\\DiscFac$ |Intertemporal discount factor | $\\verb|DiscFac|$ | $0.96$ | |\n", - "| $\\CRRA$ |Coefficient of relative risk aversion | $\\verb|CRRA|$ | $2.0$ | |\n", - "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\verb|Rboro|$ | $1.20$ | |\n", - "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\verb|Rsave|$ | $1.01$ | |\n", - "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\verb|LivPrb|$ | $[0.98]$ | $\\surd$ |\n", - "|$\\PermGroFac_{t+1}$|Permanent income growth factor| $\\verb|PermGroFac|$ | $[1.01]$ | $\\surd$ |\n", - "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | $\\verb|PermShkStd|$ | $[0.1]$ |$\\surd$ |\n", - "| $N_\\psi$ | Number of discrete permanent income shocks | $\\verb|PermShkCount|$ | $7$ | |\n", - "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | $\\verb|TranShkStd|$ | $[0.2]$ | $\\surd$ |\n", - "| $N_\\theta$ | Number of discrete transitory income shocks | $\\verb|TranShkCount|$ | $7$ | |\n", - "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\verb|UnempPrb|$ | $0.05$ | |\n", - "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\verb|IncUnemp|$ | $0.3$ | |\n", - "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\verb|UnempPrbRet|$ | $0.0005$ | |\n", - "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | $\\verb|IncUnempRet|$ | $0.0$ | |\n", - "| $(none)$ | Period of the lifecycle model when retirement begins | $\\verb|T_retire|$ | $0$ | |\n", - "| $(none)$ | Minimum value in assets-above-minimum grid | $\\verb|aXtraMin|$ | $0.001$ | |\n", - "| $(none)$ | Maximum value in assets-above-minimum grid | $\\verb|aXtraMax|$ | $20.0$ | |\n", - "| $(none)$ | Number of points in base assets-above-minimum grid | $\\verb|aXtraCount|$ | $48$ | |\n", - "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\verb|aXtraNestFac|$ | $3$ | |\n", - "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\verb|aXtraExtra|$ | $None$ | |\n", - "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | $\\verb|BoroCnstArt|$ | $None$ | |\n", - "| $(none)$ |Indicator for whether $\\verb|vFunc|$ should be computed | $\\verb|vFuncBool|$ | $True$ | |\n", - "| $(none)$ |Indicator for whether $\\verb|cFunc|$ should use cubic splines | $\\verb|CubicBool|$ | $False$ | |\n", - "|$T$| Number of periods in this type's \"cycle\" | $\\verb|T_cycle|$ | $1$ | |\n", - "|(none)| Number of times the \"cycle\" occurs | $\\verb|cycles|$ | $0$ | |\n", + "| $\\DiscFac$ |Intertemporal discount factor | $\\verb!DiscFac!$ | $0.96$ | |\n", + "| $\\CRRA$ |Coefficient of relative risk aversion | $\\verb!CRRA!$ | $2.0$ | |\n", + "| $\\Rfree_{boro}$ | Risk free interest factor for borrowing | $\\verb!Rboro!$ | $1.20$ | |\n", + "| $\\Rfree_{save}$ | Risk free interest factor for saving | $\\verb!Rsave!$ | $1.01$ | |\n", + "| $1 - \\DiePrb_{t+1}$ |Survival probability | $\\verb!LivPrb!$ | $[0.98]$ | $\\surd$ |\n", + "| $\\PermGroFac_{t+1}$|Permanent income growth factor| $\\verb!PermGroFac!$ | $[1.01]$ | $\\surd$ |\n", + "| $\\sigma_\\psi$ | Standard deviation of log permanent income shocks | $\\verb!PermShkStd!$ | $[0.1]$ | $\\surd$ |\n", + "| $N_\\psi$ | Number of discrete permanent income shocks | $\\verb!PermShkCount!$ | $7$ | |\n", + "| $\\sigma_\\theta$ | Standard deviation of log transitory income shocks | $\\verb!TranShkStd!$ | $[0.2]$ | $\\surd$ |\n", + "| $N_\\theta$ | Number of discrete transitory income shocks | $\\verb!TranShkCount!$ | $7$ | |\n", + "| $\\mho$ | Probability of being unemployed and getting $\\theta=\\underline{\\theta}$ | $\\verb!UnempPrb!$ | $0.05$ | |\n", + "| $\\underline{\\theta}$ | Transitory shock when unemployed | $\\verb!IncUnemp!$ | $0.3$ | |\n", + "| $\\mho^{Ret}$ | Probability of being \"unemployed\" when retired | $\\verb!UnempPrbRet!$ | $0.0005$ | |\n", + "| $\\underline{\\theta}^{Ret}$ | Transitory shock when \"unemployed\" and retired | $\\verb!IncUnempRet!$ | $0.0$ | |\n", + "| $(none)$ | Period of the lifecycle model when retirement begins | $\\verb!T_retire!$ | $0$ | |\n", + "| $(none)$ | Minimum value in assets-above-minimum grid | $\\verb!aXtraMin!$ | $0.001$ | |\n", + "| $(none)$ | Maximum value in assets-above-minimum grid | $\\verb!aXtraMax!$ | $20.0$ | |\n", + "| $(none)$ | Number of points in base assets-above-minimum grid | $\\verb!aXtraCount!$ | $48$ | |\n", + "| $(none)$ | Exponential nesting factor for base assets-above-minimum grid | $\\verb!aXtraNestFac!$ | $3$ | |\n", + "| $(none)$ | Additional values to add to assets-above-minimum grid | $\\verb!aXtraExtra!