{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

A Two-Asset Savings Model with an Income-Contribution Scheme

\n", "

Mateo Velásquez-Giraldo

\n", "

mvelasq2@jhu.edu

\n", "

Johns Hopkins University

\n", "\n", "This notebook demonstrates the use of the `RiskyContrib` agent type\n", "of the [HARK Toolkit](https://econ-ark.org/toolkit). The model represents an agent who can\n", "save using two different assets---one risky and the other risk-free---to insure\n", "against fluctuations in his income, but faces frictions to transferring funds between\n", "assets. The flexibility of its implementation and its inclusion in the HARK\n", "toolkit will allow users to adapt the model to realistic life-cycle calibrations, and\n", "also to embedded it in heterogeneous-agents macroeconomic models." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "code_folding": [ 0 ], "scrolled": true }, "outputs": [], "source": [ "# Preamble\n", "\n", "from HARK.ConsumptionSaving.ConsRiskyContribModel import (\n", " RiskyContribConsumerType,\n", " init_risky_contrib_lifecycle,\n", ")\n", "from time import time\n", "from copy import copy\n", "\n", "import numpy as np\n", "import seaborn as sns\n", "import pandas as pd\n", "from Simulations.tools import pol_funcs_dframe, age_profiles\n", "import os\n", "\n", "# This is a jupytext paired notebook that autogenerates a .py file\n", "# which can be executed from a terminal command line\n", "# But a terminal does not permit inline figures, so we need to test jupyter vs terminal\n", "# Google \"how can I check if code is executed in the ipython notebook\"\n", "from IPython import get_ipython # In case it was run from python instead of ipython\n", "\n", "# If the ipython process contains 'terminal' assume not in a notebook\n", "def in_ipynb():\n", " try:\n", " if 'terminal' in str(type(get_ipython())):\n", " return False\n", " else:\n", " return True\n", " except NameError:\n", " return False\n", " \n", "# Determine whether to make the figures inline (for spyder or jupyter)\n", "# vs whatever is the automatic setting that will apply if run from the terminal\n", "if in_ipynb():\n", " # %matplotlib inline generates a syntax error when run from the shell\n", " # so do this instead\n", " get_ipython().run_line_magic('matplotlib', 'inline')\n", "else:\n", " get_ipython().run_line_magic('matplotlib', 'auto')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Description\n", "\n", "I now discuss the main components of the model informally, and leave its full\n", "recursive mathematical representation for Section \\ref{sec:recursive}.\n", "\n", "### Time, mortality, and utility\n", "\n", "Time advances in discrete steps that I will index with $t$. The model can\n", "be used in both infinite and finite-horizon versions.\n", "\n", "Agents face an exogenous risk of death $\\delta_t$ each period, which becomes certain at the \n", "maximum age of the finite-horizon version. There are no intentional bequests; agents\n", "will consume all of their resources if they reach the last period, but they can leave\n", "accidental bequests upon premature death.\n", "\n", "In each period, agents derive utility from consumption only. Their utility function\n", "follows a constant relative risk aversion specification. Formally, for a level of \n", "consumption $C$, the agent derives instant utility\n", "\\begin{equation}\n", "\tu(C) = \\frac{C^{1-\\rho}}{1- \\rho}.