{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f7aa3757",
   "metadata": {},
   "source": [
    "# The Life Cycle Model: Theory vs Data\n",
    "\n",
    "[![badge](https://img.shields.io/badge/Launch%20using%20-Econ--ARK-blue)](https://econ-ark.org/materials/lifecyclemodeltheoryvsdata#launch)\n",
    "\n",
    "National registry data on income and wealth from Scandinavian countries (esp. Norway) have recently become available (with a lot of security) to some (lucky!) researchers.   These data offer a uniquely powerful tool for testing (and improving) our models of consumption and saving behavior over the life cycle.\n",
    "\n",
    "This notebook is an example of how to construct a life cycle model with the HARK toolkit that makes predictions that can be compared to the raw data statistics=.\n",
    "\n",
    "For example, some papers have tabulated information about the **growth rate** of assets at different ages over the life cycle.\n",
    "\n",
    "The default parameters of the HARK life cycle model have not been optmized to match features of the Norwegian data; a first step in a real \"structural\" estimation would be to use Norwegian calibrate the inputs to the model (like the profile of income, and the magnitude of income shocks, over the life cycle), and then to find the values of parameters like the time preference rate that allow the model to fit the data best.  (See [SolvingMicroDSOPs](https://www.econ2.jhu.edu/people/ccarroll/SolvingMicroDSOPs) for how this can be done, and search for the corresponding HARK content using [our documentation](https://hark.readthedocs.io))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40023351",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial imports and notebook setup, click arrow to show\n",
    "\n",
    "# The consumption-saving micro model\n",
    "from matplotlib import pyplot as plt\n",
    "import warnings\n",
    "import HARK.ConsumptionSaving.ConsIndShockModel as cShksModl\n",
    "from HARK.utilities import plot_funcs  # Some tools\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77a41e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------------------------------------------------------------------------------\n",
    "# - Define all of the model parameters for SolvingMicroDSOPs and ConsumerExamples -\n",
    "# ---------------------------------------------------------------------------------\n",
    "\n",
    "exp_nest = 3  # Number of times to \"exponentially nest\" when constructing a_grid\n",
    "aXtraMin = 0.001  # Minimum end-of-period \"assets above minimum\" value\n",
    "aXtraMax = 20  # Maximum end-of-period \"assets above minimum\" value\n",
    "aXtraHuge = None  # A very large value of assets to add to the grid, not used\n",
    "aXtraExtra = None  # Some other value of assets to add to the grid, not used\n",
    "aXtraCount = 8  # Number of points in the grid of \"assets above minimum\"\n",
    "\n",
    "# Artificial borrowing constraint; imposed minimum level of end-of period assets\n",
    "BoroCnstArt = 0.0\n",
    "CubicBool = (\n",
    "    True  # Use cubic spline interpolation when True, linear interpolation when False\n",
    ")\n",
    "vFuncBool = False  # Whether to calculate the value function during solution\n",
    "\n",
    "Rfree = 1.03  # Interest factor on assets\n",
    "PermShkCount = (\n",
    "    7  # Number of points in discrete approximation to permanent income shocks\n",
    ")\n",
    "TranShkCount = (\n",
    "    7  # Number of points in discrete approximation to transitory income shocks\n",
