cstwMPC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Distribution of Wealth and the MPC\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook presents a selection of results from the paper [The Distribution of Wealth and the Marginal Propensity to Consume](http://econ.jhu.edu/people/ccarroll/papers/cstwMPC), using the [Econ-ARK/HARK](https://github.com/econ-ark/HARK) toolkit. It sketches the steps that would need to be taken to solve the model using the [dolARK](https://github.com/EconForge/dolARK) modeling system.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": [],
    "execution": {
     "iopub.execute_input": "2023-04-06T15:42:34.138231Z",
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     "shell.execute_reply": "2023-04-06T15:42:35.764534Z"
    }
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   "outputs": [],
   "source": [
    "# This cell does some standard python setup!\n",
    "\n",
    "import code.calibration as parameters\n",
    "import warnings  # The warnings package allows us to ignore some harmless but alarming warning messages\n",
    "from code.calibration import SCF_wealth, SCF_weights\n",
    "from code.estimation import estimate\n",
    "\n",
    "# Import related generic python packages\n",
    "import matplotlib.pyplot as plt  # Plotting tools\n",
    "import numpy as np\n",
    "from HARK.utilities import get_lorenz_shares\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "def mystr(number):\n",
    "    return f\"{number:.4f}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstract\n",
    "\n",
    "In a model calibrated to match micro- and macroeconomic evidence on household income dynamics, this paper shows that a modest degree of heterogeneity in household preferences or beliefs is sufficient to match empirical measures of wealth inequality in the U.S. The hegerogeneity-augmented model's predictions are consistent with microeconomic evidence that suggests that the annual marginal propensity to consume (MPC) is much larger than the roughly 0.04 implied by commonly-used macroeconomic models (even ones including some heterogeneity). The high MPC arises because many consumers hold little wealth despite having a strong precautionary motive. The model also plausibly predicts that the aggregate MPC can differ greatly depending on how the shock is distributed across households (depending, e.g., on their wealth, or employment status).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notation For the Core Model\n",
    "\n",
    "We define the following notation.\n",
    "\n",
    "|    Exogenous Random Variable     | Description       | Code            | Value |\n",
    "| :------------------------------: | ----------------- | --------------- | :---: |\n",
    "| $\\newcommand{\\tshk}{\\zeta}\\tshk$ | Transitory Income | $\\texttt{tshk}$ |       |\n",
    "| $\\newcommand{\\pshk}{\\psi}\\pshk$  | Permanent Shock   | $\\texttt{pshk}$ |       |\n",
    "\n",
    "|                Parameter                 | Description                          | Code                  |  Value  |\n",
    "| :--------------------------------------: | ------------------------------------ | --------------------- | :-----: |\n",
    "| $\\newcommand{\\PLives}{\\Lambda} \\PLives$  | Probability of living                | $\\texttt{PLives}$     | 0.99375 |\n",
    "| $\\newcommand{\\Discount}{\\beta}\\Discount$ | Time Preference Factor               | $\\texttt{Discount}$   |  0.96   |\n",
    "|     $\\newcommand{\\CRRA}{\\rho}\\CRRA$      | Coeﬃcient of Relative Risk Aversion  | $\\texttt{CRRA}$       |    1    |\n",
    "|              $\\sigma_\\tshk$              | Transitory Income Standard Deviation | $\\texttt{PermShkStd}$ |   0.1   |\n",
    "|              $\\sigma_\\pshk$              | Permanent Shock Standard Deviation   | $\\texttt{TranShkStd}$ |   0.1   |\n",
    "\n",
    "|                 Variable                  | Description         | Code                |\n",
    "| :---------------------------------------: | ------------------- | ------------------- |\n",