\n", "\\end{equation}\n", "\n", "#### Income process\n", "\n", "Agents supply labor inelastically. Their labor earnings $Y_{i,t}$ are the product of a\n", "permanent component $P_{i,t}$ and a transitory stochastic component $\\theta_{i,t}$ as\n", "in \\cite{Carroll1997qje}, where $i$ indexes different agents. Formally,\n", "\\begin{equation*}\n", "\\begin{split}\n", "\\ln Y_{i,t} &= \\ln P_{i,t} + \\ln \\theta_{i,t} \\\\\n", "\\ln P_{i,t} &= \\ln P_{i,t-1} + \\ln \\Gamma_{i,t} + \\ln \\psi_{i,t}\n", "\\end{split}\n", "\\end{equation*}\n", "where $\\Gamma_{i,t}$ is a deterministic growth factor that can capture\n", "life-cycle patterns in earnings, and\n", "$\\ln \\psi_{i,t}\\sim \\mathcal{N}(-\\sigma^2_{\\psi,t}/2, \\sigma_{\\psi,t})$\n", "is a multiplicative shock to permanent income\\footnote{The mean of the shock is set so that $E[\\psi_{i,t}] = 1$.}.\n", "\n", "The transitory component $\\theta_{i,t}$ is a mixture that models unemployment and\n", "other temporal fluctuations in income as\n", "\\begin{equation*}\n", "\\ln\\theta_{i,t} = \\begin{cases}\n", "\\ln \\mathcal{U}, & \\text{With probability } \\mho\\\\\n", "\\ln \\tilde{\\theta}_{i,t}\\sim\\mathcal{N}(-\\sigma^2_{\\theta,t}/2, \\sigma_{\\theta,t}), & \\text{With probability } 1-\\mho,\n", "\\end{cases}\n", "\\end{equation*}\n", "with $\\mho$ representing the probability of unemployment and $\\mathcal{U}$ the replacement\n", "factor of unemployment benefits.\n", "\n", "This specification of the income process is parsimonious and flexible enough to accommodate\n", "life-cycle patterns in income growth and volatility, transitory unemployment and exogenous\n", "retirement. Introduced by \\cite{Carroll1997qje}, this income specification is common in studies\n", "of life-cycle wealth accumulation and portfolio choice; see e.g.,\n", "\\cite{Cagetti2003jbes,Cocco2005rfs,Fagereng2017jof}. The specification has\n", "also been used in studies of income volatility such as \\cite{Carroll1992bpea,Carroll1997jme,Sabelhaus2010jme}, which have yielded calibrations of its stochastic shocks' distributions.\n", "\n", "#### Financial assets and frictions\n", "\n", "Agents smooth their consumption by saving and have two assets\n", "available for this purpose. The first is a risk-free liquid account with \n", "constant per-period return factor $R$. The second has a stochastic return\n", "factor $\\tilde{R}$ that is log-normally distributed and independent across\n", "time. Various interpretations such as stocks, a retirement fund, or entrepreneurial\n", "capital could be given to the risky asset. Importantly, consumption must be paid for\n", "using funds from the risk-free account. The levels of risk-free and risky assets\n", "owned by the agent will both be state variables, denoted with $M_{i,t}$ and $N_{i,t}$\n", "respectively.\n", "\n", "Portfolio rebalancing takes place by moving funds between the risk-free\n", "and risky accounts. These flows are one of the agents' control variables\n", "and are denoted as $D_{i,t}$, with $D_{i,t}>0$ representing a movement of\n", "funds from the risk-free to the risky account. Withdrawals from the risky\n", "account are subject to a constant-rate tax $\\tau$ which can represent, for\n", "instance, capital-gains realization taxes or early retirement-fund withdrawal\n", "penalties. In sum, denoting post-rebalancing asset levels with $\\tilde{\\cdot}$,\n", "\\begin{equation*}\n", "\\begin{split}\n", "\\tilde{M}_{i,t} &= M_{i,t} - D_{i,t}(1 - 1_{[D_{i,t}\\leq0]}\\tau)\\\\\n", "\\tilde{N}_{i,t} &= N_{i,t} + D_{i,t}.\n", "\\end{split}\n", "\\end{equation*}\n", "\n", "At any given period, an agent might not be able to rebalance his portfolio.