$ | $None$ | |\n", + "| $\\underline{a}$ | Artificial borrowing constraint (normalized) | $\\verb!BoroCnstArt!$ | $None$ | |\n", + "| $(none)$ |Indicator for whether $\\verb!vFunc!$ should be computed | $\\verb!vFuncBool!$ | $True$ | |\n", + "| $(none)$ |Indicator for whether $\\verb!cFunc!$ should use cubic splines | $\\verb!CubicBool!$ | $False$ | |\n", + "| $T$| Number of periods in this type's \"cycle\" | $\\verb!T_cycle!$ | $1$ | |\n", + "| $(none)$ | Number of times the \"cycle\" occurs | $\\verb!cycles!$ | $0$ | |\n", "\n", "These example parameters are almost identical to those used for `IndShockExample` in the prior notebook, except that the interest rate on borrowing is 20% (like a credit card), and the interest rate on saving is 1%. Moreover, the artificial borrowing constraint has been set to `None`. The cell below defines a parameter dictionary with these example values." ] @@ -272,16 +272,16 @@ "\n", "| Description | Code | Example value |\n", "| :---: | --- | --- |\n", - "| Number of consumers of this type | $\\verb|AgentCount|$ | $10000$ |\n", - "| Number of periods to simulate | $\\verb|T_sim|$ | $500$ |\n", - "| Mean of initial log (normalized) assets | $\\verb|aNrmInitMean|$ | $-6.0$ |\n", - "| Stdev of initial log (normalized) assets | $\\verb|aNrmInitStd|$ | $1.0$ |\n", - "| Mean of initial log permanent income | $\\verb|pLvlInitMean|$ | $0.0$ |\n", - "| Stdev of initial log permanent income | $\\verb|pLvlInitStd|$ | $0.0$ |\n", - "| Aggregrate productivity growth factor | $\\verb|PermGroFacAgg|$ | $1.0$ |\n", - "| Age after which consumers are automatically killed | $\\verb|T_age|$ | $None$ |\n", + "| Number of consumers of this type | $\\verb!AgentCount!$ | $10000$ |\n", + "| Number of periods to simulate | $\\verb!T_sim!$ | $500$ |\n", + "| Mean of initial log (normalized) assets | $\\verb!aNrmInitMean!$ | $-6.0$ |\n", + "| Stdev of initial log (normalized) assets | $\\verb!aNrmInitStd!$ | $1.0$ |\n", + "| Mean of initial log permanent income | $\\verb!pLvlInitMean!$ | $0.0$ |\n", + "| Stdev of initial log permanent income | $\\verb!pLvlInitStd!$ | $0.0$ |\n", + "| Aggregrate productivity growth factor | $\\verb!PermGroFacAgg!$ | $1.0$ |\n", + "| Age after which consumers are automatically killed | $\\verb!T_age!$ | $None$ |\n", "\n", - "Here, we will simulate 10,000 consumers for 500 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\verb|pLvlInitMean|)$, as $\\verb|pLvlInitStd|$ has been set to zero; they will have essentially zero assets at birth, as $\\verb|aNrmInitMean|$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", + "Here, we will simulate 10,000 consumers for 500 periods. All newly born agents will start with permanent income of exactly $P_t = 1.0 = \\exp(\\verb!pLvlInitMean!)$, as $\\verb!pLvlInitStd!$ has been set to zero; they will have essentially zero assets at birth, as $\\verb!aNrmInitMean!$ is $-6.0$; assets will be less than $1\\%$ of permanent income at birth.\n", "\n", "These example parameter values were already passed as part of the parameter dictionary that we used to create `KinkyExample`, so it is ready to simulate. We need to set the `track_vars` attribute to indicate the variables for which we want to record a *history*." ] @@ -416,7 +416,7 @@ "source": [ "We can see there's a significant point mass of consumers with *exactly* $a_t=0$; these are consumers who do not find it worthwhile to give up a bit of consumption to begin saving (because $\\Rfree_{save}$ is too low), and also are not willing to finance additional consumption by borrowing (because $\\Rfree_{boro}$ is too high).\n", "\n", - "The smaller point masses in this distribution are due to $\\verb|HARK|$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks. For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$. You can verify this by changing $\\verb|TranShkCount|$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye." + "The smaller point masses in this distribution are due to $\\verb!HARK!$ drawing simulated income shocks from the discretized distribution, rather than the \"true\" lognormal distributions of shocks. For consumers who ended $t-1$ with $a_{t-1}=0$ in assets, there are only 8 values the transitory shock $\\theta_{t}$ can take on, and thus only 8 values of $m_t$ thus $a_t$ they can achieve; the value of $\\psi_t$ is immaterial to $m_t$ when $a_{t-1}=0$. You can verify this by changing $\\verb!TranShkCount!$ to some higher value, like 25, in the dictionary above, then running the subsequent cells; the smaller point masses will not be visible to the naked eye." ] } ],