    "|       $\\newcommand{\\aRat}{a}\\aRat$        | Assets              | $\\texttt{aRat}$     |\n",
    "|       $\\newcommand{\\mRat}{m}\\mRat$        | Market resources    | $\\texttt{mRt}$      |\n",
    "|       $\\newcommand{\\KLev}{K}\\KLev$        | Capital Aggregate   | $\\texttt{KLev}$     |\n",
    "| $\\newcommand{\\kapShare}{\\alpha}\\kapShare$ | Capital share       | $\\texttt{kapShare}$ |\n",
    "|       $\\newcommand{\\LLev}{L}\\LLev$        | Labor Aggregate     | $\\texttt{LLev}$     |\n",
    "|     $\\newcommand{\\labor}{\\ell}\\labor$     | Labor share         | $\\texttt{labor}$    |\n",
    "|       $\\newcommand{\\kRat}{k}\\kRat$        | $K/P$               | $\\texttt{kRat}$     |\n",
    "|       $\\newcommand{\\pRat}{p}\\pRat$        | Permanent Income    |                     |\n",
    "|               $\\mathbf{P}$                |                     |                     |\n",
    "|      $\\newcommand{\\rProd}{r}\\rProd$       | Interest rate       | $\\texttt{rProd}$    |\n",
    "|       $\\newcommand{\\yLev}{y} \\yLev$       | Income              | $\\texttt{yLev}$     |\n",
    "|       $\\newcommand{\\Wage}{W}\\Wage$        | Aggregate Wage Rate | $\\texttt{Wage}$     |\n",
    "\n",
    "|                 Functions                 | Description | Code             | Value |\n",
    "| :---------------------------------------: | ----------- | ---------------- | :---: |\n",
    "|  $\\newcommand{\\cFunc}{\\mathrm{c}}\\cFunc$  | Consumption | $\\texttt{cFunc}$ |       |\n",
    "| $\\newcommand{\\valfn}{\\mathrm{v}} \\valfn$  | Value       | $\\texttt{valfn}$ |\n",
    "| $\\newcommand{\\uFunc}{{\\mathrm{u}}}\\uFunc$ | Utility     | $\\texttt{uFunc}$ |       |\n",
    "\n",
    "$\\newcommand{\\cRat}{c}$\n",
    "$\\newcommand{\\Ex}{\\mathbb{E}}$\n",
    "$\\newcommand{\\PDies}{\\mathsf{P}}$\n",
    "$\\newcommand{\\ptyLev}{a}$\n",
    "$\\newcommand{\\YLev}{Y}$\n",
    "$\\newcommand{\\wEndRat}{\\aRat}$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The consumer has a standard Constant Relative Risk Aversion utility function $$u(c)=\\frac{c^{1-\\rho}}{1-\\rho}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idiosyncratic (household) income process is logarithmic Friedman:\n",
    "\\begin{align*}\n",
    "\\yLev_{t+1} & = \\pRat_{t+1}\\tshk_{t+1}\\Wage\\\\\n",
    "\\pRat_{t+1} & = \\pRat_{t}\\pshk_{t+1}\n",
    "\\end{align*}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Bellman form of the value function for households is:\n",
    "\n",
    "\\begin{align*}\n",
    "\\valfn(\\mRat_{t}) & = \\underset{\\cFunc_{t}}{\\max } ~~ \\uFunc(\\cFunc_{t}(\\mRat_{t}))+\\Discount \\PLives \\Ex_{t}\\left[ \\pshk_{t+1}^{1-\\CRRA}\\valfn(\\mRat_{t+1})\n",
    "\\right] \\\\\n",
    "\\notag & \\text{s.t.}\\\\\n",
    "\\wEndRat_{t} & = \\mRat_{t}-\\cRat_{t},\\\\\n",
    "\\wEndRat_{t} & \\geq0, \\\\\n",
    "\\kRat_{t+1} & = \\wEndRat_{t}/(\\PLives \\pshk_{t+1}),\n",
    "\\\\\n",
    "\\mRat_{t+1} & = (\\daleth +\\rProd_{t})\\kRat_{t+1}+\\tshk_{t+1},\\\\\n",
    "\\rProd & = \\kapShare\\ptyLev(\\KLev/\\labor\\LLev)^{\\kapShare-1}\\\\\n",
    "\\end{align*}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T15:42:35.767537Z",
     "iopub.status.busy": "2023-04-06T15:42:35.767537Z",
     "iopub.status.idle": "2023-04-06T15:42:35.780539Z",
     "shell.execute_reply": "2023-04-06T15:42:35.780539Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nCopied here from do_min.py.\\nDesign decisions about whether to include this code explicitly,\\nor import it, or execute it as is here, TBD.\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "This will run the absolute minimum amount of work that actually produces\n",
    "relevant output-- no aggregate shocks, perpetual youth, matching net worth.\n",
    "Will run both beta-point and beta-dist versions.\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "Copied here from do_min.py.\n",
    "Design decisions about whether to include this code explicitly,\n",
    "or import it, or execute it as is here, TBD.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T15:42:35.782541Z",
