From db1815ea00015859c68ccdf101679497c2ceff75 Mon Sep 17 00:00:00 2001 From: "Matthew N. White" Date: Sat, 27 Feb 2021 22:06:48 -0500 Subject: [PATCH] Update model text in rep agent model The text in the rep agent example was copy-pasted from the medical shock model. It looks like some stuff got fixed, but I cleaned up the rest. Notebook should probably be run again by someone with the latest version of HARK installed locally. Only markdown cells changed on this committ --- .../example_ConsRepAgentModel.ipynb | 122 +++++------------- 1 file changed, 34 insertions(+), 88 deletions(-) diff --git a/examples/ConsumptionSaving/example_ConsRepAgentModel.ipynb b/examples/ConsumptionSaving/example_ConsRepAgentModel.ipynb index e43527e66..2ef9f56ab 100644 --- a/examples/ConsumptionSaving/example_ConsRepAgentModel.ipynb +++ b/examples/ConsumptionSaving/example_ConsRepAgentModel.ipynb @@ -2,20 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/sb/projects/econ-ark/HARK/HARK/Calibration/Income/IncomeTools.py:449: UserWarning: Sabelhaus and Song (2010) provide variance profiles for ages 27 to 54. Extrapolating variances using the extreme points.\n", - " warn(\n", - "/home/sb/projects/econ-ark/HARK/HARK/datasets/SCF/WealthIncomeDist/SCFDistTools.py:79: UserWarning: Returning SCF summary statistics for ages (20,25].\n", - " warn(\"Returning SCF summary statistics for ages \" + age_bracket + \".\")\n" - ] - } - ], + "outputs": [], "source": [ "from copy import deepcopy\n", "from time import time\n", @@ -39,6 +28,17 @@ "In RA models, all attributes are either time invariant or exist on a short cycle. Also, models must be infinite horizon." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each period, the representative agent makes a decision about how much of his resources $m_t$ he should consume $c_t$ and how much should retain as assets $a_t$. He gets a flow of utility from consumption, with CRRA preferences (with coefficient $\\rho$). Retained assets are used to finance productive capital $k_{t+1}$ in the next period. Output is produced according to a Cobb-Douglas production function using capital and labor $\\ell_{t+1}$, with a capital share of $\\alpha$; a fraction $\\delta$ of capital depreciates immediately after production.\n", + "\n", + "The agent's labor productivity is subject to permanent and transitory shocks, $\\psi_t$ and $\\theta_t$ respectively. The representative agent stands in for a continuum of identical households, so markets are assumed competitive: the factor returns to capital and income are the (net) marginal product of these inputs.\n", + "\n", + "In the notation below, all lowercase state and control variables ($m_t$, $c_t$, etc) are normalized by the permanent labor productivity of the agent. The level of these variables at any time $t$ can be recovered by multiplying by permanent labor productivity $p_t$ (itself usually normalized to 1 at model start)." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -46,12 +46,14 @@ "The agent's problem can be written in Bellman form as:\n", "\n", "\\begin{eqnarray*}\n", - "v_t(M_t,p_t) &=& \\max_{c_t} U(c_t) + \\beta (1-\\mathsf{D}_{t+1}) \\mathbb{E} [v_{t+1}(M_{t+1}, p_{t+1})], \\\\\n", - "a_t &=& M_t - c_t, \\\\\n", - "a_t &\\geq& \\underline{a}, \\\\\n", - "M_{t+1} &=& R a_t + \\theta_{t+1}, \\\\\n", - "p_{t+1} &=& \\Gamma_{t+1}\\psi_{t+1}, \\\\\n", - "\\psi_t \\sim F_{\\psi t} &\\qquad& \\theta_t \\sim F_{\\theta t}, \\mathbb{E} [F_{\\psi t}] = 1, \\\\\n", + "v_t(m_t) &=& \\max_{c_t} U(c_t) + \\beta \\mathbb{E} [(\\Gamma_{t+1}\\psi_{t+1})^{1-\\rho} v_{t+1}(m_{t+1})], \\\\\n", + "a_t &=& m_t - c_t, \\\\\n", + "\\psi_{t+1} &\\sim& F_{\\psi t+1}, \\qquad \\mathbb{E} [F_{\\psi t}] = 1,\\\\\n", + "\\theta_{t+1} &\\sim& F_{\\theta t+1}, \\\\\n", + "k_{t+1} &=& a_t/(\\Gamma_{t+1}\\psi_{t+1}), \\\\\n", + "R_{t+1} &=& 1 - \\delta + \\alpha (k_{t+1}/\\theta_{t+1})^{(\\alpha - 1)}, \\\\\n", + "w_{t+1} &=& (1-\\alpha) (k_{t+1}/\\theta_{t+1})^\\alpha, \\\\\n", + "m_{t+1} &=& R_{t+1} k_{t+1} + w_{t+1}\\theta_{t+1}, \\\\\n", "U(c) &=& \\frac{c^{1-\\rho}}{1-\\rho}\n", "\\end{eqnarray*}" ] @@ -60,12 +62,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The one period problem for this model is solved by the function $\\texttt{solveConsMedShock}$, which creates an instance of the class $\\texttt{ConsMedShockSolver}$. The class $\\texttt{MedShockConsumerType}$ extends $\\texttt{PersistentShockConsumerType}$ from $\\texttt{GenIncProcessModel}$ to represents agents in this model." + "The one period problem for this model is solved by the function $\\texttt{solveConsRepAgent}$." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -79,29 +81,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solving a representative agent problem took 0.17458868026733398 seconds.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Make and solve a rep agent model\n", "RAexample = RepAgentConsumerType(**RA_params)\n", @@ -116,17 +98,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulating a representative agent for 2000 periods took 1.5887715816497803 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "# Simulate the representative agent model\n", "RAexample.T_sim = 2000\n", @@ -146,29 +120,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solving a two state representative agent problem took 0.38216400146484375 seconds.\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Make and solve a Markov representative agent\n", "RA_markov_params = deepcopy(RA_params)\n", @@ -190,17 +144,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulating a two state representative agent for 2000 periods took 1.9312803745269775 seconds.\n" - ] - } - ], + "outputs": [], "source": [ "# Simulate the two state representative agent model\n", "RAmarkovExample.T_sim = 2000\n", @@ -226,9 +172,9 @@ "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "econ-ark-3.8", + "display_name": "Python 3", "language": "python", - "name": "econ-ark-3.8" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -240,7 +186,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.7" + "version": "3.7.3" } }, "nbformat": 4,