\n", "This ability is governed by an exogenous stochastic shock that is realized\n", "at the start of the period\n", "\n", "\\begin{equation*}\n", "\\Adj_t \\sim \\text{Bernoulli}(p_t),\n", "\\end{equation*}\n", "\n", "with $\\Adj_t=1$ meaning that the agent can rebalance and $\\NAdj_t=1$ ($\\Adj_t = 0$)\n", "forcing him to set $D_{i,t} = 0$. This friction is a parsimonious way to capture\n", "the fact that portfolio rebalancing is costly and households do it sporadically.\n", "Recent studies have advocated for \\cite{Giglio2021aer} and used\n", "\\cite{Luetticke2021aej_macro} this kind of rebalancing friction.\n", "\n", "To partially evade the possibility of being unable to rebalance their accounts, agents\n", "can use an income deduction scheme. By default, labor income ($Y_{i,t}$) is deposited to\n", "the risk-free liquid account at the start of every period. However, agents can pre-commit\n", "to have a fraction $\\Contr_t\\in[0,1]$ of their income diverted to their risky account instead.\n", "This fraction can be tweaked by the agent whenever $\\Adj_t = 1$; otherwise it stays at its\n", "previous value, $\\Contr_{t+1} = \\Contr_t$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Timing\n", "\n", "
\n", "\n", "
\n", "\n", "The previous figure summarizes the timing of stochastic shocks and\n", "optimizing decisions that occur within a period of the life cycle model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Recursive representation of the model\n", "\n", "Individual subscripts $i$ are dropped for simplicity. The value function for\n", "an agent who is not allowed to rebalance his portfolio at time $t$ is\n", "\n", "\\begin{equation*}\n", "\\begin{split}\n", "V^{\\NAdj}_{t}(M_t, N_t, P_t, \\Contr_t) = \\max_{C_t} u(C_t) \n", "+ p_{t+1} &\\beta\\delta_{t+1} E_t \\left[ V^{\\Adj}_{t+1}\\left( M_{t+1}, N_{t+1}, \n", "P_{t+1} \\right)\\right] +\\\\\n", "\\left(1-p_{t+1}\\right) &\\beta\\delta_{t+1} E_t\\left[V^{\\NAdj}_{t+1}\\left(M_{t+1}, \n", "N_{t+1}, P_{t+1}, \\Contr_{t+1}\\right) \\right]\\\\\n", "\\text{Subject to:} \\quad &\\\\\n", "0\\leq& C_t \\leq M_t \\\\\n", "A_t =& M_t - C_t \\\\\n", "M_{t+1} =& R A_t + (1-\\Contr_{t+1}) Y_{t+1}\\\\\n", "N_{t+1} =& \\tilde{R}_{t+1}N_t + \\Contr_{t+1}Y_{t+1}\\\\\n", "P_{t+1} =& \\Gamma_{t+1} \\psi_{t+1} P_{t}\\\\\n", "Y_{t+1} =& \\theta_{t+1} P_{t+1}\\\\\n", "\\Contr_{t+1} =& \\Contr_t\n", "\\end{split}\n", "\\end{equation*}\n", "\n", "and that of agent who is allowed to rebalance is\n", "\n", "\\begin{equation*}\n", "\\begin{split}\n", "V^{\\Adj}_{t}(M_t, N_t, P_t) = \\max_{C_t,D_t,\\Contr_{t+1}} \n", "u(C_t) + p_{t+1} &\\beta\\delta_{t+1} E_t \\left[ V^{\\Adj}_{t+1}\\left( M_{t+1}, \n", "N_{t+1}, P_{t+1} \\right)\\right] +\\\\\n", "\\left(1-p_{t+1}\\right) &\\beta\\delta_{t+1} E_t\\left[V^{\\NAdj}_{t+1}\\left(M_{t+1}, \n", "N_{t+1}, P_{t+1}, \\Contr_{t+1}\\right) \\right]\\\\\n", "\\text{Subject to:} \\quad &\\\\\n", "\\quad -N_t \\leq D_t \\leq M_t, \\quad \\Contr_{t+1} \\in& [0,1], \\quad 0 \\leq C_t \\leq \\tilde{M}_t\\\\\n", "\\hfill\\\\\n", "\\tilde{M}_t =& M_t - D_t\\left(1-1_{[D_t\\leq0]}\\tau\\right)\\\\\n", "\\tilde{N}_t =& N_t + D_t\\\\\n", "A_t =& \\tilde{M}_t - C_t \\\\\n", "M_{t+1} =& R A_t + (1-\\Contr_{t+1}) Y_{t+1}\\\\\n", "N_{t+1} =& \\tilde{R}_{t+1} \\tilde{N}_t + \\Contr_{t+1}Y_{t+1}\\\\\n", "P_{t+1} =& \\Gamma_{t+1}\\psi_{t+1} P_{t}\\\\\n", "Y_{t+1} =& \\theta_{t+1} P_{t+1}\n", "\\end{split}\n", "\\end{equation*}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Parametrizations" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 0 }, "source": [ "This notebook will only demonstrate a life-cycle calibration of the model. However, the current implementation of the model is able to find and use the solution to infinite-horizon formulations (see the document in this repository for details).