     "iopub.status.busy": "2023-04-06T15:42:35.782541Z",
     "iopub.status.idle": "2023-04-06T15:42:35.796047Z",
     "shell.execute_reply": "2023-04-06T15:42:35.796047Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# For speed here, use the \"tractable\" version of the model\n",
    "# This is not the \"right\" model, but illustrates the key point\n",
    "\"\"\"\n",
    "This options file specifies parameter heterogeneity, making the choice in the paper:\n",
    "uniformly distributed discount factors.\n",
    "\"\"\"\n",
    "param_name = \"DiscFac\"  # Which parameter to introduce heterogeneity in\n",
    "dist_type = \"uniform\"  # Which type of distribution to use\n",
    "\n",
    "\"\"\"\n",
    "This options file specifies the \"standard\" work options for cstwMPC, estimating the model only.\n",
    "\"\"\"\n",
    "run_estimation = True  # Runs the estimation if True\n",
    "# Choose which sensitivity analyses to run: rho, xi_sigma, psi_sigma, mu, urate, mortality, g, R\n",
    "run_sensitivity = [False, False, False, False, False, False, False, False]\n",
    "# Computes K/Y ratio for a wide range of beta; should have do_beta_dist = False\n",
    "find_beta_vs_KY = False\n",
    "# Uses a \"tractable consumer\" rather than solving full model when True\n",
    "do_tractable = True\n",
    "\n",
    "# Solve for the $\\beta-Point$ (do_param_dist=False) for speed\n",
    "\"\"\"\n",
    "This options file establishes the second simplest model specification possible:\n",
    "with heterogeneity, no aggregate shocks, perpetual youth model, matching net worth.\n",
    "\"\"\"\n",
    "\n",
    "do_param_dist = False  # Do param-dist version if True, param-point if False\n",
    "do_lifecycle = False  # Use lifecycle model if True, perpetual youth if False\n",
    "do_agg_shocks = False  # Solve the FBS aggregate shocks version of the model\n",
    "# Matches liquid assets data when True, net worth data when False\n",
    "do_liquid = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T15:42:35.798050Z",
     "iopub.status.busy": "2023-04-06T15:42:35.798050Z",
     "iopub.status.idle": "2023-04-06T15:43:51.962677Z",
     "shell.execute_reply": "2023-04-06T15:43:51.962677Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning an estimation with the specification name PYUnifbetaPointIndNetWorth...\n",
      "get_KY_ratio_difference tried center = 0.95 and got -2.980471591088475\n",
      "get_KY_ratio_difference tried center = 0.995 and got 1.997155517846061\n",
      "get_KY_ratio_difference tried center = 0.9769448109839811 and got -2.3081130220958577\n",
      "get_KY_ratio_difference tried center = 0.9866244433919603 and got -1.216028776433893\n",
      "get_KY_ratio_difference tried center = 0.9897941706535301 and got 0.303456962113164\n",
      "get_KY_ratio_difference tried center = 0.9891611434515486 and got 0.024231098773936477\n",
      "get_KY_ratio_difference tried center = 0.989107374037012 and got -0.0010138086477762265\n",
      "get_KY_ratio_difference tried center = 0.9891095333595451 and got 4.311107538068626e-06\n",
      "get_KY_ratio_difference tried center = 0.9891090333595447 and got -0.00023140282526235012\n",
      "Estimate is center=0.9891095333595451, spread=0.0, took 44.62666606903076 seconds.\n",
      "Running in interactive shell (Jupyter notebook or spyder)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate is center=0.9891095333595451, spread=0.0\n",
      "Lorenz distance is 42.02894388233986\n",
      "Average MPC for all consumers is 0.099\n",
      "Average MPC in the top percentile of W/Y is 0.069\n",
      "Average MPC in the top decile of W/Y is 0.071\n",
      "Average MPC in the top quintile of W/Y is 0.072\n",
      "Average MPC in the second quintile of W/Y is 0.074\n",
      "Average MPC in the middle quintile of W/Y is 0.074\n",
      "Average MPC in the fourth quintile of W/Y is 0.076\n",
      "Average MPC in the bottom quintile of W/Y is 0.197\n",
      "Average MPC in the top percentile of y is 0.075\n",
      "Average MPC in the top decile of y is 0.078\n",