    ")\n",
    "UnempPrb = 0.005  # Probability of unemployment while working\n",
    "UnempPrbRet = 0.000  # Probability of \"unemployment\" while retired\n",
    "IncUnemp = 0.0  # Unemployment benefits replacement rate\n",
    "IncUnempRet = 0.0  # \"Unemployment\" benefits when retired\n",
    "\n",
    "final_age = 90  # Age at which the problem ends (die with certainty)\n",
    "retirement_age = 65  # Age at which the consumer retires\n",
    "initial_age = 25  # Age at which the consumer enters the model\n",
    "TT = final_age - initial_age  # Total number of periods in the model\n",
    "retirement_t = retirement_age - initial_age - 1\n",
    "\n",
    "# Initial guess of the coefficient of relative risk aversion during estimation (rho)\n",
    "CRRA_start = 4.0\n",
    "# Initial guess of the adjustment to the discount factor during estimation (beth)\n",
    "DiscFacAdj_start = 0.99\n",
    "DiscFacAdj_bound = [\n",
    "    0.0001,\n",
    "    15.0,\n",
    "]  # Bounds for beth; if violated, objective function returns \"penalty value\"\n",
    "CRRA_bound = [\n",
    "    0.0001,\n",
    "    15.0,\n",
    "]  # Bounds for rho; if violated, objective function returns \"penalty value\"\n",
    "\n",
    "# Expected growth rates of permanent income over the lifecycle, starting from age 25\n",
    "PermGroFac = [\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.025,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    1.01,\n",
    "    0.7,  # <-- This represents retirement\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "]\n",
    "\n",
    "# Age-varying discount factors over the lifecycle, lifted from Cagetti (2003)\n",
    "DiscFac_timevary = [\n",
    "    1.064914,\n",
    "    1.057997,\n",
    "    1.051422,\n",
    "    1.045179,\n",
    "    1.039259,\n",
    "    1.033653,\n",
    "    1.028352,\n",
    "    1.023348,\n",
    "    1.018632,\n",
    "    1.014198,\n",
    "    1.010037,\n",
    "    1.006143,\n",
    "    1.002509,\n",
    "    0.9991282,\n",
    "    0.9959943,\n",
    "    0.9931012,\n",
    "    0.9904431,\n",
    "    0.9880143,\n",
    "    0.9858095,\n",
    "    0.9838233,\n",
    "    0.9820506,\n",
    "    0.9804866,\n",
    "    0.9791264,\n",
    "    0.9779656,\n",
    "    0.9769995,\n",
    "    0.9762239,\n",
    "    0.9756346,\n",
    "    0.9752274,\n",
    "    0.9749984,\n",
    "    0.9749437,\n",
    "    0.9750595,\n",
    "    0.9753422,\n",
    "    0.9757881,\n",
    "    0.9763936,\n",
    "    0.9771553,\n",
    "    0.9780698,\n",
    "    0.9791338,\n",
    "    0.9803439,\n",
    "    0.981697,\n",
    "    0.8287214,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "    0.9902111,\n",
    "]\n",
    "\n",
    "# Survival probabilities over the lifecycle, starting from age 25\n",
    "LivPrb = [\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,\n",
    "    1.0,  # <-- automatic survival to age 65\n",
    "    0.98438596,\n",
    "    0.98438596,\n",
    "    0.98438596,\n",
    "    0.98438596,\n",
    "    0.98438596,\n",
    "    0.97567062,\n",
    "    0.97567062,\n",
    "    0.97567062,\n",
    "    0.97567062,\n",
    "    0.97567062,\n",
    "    0.96207901,\n",
    "    0.96207901,\n",
    "    0.96207901,\n",
    "    0.96207901,\n",
    "    0.96207901,\n",
    "    0.93721595,\n",
    "    0.93721595,\n",
    "    0.93721595,\n",
    "    0.93721595,\n",
    "    0.93721595,\n",
    "    0.63095734,\n",
    "    0.63095734,\n",
    "    0.63095734,\n",
    "    0.63095734,\n",