\n", "\n", "For the present exercise, I calibrate the model's mortality and income paths to represent individuals who enter the model at age 25, retire exogenously at 65, and live to a maximum age of 90. Survival probabilities ($\\delta$) come from the 2004 SSA life-tables for males. Income growth factors and volatilities ($\\Gamma$, $\\sigma_\\psi$, $\\sigma_\\theta$) come from the calibration for high-school graduates in \\cite{Cagetti2003jbes}.\n", "\n", "To highlight the effect of different financial frictions on wealth accumulation and portfolio choice, I consider different configurations for the risky-withdrawal tax $\\tau$ and the probability of being able to rebalance $p$:\n", "\n", "\\begin{itemize}\n", "\\item \\textbf{Base}: the probability of being able to rebalance is $p = 1$\n", "and the risky withdrawal tax rate is $\\tau = 0$, both constant throughout the agents' lives.\n", "\n", "\\item \\textbf{Tax}: the risky withdrawal tax is constant at $10\\%$ and the agents\n", "can always rebalance their portfolios.\n", "\n", "\\item \\textbf{Calvo}: there is no risky withdrawal tax, but there is only a $25\\%$ chance\n", "that agents can rebalance their portfolios every year.\n", "\n", "\\item \\textbf{Retirement}: there is no risky withdrawal tax, but the agents' ability\n", "to rebalance their portfolio is time-varying; they can rebalance their assets and pick\n", "their income-deduction scheme for certain when they enter the model at age 25, but\n", "then they have no chance of doing so again ($p=0$) until they retire. After retirement\n", "at age 65, they can always rebalance their portfolio ($p=1$).\n", "\\end{itemize}\n", "\n", "The rest of the parameters take the following values\n", "\n", "| Name in HARK | Mathematical Symbol | Value |\n", "|--------------|-----------------------|---------|\n", "| `CRRA` | $\\rho$ | $5.0$ |\n", "| `Rfree` | $R$ | $1.03$ |\n", "| `DiscFac` | $\\beta$ | $0.9$ |\n", "| `UnempPrb` | $\\mho$ | $0.05$ |\n", "| `IncUnemp` | $\\mathcal{U}$ | $0.3$ |\n", "| `RiskyAvg` | $E[\\tilde{R}]$ | $1.08$ |\n", "| `RiskyStd` | $\\sqrt{V[\\tilde{R}]}$ | $0.18$ |\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "code_folding": [ 0 ], "title": "Base parametrization" }, "outputs": [], "source": [ "# Base calibration setup\n", "\n", "# Make the problem life-cycle\n", "par_LC_base = init_risky_contrib_lifecycle.copy()\n", "\n", "# Turn off aggregate growth\n", "par_LC_base['PermGroFacAgg'] = 1.0\n", "\n", "# and frictionless to start\n", "par_LC_base[\"AdjustPrb\"] = 1.0\n", "par_LC_base[\"tau\"] = 0.0\n", "\n", "# Make contribution shares a continuous choice\n", "par_LC_base[\"DiscreteShareBool\"] = False\n", "par_LC_base[\"vFuncBool\"] = False\n", "\n", "# Make grids go up to higher levels of resources\n", "# (one of the calibration produces high levels of nNrm)\n", "par_LC_base.update({\"mNrmMax\": 500, \"nNrmMax\":500})" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "code_folding": [ 0 ], "title": "A version with the tax" }, "outputs": [], "source": [ "# Alternative calibrations\n", "\n", "# Tax\n", "par_LC_tax = copy(par_LC_base)\n", "par_LC_tax[\"tau\"] = 0.1\n", "\n", "# Calvo\n", "par_LC_calvo = copy(par_LC_base)\n", "par_LC_calvo[\"AdjustPrb\"] = 0.25\n", "\n", "# Retirement\n", "par_LC_retirement = copy(par_LC_base)\n", "par_LC_retirement[\"AdjustPrb\"] = [1.0] + [0.0]*39 + [1.0]*25\n", "par_LC_retirement[\"tau\"] = [0.0]*41 + [0.0]*24\n", "par_LC_retirement[\"UnempPrb\"] = 0.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution\n", "\n", "With the parametrizations created, I now create and solve the agents." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "lines_to_next_cell": 2, "title": "Create and solve agents with all the parametrizations" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Now solving Base\n", "Finished cycle #1 of 1 in 130.2132887840271 seconds.\n", "Solving Base took 130.2132887840271 seconds.\n", "Now solving Tax\n", "Finished cycle #1 of 1 in 143.99821996688843 seconds.\n", "Solving Tax took 143.99922680854797 seconds.\n", "Now solving Calvo\n", "Finished cycle #1 of 1 in 376.7379426956177 seconds.\n", "Solving Calvo took 376.7379426956177 seconds.\n", "Now solving Retirement\n", "Finished cycle #1 of 1 in 234.79594135284424 seconds.\n", "Solving Retirement took 234.79594135284424 seconds.\n" ] } ], "source": [ "agents = {\n", " \"Base\": RiskyContribConsumerType(**par_LC_base),\n", " \"Tax\": RiskyContribConsumerType(**par_LC_tax),\n", " \"Calvo\": RiskyContribConsumerType(**par_LC_calvo),\n", " \"Retirement\": RiskyContribConsumerType(**par_LC_retirement),\n", "}\n", "\n", "for agent in agents:\n", "\n", " print(\"Now solving \" + agent)\n", " t0 = time()\n", " agents[agent].solve(verbose=True)\n", " t1 = time()\n", " print(\"Solving \" + agent + \" took \" + str(t1 - t0) + \" seconds.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Policy function inspection\n", "\n", "Once agents have been solved, we can use their policy functions $\\dFrac_t(m,n)$, $\\Contr_t(\\tilde{m},\\tilde{n})$, and $c_t(\\tilde{m},\\tilde{n}, \\Contr)$ for any period $t$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.13150902906720252\n", "0.0\n", "0.7116885471009962\n" ] } ], "source": [ "# Example\n", "calib = \"Base\"\n", "t = 0\n", "print(agents[calib].solution[t].stage_sols[\"Reb\"].dfracFunc_Adj(1,1))\n", "print(agents[calib].solution[t].stage_sols[\"Sha\"].ShareFunc_Adj(1.2,0.8))\n", "print(agents[calib].solution[t].stage_sols[\"Cns\"].cFunc(1.2,0.8,0.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I now illustrate the policy functions of different calibrations for an arbitrary period graphically.\n", "\n", "Note that the solution algorithm represents the three simultaneous decisions that an agent can take as happening sequentially in ''stages'', in the order `Rebalancing` -> `Income deduction share` -> `Consumption`. See the document in this repository for more details." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "code_folding": [ 0 ] }, "outputs": [], "source": [ "# Setup\n", "from HARK.utilities import (\n", " determine_platform,\n", " test_latex_installation,\n", " setup_latex_env_notebook,\n", ")\n", "\n", "pf = determine_platform()\n", "try:\n", " latexExists = test_latex_installation(pf)\n", "except ImportError: # windows and MacOS requires manual install\n", " latexExists = False\n", "\n", "setup_latex_env_notebook(pf, latexExists)\n", "\n", "# General aesthetics\n", "sns.set_style(\"whitegrid\")\n", "sns.set_context(\"paper\", font_scale=1.5, rc={\"lines.linewidth\": 2.5})\n", "\n", "# Parameters to feed to policy functions\n", "t = 10\n", "age = t + 24\n", "mNrmGrid = np.linspace(0, 40, 100)\n", "nNrm_vals = np.array([0.0, 20.0, 40])\n", "Share_vals = np.array([0.0, 0.5])\n", "\n", "# Evaluate policy functions\n", "polfuncs = pol_funcs_dframe(agents, t, mNrmGrid, nNrm_vals, Share_vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rebalancing\n", "\n", "The solution to this stage problem will be the policy function $d_t(\\cdot, \\cdot)$\n", "that gives the optimal flow from risk-free to risky assets, which can be negative.