      "Average MPC in the top quintile of y is 0.084\n",
      "Average MPC in the second quintile of y is 0.117\n",
      "Average MPC in the middle quintile of y is 0.118\n",
      "Average MPC in the fourth quintile of y is 0.082\n",
      "Average MPC in the bottom quintile of y is 0.093\n",
      "Average MPC for the employed is 0.096\n",
      "Average MPC for the unemployed is 0.130\n",
      "Average MPC for the retired is nan\n",
      "Of the population with the 1/3 highest MPCs...\n",
      "65.397% are in the bottom wealth quintile,\n",
      "26.121% are in the second wealth quintile,\n",
      "5.981% are in the third wealth quintile,\n",
      "1.831% are in the fourth wealth quintile,\n",
      "and 0.670% are in the top wealth quintile.\n",
      "\n",
      "These results have been saved to ./code/results/PYUnifbetaPointIndNetWorth.txt\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "options = {\n",
    "    \"param_name\": param_name,\n",
    "    \"dist_type\": dist_type,\n",
    "    \"run_estimation\": run_estimation,\n",
    "    \"run_sensitivity\": run_sensitivity,\n",
    "    \"find_beta_vs_KY\": find_beta_vs_KY,\n",
    "    \"do_tractable\": do_tractable,\n",
    "    \"do_param_dist\": do_param_dist,\n",
    "    \"do_lifecycle\": do_lifecycle,\n",
    "    \"do_agg_shocks\": do_agg_shocks,\n",
    "    \"do_liquid\": do_liquid,\n",
    "}\n",
    "\n",
    "\n",
    "EstimationEconomy = estimate(options, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "β=0.98911 : intertemporal discount factor (DiscFac)\n",
      "R=1.01635 : risk free interest factor (Rfree)\n",
      "G=1.00000 : permanent income growth factor (PermGroFac)\n",
      "ρ=1.01000 : coefficient of relative risk aversion (CRRA)\n",
      "ℒ=0.99375 : survival probability (LivPrb)\n",
      "Þ=(βℒR)^(1/ρ)=0.99901 : absolute patience factor (APFac)\n",
      "℘=0.01400 : probability of worst income shock realization (WorstPrb)\n",
      "\n",
      "APFac=0.99901 : The Absolute Patience Factor satisfies the Absolute Impatience Condition (AIC) Þ < 1.\n",
      "RPFac=0.98294 : The Return Patience Factor satisfies the Return Impatience Condition (RIC) Þ/R < 1.\n",
      "WRPFac=0.01436 : The Weak Return Patience Factor satisfies the Weak Return Impatience Condition (WRIC) ℘ Þ/R < 1.\n",
      "GPFacRaw=0.99901 : The Growth Patience Factor satisfies the Growth Impatience Condition (GICRaw) Þ/G < 1.\n",
      "GPFacMod=1.00227 : The Risk-Modified Growth Patience Factor violates the Risk-Modified Growth Impatience Condition (GICMod) Þ/(G‖Ψ‖_(-1)) < 1.\n",
      "GPFacLiv=0.99277 : The Mortality-Adjusted Growth Patience Factor satisfies the Mortality-Adjusted Growth Impatience Condition (GICLiv) ℒÞ/G < 1.\n",
      "E[log Ψ]=-0.00163 : The expected log permanent income shock violates the Szeidl Growth Impatience Condition (GICSdl) log(Þ/G) < E[log Ψ].\n",
      "GPFacHrm=0.99738 : The Harmenberg Expected Growth Patience Factor satisfies the Harmenberg Growth Normalized Impatience Condition (GICHrm) Þ/G < exp(E[Ψlog Ψ]).\n",
      "PFVAFac=0.98293 : The Finite Value of Autarky Factor satisfies the Finite Value of Autarky Condition βG^(1-ρ) < 1.\n",
      "VAFac=0.98913 : The Risk-Modified Finite Value of Autarky Factor satisfies the Risk-Modified Finite Value of Autarky Condition β(G‖Ψ‖_(1-ρ))^(1-ρ) < 1.\n",
      "FHWFac=0.98391 : The Finite Human Wealth Factor satisfies the Finite Human Wealth Condition (FHWC) G/R < 1.\n",
      "\n",
      "Because both the WRIC and FVAC are satisfied, the recursive solution to the infinite horizon problem represents a contraction mapping on the consumption function. Thus a non-degenerate solution exists.\n",
      "\n",
      "Because both the RIC and FHWC condition are satisfied, the consumption function will approach the linear perfect foresight solution as m becomes arbitrarily large.\n",
      "\n",
      "Because the GICRaw is satisfied, there exists a pseudo-steady-state wealth ratio at which the level of wealth is expected to grow at the same rate as permanent income.\n",
      "\n",