    "    0.63095734,\n",
    "]\n",
    "\n",
    "\n",
    "# Standard deviations of permanent income shocks by age, starting from age 25\n",
    "PermShkStd = [\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,  # <-- no permanent income shocks after retirement\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "]\n",
    "\n",
    "# Standard deviations of transitory income shocks by age, starting from age 25\n",
    "TranShkStd = [\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.1,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,  # <-- no transitory income shocs after retirement\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "    0.0,\n",
    "]\n",
    "\n",
    "# Age groups for the estimation: calculate average wealth-to-permanent income ratio\n",
    "# for consumers within each of these age groups, compare actual to simulated data\n",
    "empirical_cohort_age_groups = [\n",
    "    [26, 27, 28, 29, 30],\n",
    "    [31, 32, 33, 34, 35],\n",
    "    [36, 37, 38, 39, 40],\n",
    "    [41, 42, 43, 44, 45],\n",
    "    [46, 47, 48, 49, 50],\n",
    "    [51, 52, 53, 54, 55],\n",
    "    [56, 57, 58, 59, 60],\n",
    "]\n",
    "\n",
    "initial_wealth_income_ratio_vals = [\n",
    "    0.17,\n",
    "    0.5,\n",
    "    0.83,\n",
    "]  # Three point discrete distribution of initial w\n",
    "initial_wealth_income_ratio_probs = [\n",
    "    0.33333,\n",
    "    0.33333,\n",
    "    0.33334,\n",
    "]  # Equiprobable discrete distribution of initial w\n",
    "num_agents = 10000  # Number of agents to simulate\n",
    "bootstrap_size = 50  # Number of re-estimations to do during bootstrap\n",
    "seed = 31382  # Just an integer to seed the estimation\n",
    "\n",
    "\n",
    "# Dictionary that can be passed to ConsumerType to instantiate\n",
    "init_consumer_objects = {\n",
    "    \"CRRA\": CRRA_start,\n",
    "    \"Rfree\": Rfree,\n",
    "    \"PermGroFac\": PermGroFac,\n",
    "    \"BoroCnstArt\": BoroCnstArt,\n",
    "    \"PermShkStd\": PermShkStd,\n",
    "    \"PermShkCount\": PermShkCount,\n",
    "    \"TranShkStd\": TranShkStd,\n",
    "    \"TranShkCount\": TranShkCount,\n",
    "    \"T_cycle\": TT,\n",
    "    \"UnempPrb\": UnempPrb,\n",
    "    \"UnempPrbRet\": UnempPrbRet,\n",
    "    \"T_retire\": retirement_t,\n",
    "    \"T_age\": TT + 1,\n",
    "    \"IncUnemp\": IncUnemp,\n",
    "    \"IncUnempRet\": IncUnempRet,\n",
    "    \"aXtraMin\": aXtraMin,\n",
    "    \"aXtraMax\": aXtraMax,\n",
    "    \"aXtraCount\": aXtraCount,\n",
    "    \"aXtraExtra\": [aXtraExtra, aXtraHuge],\n",
    "    \"aXtraNestFac\": exp_nest,\n",
    "    \"LivPrb\": LivPrb,\n",
    "    \"DiscFac\": DiscFac_timevary,\n",
    "    \"AgentCount\": num_agents,\n",
    "    \"seed\": seed,\n",
    "    \"tax_rte\": 0.0,\n",
    "    \"vFuncBool\": vFuncBool,\n",
    "    \"CubicBool\": CubicBool,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "288abe3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up default values for CRRA, DiscFac, and simulation variables in the dictionary\n",
    "init_consumer_objects[\n",
    "    \"CRRA\"\n",
    "] = 2.00  # Default coefficient of relative risk aversion (rho)\n",
    "# Default intertemporal discount factor (beta)\n",
    "init_consumer_objects[\"DiscFac\"] = 0.97\n",
    "# Aggregate permanent income growth factor\n",
    "init_consumer_objects[\"PermGroFacAgg\"] = 1.0\n",
    "init_consumer_objects[\"aNrmInitMean\"] = -10.0  # Mean of log initial assets\n",
    "# Standard deviation of log initial assets\n",
