{ "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\":1000})" ] }, { "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 37.66172480583191 seconds.\n", "Solving Base took 37.66172480583191 seconds.\n", "Now solving Tax\n", "Finished cycle #1 of 1 in 36.05218529701233 seconds.\n", "Solving Tax took 36.05218529701233 seconds.\n", "Now solving Calvo\n", "Finished cycle #1 of 1 in 84.95266103744507 seconds.\n", "Solving Calvo took 84.95266103744507 seconds.\n", "Now solving Retirement\n", "Finished cycle #1 of 1 in 53.0900604724884 seconds.\n", "Solving Retirement took 53.0900604724884 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": [ "0.1280581440929615\n", "0.0\n", "0.7103217030091933\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", "text/plain": [ "
" ] }, "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", 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" ] }, "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": [], "source": [ "# Simulation\n", "\n", "n_agents = 100\n", "t_sim = 200\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", "text/plain": [ "
" ] }, "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 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "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 }