      "Because the GICMod is violated, expected growth of the ratio of market resources to permanent income exceeds one as market resources go to infinity. Hence the consumer might not have a target ratio of market resources to permanent income.\n",
      "\n",
      "Because the GICLiv is satisfied, a target ratio of aggregate market resources to aggregate permanent income exists.\n",
      "\n",
      "Because the GICSdl is violated, there do not exist invariant distributions of permanent income-normalized variables.\n",
      "\n",
      "Because the GICHrm is satisfied, there exists a target ratio of the individual market resources to permanent income, under the permanent-income-neutral measure.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "MyGuys = EstimationEconomy.agents[0]\n",
    "MyGuys.check_conditions()\n",
    "print(MyGuys.bilt[\"conditions_report\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T15:43:51.964678Z",
     "iopub.status.busy": "2023-04-06T15:43:51.964678Z",
     "iopub.status.idle": "2023-04-06T15:43:52.105210Z",
     "shell.execute_reply": "2023-04-06T15:43:52.105210Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Construct the Lorenz curves and plot them\n",
    "\n",
    "pctiles = np.linspace(0.001, 0.999, 15)\n",
    "SCF_Lorenz_points = get_lorenz_shares(\n",
    "    SCF_wealth, weights=SCF_weights, percentiles=pctiles\n",
    ")\n",
    "\n",
    "sim_wealth = EstimationEconomy.reap_state[\"aLvl\"][0]\n",
    "sim_Lorenz_points = get_lorenz_shares(sim_wealth, percentiles=pctiles)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.title(\"Wealth Distribution\")\n",
    "plt.plot(pctiles, SCF_Lorenz_points, \"--k\", label=\"SCF\")\n",
    "plt.plot(pctiles, sim_Lorenz_points, \"-b\", label=\"Beta-Point\")\n",
    "plt.plot(pctiles, pctiles, \"g-.\", label=\"45 Degree\")\n",
    "plt.xlabel(\"Percentile of net worth\")\n",
    "plt.ylabel(\"Cumulative share of wealth\")\n",
    "plt.legend(loc=2)\n",
    "plt.ylim([0, 1])\n",
    "plt.show(\"wealth_distribution_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(sim_wealth.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<code.agents.DoWAgent object at 0x14a653b5ca0.\n",
      "Parameters:\n",
      "cycles: 0\n",
      "CRRA: 1.01\n",
      "Rfree: 1.0163522012578616\n",
      "DiscFac: 0.9891095333595451\n",
      "LivPrb: [0.99375]\n",
      "PermGroFac: [1.0]\n",
      "BoroCnstArt: 0.0\n",
      "MaxKinks: 400\n",
      "AgentCount: 10000\n",
      "aNrmInitMean: -11.512925464970229\n",
      "aNrmInitStd: 0.0\n",
      "pLvlInitMean: 0.0\n",
      "pLvlInitStd: 0.0\n",
      "PermGroFacAgg: 1.0\n",
      "T_age: 400\n",
      "T_cycle: 1\n",
      "PerfMITShk: False\n",
      "aXtraMin: 1e-05\n",
      "aXtraMax: 20\n",
      "aXtraNestFac: 3\n",
      "aXtraCount: 20\n",
      "aXtraExtra: [None]\n",
      "PermShkStd: [0.06030226891555272]\n",
      "PermShkCount: 5\n",
      "TranShkStd: [0.2]\n",
      "TranShkCount: 5\n",
      "UnempPrb: 0.07\n",
      "UnempPrbRet: None\n",
      "IncUnemp: 0.15\n",
      "IncUnempRet: None\n",
      "tax_rate: 0.0\n",
      "T_retire: 0\n",
      "vFuncBool: False\n",
      "CubicBool: False\n",
      "neutral_measure: False\n",
      "NewbornTransShk: False\n",
      "PopGroFac: 1.0\n",
      "T_sim: 1200\n",
      "IndL: 1.1111111111111112>]\n"
     ]
    }
   ],
   "source": [
    "print(EstimationEconomy.agents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Preference Heterogeneneity\n",
    "\n",
    "Our specific approach is to replace the assumption that all households have the same time\n",
    "preference factor with an assumption that, for some dispersion $\\nabla$, time\n",
    "preference factors are distributed uniformly in the population between\n",
    "$\\grave{\\Discount}-\\nabla$ and $\\grave{\\Discount}+\\nabla$ (for this reason, the model is referred to as the $\\Discount$-Dist model). Then,\n",
    "using simulations, we search for the values of $\\grave{\\Discount}$ and\n",