    "init_consumer_objects[\"aNrmInitStd\"] = 1.0\n",
    "# Mean of log initial permanent income\n",
    "init_consumer_objects[\"pLvlInitMean\"] = 0.0\n",
    "init_consumer_objects[\n",
    "    \"pLvlInitStd\"\n",
    "] = 0.0  # Standard deviation of log initial permanent income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1d2fdc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make an instance of a lifecycle consumer to be used for estimation\n",
    "LifeCyclePop = cShksModl.IndShockConsumerType(**init_consumer_objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb71801f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aNrm': array([[0.15124943, 0.15125731, 0.15126049, ..., 0.15125088, 0.15125539,\n",
       "         0.151246  ],\n",
       "        [0.22518609, 0.18215553, 0.35807711, ..., 0.27812983, 0.17667259,\n",
       "         0.15458877],\n",
       "        [0.32597743, 0.38337328, 0.37146627, ..., 0.41407619, 0.2350288 ,\n",
       "         0.25442612],\n",
       "        ...,\n",
       "        [0.01608449, 0.35744504, 0.23336433, ..., 0.46517625, 0.49899843,\n",
       "         0.52070675],\n",
       "        [0.15126599, 0.40279067, 0.15865312, ..., 0.63411803, 0.59225189,\n",
       "         0.50505825],\n",
       "        [0.21086177, 0.43497386, 0.10261234, ..., 0.49429389, 0.5951336 ,\n",
       "         0.48172896]]),\n",
       " 'aLvl': array([[0.16005575, 0.14242219, 0.14242518, ..., 0.16005728, 0.16712597,\n",
       "         0.13183977],\n",
       "        [0.2633008 , 0.189512  , 0.31746737, ..., 0.32520573, 0.23338661,\n",
       "         0.13744097],\n",
       "        [0.42114459, 0.37555838, 0.31010102, ..., 0.42203979, 0.29234057,\n",
       "         0.23937429],\n",
       "        ...,\n",
       "        [0.01270434, 0.43632293, 0.62845058, ..., 0.70423178, 0.57352318,\n",
       "         0.65775617],\n",
       "        [0.16007327, 0.46295571, 0.42725314, ..., 0.90391888, 0.59336343,\n",
       "         0.67513518],\n",
       "        [0.22758893, 0.59771874, 0.27633522, ..., 0.84239982, 0.58615115,\n",
       "         0.65679187]]),\n",
       " 'pLvl': array([[1.05822383, 0.94158876, 0.94158876, ..., 1.05822383, 1.10492571,\n",
       "         0.87169091],\n",
       "        [1.16925872, 1.04038564, 0.8865894 , ..., 1.16925872, 1.32101196,\n",
       "         0.88907473],\n",
       "        [1.29194402, 0.97961543, 0.83480262, ..., 1.0192322 , 1.24385002,\n",
       "         0.94084007],\n",
       "        ...,\n",
       "        [0.78985017, 1.2206714 , 2.6930019 , ..., 1.51390313, 1.14934868,\n",
       "         1.26319886],\n",
       "        [1.05822383, 1.14937048, 2.6930019 , ..., 1.42547418, 1.0018768 ,\n",
       "         1.33674714],\n",
       "        [1.0793276 , 1.37414863, 2.6930019 , ..., 1.70424892, 0.98490683,\n",
       "         1.3634054 ]]),\n",
       " 'mNrm': array([[1.00001827, 1.00003607, 1.00004326, ..., 1.00002153, 1.00003174,\n",
       "         1.00001051],\n",
       "        [1.14105943, 1.06423984, 1.33773072, ..., 1.22438773, 1.05354839,\n",
       "         1.00744107],\n",
       "        [1.29330935, 1.37152667, 1.35560327, ..., 1.41203468, 1.15716564,\n",
       "         1.18806724],\n",
       "        ...,\n",
       "        [1.06456208, 1.33262987, 1.34334302, ..., 1.47580996, 1.48887221,\n",
       "         1.52996964],\n",
       "        [1.00005568, 1.39107394, 1.24036526, ..., 1.68112185, 1.5896886 ,\n",
       "         1.50688532],\n",