\n", "However, it is convenient to define a normalized policy function $\\dFrac_t$ as\n", "\\begin{equation*}\n", "\\dFrac_t(m, n) = \\begin{cases}\n", "d_t(m,n)/m, & \\text{ if } d_t(m,n) \\geq 0 \\\\\n", "d_t(m,n)/n, & \\text{ if } d_t(m,n) < 0\n", "\\end{cases}\n", "\\end{equation*}\n", "so that $-1 \\leq \\dFrac(m,n) \\leq 1$ for all $(m,n)$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "code_folding": [ 0 ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Rebalancing fraction\n", "polfuncs[\"$n$\"] = polfuncs[\"n\"]\n", "g = sns.FacetGrid(\n", " polfuncs[polfuncs.control == \"dfrac\"],\n", " col=\"$n$\",\n", " hue=\"model\",\n", " height=3,\n", " aspect=(7 / 3) * 1 / 3,\n", ")\n", "g.map(sns.lineplot, \"m\", \"value\", alpha=0.7)\n", "g.add_legend(bbox_to_anchor=[0.5, 0.0], ncol=4, title=\"\")\n", "g.set_axis_labels(\"$m$\", \"Rebalancing fraction: $\\dFrac$\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Income deduction share" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "code_folding": [ 0 ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# After rebalancing, m and n turn to their \"tilde\" versions. Create ntilde\n", "# just for seaborn's grid labels.\n", "polfuncs[\"$\\\\tilde{n}$\"] = polfuncs[\"n\"]\n", "polfuncs[\"$\\\\Contr$\"] = polfuncs[\"Share\"]\n", "\n", "# Share fraction\n", "g = sns.FacetGrid(\n", " polfuncs[polfuncs.control == \"Share\"],\n", " col=\"$\\\\tilde{n}$\",\n", " hue=\"model\",\n", " height=3,\n", " aspect=(7 / 3) * 1 / 3,\n", ")\n", "g.map(sns.lineplot, \"m\", \"value\", alpha=0.7)\n", "g.add_legend(bbox_to_anchor=[0.5, 0.0], ncol=4, title=\"\")\n", "g.set_axis_labels(\"$\\\\tilde{m}$\", r\"Deduction Share: $\\Contr$\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Consumption" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "code_folding": [ 0 ] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Consumption\n", "g = sns.FacetGrid(\n", " polfuncs[polfuncs.control == \"c\"],\n", " col=\"$\\\\tilde{n}$\",\n", " row=\"$\\\\Contr$\",\n", " hue=\"model\",\n", " height=3,\n", " aspect=(7 / 3) * 1 / 3,\n", ")\n", "g.map(sns.lineplot, \"m\", \"value\", alpha=0.7)\n", "g.add_legend(bbox_to_anchor=[0.5, 0.0], ncol=4, title=\"\")\n", "g.set_axis_labels(\"$\\\\tilde{m}$\", \"Consumption: $c$\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation and average life-cycle profiles" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 0 }, "source": [ "With the policy functions, it is easy to simulate populations of agents. I now simulate many agents for every calibration to obtain the average lifetime profiles of relevant variables like consumption, income, and wealth in its different components." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "code_folding": [ 0 ], "title": "Solve and simulate" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\GitHub\\HARK\\HARK\\core.py:865: RuntimeWarning: invalid value encountered in add\n", " self.history[var_name] = np.empty((self.T_sim, self.AgentCount)) + np.nan\n" ] } ], "source": [ "# Simulation\n", "\n", "n_agents = 100\n", "t_sim = 500\n", "profiles = []\n", "for agent in agents:\n", " agents[agent].AgentCount = n_agents\n", " agents[agent].T_sim = t_sim\n", " agents[agent].track_vars = ['pLvl','t_age','Adjust',\n", " 'mNrm','nNrm','mNrmTilde','nNrmTilde','aNrm',\n", " 'cNrm', 'Share', 'dfrac']\n", " agents[agent].initialize_sim()\n", " agents[agent].simulate()\n", " profile = age_profiles(agents[agent])\n", " profile['Model'] = agent\n", " profiles.append(profile)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "code_folding": [ 0 ], "title": "Plot life-cycle means" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot\n", "\n", "simdata = pd.concat(profiles)\n", "\n", "# Latex names\n", "simdata = simdata.rename(columns = {'pLvl': 'Perm. Income $P$',\n", " 'Mtilde': 'Risk-free Assets $\\\\tilde{M}$',\n", " 'Ntilde': 'Risky Assets $\\\\tilde{N}$',\n", " 'C': 'Consumption $C$',\n", " 'StockShare': 'Risky Share of Savings',\n", " 'Share': 'Deduct. Share $\\\\Contr$'})\n", "\n", "lc_means = pd.melt(simdata,\n", " id_vars = ['t_age', 'Model'],\n", " value_vars = ['Perm. Income $P$',\n", " 'Risk-free Assets $\\\\tilde{M}$',\n", " 'Risky Assets $\\\\tilde{N}$',\n", " 'Consumption $C$',\n", " 'Risky Share of Savings','Deduct. Share $\\\\Contr$'])\n", "\n", "lc_means['Age'] = lc_means['t_age'] + 24\n", "\n", "# Drop the last year, as people's behavior is substantially different.\n", "lc_means = lc_means[lc_means['Age']Carroll, 1997) Carroll Christopher D., ``_Buffer-Stock Saving and the Life Cycle/Permanent Income Hypothesis*_'', The Quarterly Journal of Economics, vol. 112, number 1, pp. 1-55, 02 1997. [online](https://doi.org/10.1162/003355397555109)\n", "\n", "(Cagetti, 2003) Cagetti Marco, ``_Wealth Accumulation Over the Life Cycle and Precautionary Savings_'', Journal of Business \\& Economic Statistics, vol. 21, number 3, pp. 339-353, 2003. [online](https://doi.org/10.1198/073500103288619007\n", "\n", ")\n", "\n", "(Cocco, Gomes et al., 2005) Cocco Jo\\~ao F., Gomes Francisco J. and Maenhout Pascal J., ``_Consumption and Portfolio Choice over the Life Cycle_'', The Review of Financial Studies, vol. 18, number 2, pp. 491-533, 02 2005. [online](https://doi.org/10.1093/rfs/hhi017)\n", "\n", "(Fagereng, Gottlieb et al., 2017) Fagereng Andreas, Gottlieb Charles and Guiso Luigi, ``_Asset Market Participation and Portfolio Choice over the\n", "\tLife-Cycle_'', The Journal of Finance, vol. 72, number 2, pp. 705-750, 2017. [online](https://onlinelibrary.wiley.com/doi/abs/10.1111/jofi.12484)\n", "\n", "(Carroll, 1992) Carroll Christopher D., ``_The Buffer-Stock Theory of Saving: Some Macroeconomic Evidence_'', Brookings Papers on Economic Activity, vol. 1992, number 2, pp. 61--156, 1992. [online](http://www.jstor.org/stable/2534582)\n", "\n", "(Carroll and Samwick, 1997) Carroll Christopher D. and Samwick Andrew A., ``_The nature of precautionary wealth_'', Journal of Monetary Economics, vol. 40, number 1, pp. 41-71, 1997. [online](https://www.sciencedirect.com/science/article/pii/S0304393297000366)\n", "\n", "(Sabelhaus and Song, 2010) Sabelhaus John and Song Jae, ``_The great moderation in micro labor earnings_'', Journal of Monetary Economics, vol. 57, number 4, pp. 391-403, 2010. [online](https://www.sciencedirect.com/science/article/pii/S0304393210000358)\n", "\n", "(Giglio, Maggiori et al., 2021) Giglio Stefano, Maggiori Matteo, Stroebel Johannes et al., ``_Five Facts about Beliefs and Portfolios_'', American Economic Review, vol. 111, number 5, pp. 1481-1522, May 2021. [online](https://www.aeaweb.org/articles?id=10.1257/aer.20200243)\n", "\n", "(Luetticke, 2021) Luetticke Ralph, ``_Transmission of Monetary Policy with Heterogeneity in Household Portfolios_'', American Economic Journal: Macroeconomics, vol. 13, number 2, pp. 1-25, April 2021. [online](https://www.aeaweb.org/articles?id=10.1257/mac.20190064)\n", "\n" ] } ], "metadata": { "jupytext": { "encoding": "# -*- coding: utf-8 -*-", "formats": "ipynb,py:percent", "notebook_metadata_filter": "all" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": false, "bibliofile": "RiskyContrib-Add.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": true, "report_style_numbering": false, "user_envs_cfg": false } }, "nbformat": 4, "nbformat_minor": 4 }