    "$\\nabla$ for which the model best matches the fraction of net worth held by the top $20$, $40$, $60$, and $80$ percent of the population, while at the same time matching\n",
    "the aggregate capital-to-output ratio from the perfect foresight\n",
    "model. Specifically, defining $w_{i}$ and $\\omega _{i}$ as the proportion of total aggregate net worth held by the top $i$ percent in our model and in the data, respectively, we solve the following minimization problem:\n",
    "\n",
    "$$\n",
    "\\{\\grave{\\Discount}, \\nabla\\}= \\underset{\\{{\\Discount}, \\nabla\\}}{\\text{argmin} }\\Big(\\sum_\\text{i=20, 40, 60, 80}\n",
    "  \\big(w_{i}({\\Discount}, \\nabla)-\\omega _{i}\\big)^{2}\\Big)^{1/2}\n",
    "$$\n",
    "\n",
    "subject to the constraint that the aggregate wealth (net worth)-to-output ratio in the model matches the aggregate\n",
    "capital-to-output ratio from the perfect foresight model ($\\KLev_{PF}/\\YLev_{PF}$). When solving the problem for the FBS specification we shut down the aggregate shocks (practically, this does not affect the estimates given their small size).\n",
    "\n",
    "$$\\KLev / \\YLev = \\KLev_{PF} / \\YLev_{PF}$$\n",
    "\n",
    "The solution to this problem is $\\{\\grave{\\Discount}, \\nabla\\}=\\{0.9867, 0.0067\\}$\n",
    ", so that the discount factors are evenly spread roughly between 0.98 and 0.99. We call the optimal value of the objective function the 'Lorenz distance' and use it as a measure of fit of the models.\n",
    "\n",
    "The introduction of even such a relatively modest amount of time\n",
    "preference heterogeneity sharply improves the model's fit to the targeted\n",
    "proportions of wealth holdings, bringing it reasonably in line with the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T15:43:52.107724Z",
     "iopub.status.busy": "2023-04-06T15:43:52.106716Z",
     "iopub.status.idle": "2023-04-06T15:43:52.120722Z",
     "shell.execute_reply": "2023-04-06T15:43:52.120722Z"
    },
    "lines_to_next_cell": 2,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This options file establishes the second simplest model specification possible:\n",
    "with heterogeneity, no aggregate shocks, perpetual youth model, matching net worth.\n",
    "\"\"\"\n",
    "do_param_dist = True  # Do param-dist version if True, param-point if False\n",
    "do_lifecycle = False  # Use lifecycle model if True, perpetual youth if False\n",
    "do_agg_shocks = False  # Solve the FBS aggregate shocks version of the model\n",
    "do_liquid = False  # Matches liquid assets data when True, net worth data when False\n",
    "do_tractable = False  #"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T15:43:52.122725Z",
     "iopub.status.busy": "2023-04-06T15:43:52.121725Z",
     "iopub.status.idle": "2023-04-06T15:54:09.603796Z",
     "shell.execute_reply": "2023-04-06T15:54:09.603796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning an estimation with the specification name PYUnifbetaDistIndNetWorth...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get_KY_ratio_difference tried center = 0.9867 and got -0.027981685838680104\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.0067 and got 8.571461336506975\n",
      "get_KY_ratio_difference tried center = 0.9867000100000001 and got -0.02797827291809485\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.0067 and got 8.571445407834872\n",
      "get_KY_ratio_difference tried center = 0.9867 and got -0.027979635266987657\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.00670001 and got 8.57142195333361\n",
      "get_KY_ratio_difference tried center = 0.995 and got 2.7330865328513814\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.2529243519172324\n",
      "get_KY_ratio_difference tried center = 0.99499999 and got 2.733083615718669\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.2529157441684315\n",
      "get_KY_ratio_difference tried center = 0.995 and got 2.733084886267814\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.00799999 and got 2.2529029113743224\n",
      "get_KY_ratio_difference tried center = 0.9907024122827108 and got 1.358444203604118\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.007326883851508928 and got 3.960656394826425\n",
      "get_KY_ratio_difference tried center = 0.9907024222827109 and got 1.3584472113482864\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.007326883851508928 and got 3.9606522273497453\n",
      "get_KY_ratio_difference tried center = 0.9907024122827108 and got 1.3584458559538204\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.007326893851508928 and got 3.9606219624939545\n",
      "get_KY_ratio_difference tried center = 0.9896832015697808 and got 1.16645200165719\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.259022739626737\n",
      "get_KY_ratio_difference tried center = 0.9896832115697809 and got 1.1664550180298696\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.2590183842407403\n",
      "get_KY_ratio_difference tried center = 0.9896832015697808 and got 1.1664502315495695\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.00799999 and got 2.2590510433446\n",
      "get_KY_ratio_difference tried center = 0.9886822232774574 and got 0.8615659067896888\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.714647703919424\n",
      "get_KY_ratio_difference tried center = 0.9886822332774574 and got 0.8615689814782357\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.7146430645654522\n",
      "get_KY_ratio_difference tried center = 0.9886822232774574 and got 0.8615640680475911\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.00799999 and got 2.7146756988673104\n",
      "get_KY_ratio_difference tried center = 0.988356239742786 and got 0.7608744655774\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.869386183175092\n",
      "get_KY_ratio_difference tried center = 0.988356249742786 and got 0.7608775635368952\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.8693813383303164\n",
      "get_KY_ratio_difference tried center = 0.988356239742786 and got 0.7608726009157505\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.00799999 and got 2.8694141747803634\n",
      "get_KY_ratio_difference tried center = 0.9884101465735519 and got 0.7775788527672018\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.8432477799512803\n",
      "get_KY_ratio_difference tried center = 0.9884101565735519 and got 0.7775819468316061\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.8432429789159395\n",
      "get_KY_ratio_difference tried center = 0.9884101465735519 and got 0.7775769925215203\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.00799999 and got 2.8432757686413552\n",
      "get_KY_ratio_difference tried center = 0.9884060507004983 and got 0.7763115001716536\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.845214858532942\n",
      "get_KY_ratio_difference tried center = 0.9884060607004983 and got 0.7763145945440661\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.8452100548716373\n",
      "get_KY_ratio_difference tried center = 0.9884060507004983 and got 0.7763096395847073\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.00799999 and got 2.8452428474038216\n",
      "get_KY_ratio_difference tried center = 0.9884057464707907 and got 0.776217359818467\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.845361007823885\n",
      "get_KY_ratio_difference tried center = 0.9884057564707908 and got 0.7762204542129849\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.008 and got 2.84535620372366\n",
      "get_KY_ratio_difference tried center = 0.9884057464707907 and got 0.7762154992059842\n",
      "find_lorenz_distance_at_target_KY tried spread = 0.00799999 and got 2.8453889966213977\n",
      "Estimate is center=0.9884057464707907, spread=0.008, took 475.1235671043396 seconds.\n",
      "Running in interactive shell (Jupyter notebook or spyder)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimate is center=0.9884057464707907, spread=0.008\n",
      "Lorenz distance is 2.845361007823885\n",
      "Average MPC for all consumers is 0.167\n",
      "Average MPC in the top percentile of W/Y is 0.045\n",
      "Average MPC in the top decile of W/Y is 0.049\n",
      "Average MPC in the top quintile of W/Y is 0.052\n",
      "Average MPC in the second quintile of W/Y is 0.065\n",
      "Average MPC in the middle quintile of W/Y is 0.096\n",
      "Average MPC in the fourth quintile of W/Y is 0.197\n",
      "Average MPC in the bottom quintile of W/Y is 0.424\n",
      "Average MPC in the top percentile of y is 0.148\n",
      "Average MPC in the top decile of y is 0.149\n",
      "Average MPC in the top quintile of y is 0.153\n",
      "Average MPC in the second quintile of y is 0.179\n",
      "Average MPC in the middle quintile of y is 0.179\n",
      "Average MPC in the fourth quintile of y is 0.148\n",
      "Average MPC in the bottom quintile of y is 0.174\n",
      "Average MPC for the employed is 0.160\n",
      "Average MPC for the unemployed is 0.252\n",
      "Average MPC for the retired is nan\n",
      "Of the population with the 1/3 highest MPCs...\n",
      "55.810% are in the bottom wealth quintile,\n",
      "35.476% are in the second wealth quintile,\n",
      "8.253% are in the third wealth quintile,\n",
      "0.454% are in the fourth wealth quintile,\n",
      "and 0.007% are in the top wealth quintile.\n",
      "\n",
      "These results have been saved to ./code/results/PYUnifbetaDistIndNetWorth.txt\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "options = {\n",
    "    \"param_name\": param_name,\n",
    "    \"dist_type\": dist_type,\n",
    "    \"run_estimation\": run_estimation,\n",
    "    \"run_sensitivity\": run_sensitivity,\n",
    "    \"find_beta_vs_KY\": find_beta_vs_KY,\n",
    "    \"do_tractable\": do_tractable,\n",
    "    \"do_param_dist\": do_param_dist,\n",
    "    \"do_lifecycle\": do_lifecycle,\n",
    "    \"do_agg_shocks\": do_agg_shocks,\n",
    "    \"do_liquid\": do_liquid,\n",
    "    \"do_combo_estimation\": True,\n",
    "}\n",
    "\n",
    "\n",
    "EstimationEconomy = estimate(options, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T15:54:09.605795Z",
     "iopub.status.busy": "2023-04-06T15:54:09.605795Z",
     "iopub.status.idle": "2023-04-06T15:54:09.618795Z",
     "shell.execute_reply": "2023-04-06T15:54:09.618795Z"
    }
   },
   "outputs": [],
   "source": [
    "# Construct the Lorenz curves and plot them\n",
    "\n",
    "pctiles = np.linspace(0.001, 0.999, 15)\n",
    "SCF_Lorenz_points = get_lorenz_shares(\n",
    "    SCF_wealth, weights=SCF_weights, percentiles=pctiles\n",
    ")\n",
    "\n",
    "sim_wealth = np.asarray(EstimationEconomy.reap_state[\"aLvl\"]).flatten()\n",
    "sim_Lorenz_points = get_lorenz_shares(sim_wealth, percentiles=pctiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-06T15:54:09.620796Z",
     "iopub.status.busy": "2023-04-06T15:54:09.620796Z",
     "iopub.status.idle": "2023-04-06T15:54:09.739822Z",
     "shell.execute_reply": "2023-04-06T15:54:09.739822Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.title(\"Wealth Distribution\")\n",
    "plt.plot(pctiles, SCF_Lorenz_points, \"--k\", label=\"SCF\")\n",
    "plt.plot(pctiles, sim_Lorenz_points, \"-b\", label=\"Beta-Dist\")\n",
    "plt.plot(pctiles, pctiles, \"g-.\", label=\"45 Degree\")\n",
    "plt.xlabel(\"Percentile of net worth\")\n",
    "plt.ylabel(\"Cumulative share of wealth\")\n",
    "plt.legend(loc=2)\n",
    "plt.ylim([0, 1])\n",
    "plt.show(\"wealth_distribution_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
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  "latex_envs": {
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   "eqLabelWithNumbers": true,
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   "hotkeys": {
    "equation": "Ctrl-E",
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   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "164.994px"
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   "toc_section_display": true,
   "toc_window_display": true
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  "vscode": {
   "interpreter": {
    "hash": "79df5fddd1bf7837182dc0288ea2fd71cfa8aa9192614ea42e8f71958b4b5b41"
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