       "        [1.11666181, 1.43040404, 1.16341271, ..., 1.51020732, 1.58443432,\n",
       "         1.47394272]]),\n",
       " 'cNrm': array([[0.84876883, 0.84877876, 0.84878277, ..., 0.84877065, 0.84877634,\n",
       "         0.84876451],\n",
       "        [0.91587334, 0.88208431, 0.97965361, ..., 0.94625789, 0.87687579,\n",
       "         0.85285231],\n",
       "        [0.96733192, 0.98815339, 0.984137  , ..., 0.99795848, 0.92213684,\n",
       "         0.93364112],\n",
       "        ...,\n",
       "        [1.04847759, 0.97518483, 1.10997869, ..., 1.01063371, 0.98987378,\n",
       "         1.00926289],\n",
       "        [0.84878969, 0.98828327, 1.08171214, ..., 1.04700382, 0.9974367 ,\n",
       "         1.00182707],\n",
       "        [0.90580003, 0.99543018, 1.06080037, ..., 1.01591343, 0.98930072,\n",
       "         0.99221376]]),\n",
       " 'TranShk': array([[1.        , 1.        , 1.        , ..., 1.        , 1.        ,\n",
       "         1.        ],\n",
       "        [1.00006632, 0.92323938, 1.1722675 , ..., 1.08339327, 0.92323938,\n",
       "         0.85470368],\n",
       "        [1.08339327, 1.1722675 , 0.96390423, ..., 1.08339327, 0.96390423,\n",
       "         1.0376015 ],\n",
       "        ...,\n",
       "        [1.        , 0.92323938, 1.        , ..., 1.08339327, 0.85470368,\n",
       "         1.0376015 ],\n",
       "        [1.        , 1.00006632, 1.        , ..., 1.1722675 , 1.00006632,\n",
       "         1.00006632],\n",
       "        [0.96390423, 1.08339327, 1.        , ..., 0.96390423, 0.96390423,\n",
       "         0.96390423]])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Solve and simulate the model (ignore the \"warning\" message)\n",
    "LifeCyclePop.solve()  # Obtain consumption rules by age\n",
    "LifeCyclePop.unpack(\"cFunc\")  # Expose the consumption rules\n",
    "\n",
    "# Which variables do we want to track\n",
    "LifeCyclePop.track_vars = [\"aNrm\", \"aLvl\", \"pLvl\", \"mNrm\", \"cNrm\", \"TranShk\"]\n",
    "\n",
    "LifeCyclePop.T_sim = 120  # Nobody lives to be older than 145 years (=25+120)\n",
    "# Construct the age-25 distribution of income and assets\n",
    "LifeCyclePop.initialize_sim()\n",
    "LifeCyclePop.simulate()  # Simulate a population behaving according to this model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b826f6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Consumption as a function of market resources while working:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the consumption functions during working life\n",
    "\n",
    "print(\"Consumption as a function of market resources while working:\")\n",
    "mMin = min([LifeCyclePop.solution[t].mNrmMin for t in range(LifeCyclePop.T_cycle)])\n",
    "plot_funcs(LifeCyclePop.cFunc[: LifeCyclePop.T_retire], mMin, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "696f13fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the saving rate function\n",
    "def savRteFunc(SomeType, m, t):\n",
    "    \"\"\"\n",
    "    Parameters:\n",
    "    ----------\n",
    "        SomeType:\n",
    "             Agent type that has been solved and simulated.\n",
    "        m:\n",
    "            normalized market resources of agent\n",
    "        t:\n",
    "            age of agent (from starting in the workforce)\n",
    "\n",
    "\n",
    "    Returns:\n",
    "    --------\n",
    "        savRte: float\n",
    "\n",
    "    \"\"\"\n",
    "    inc = (SomeType.Rfree - 1.0) * (\n",
    "        m - 1.0\n",
    "    ) + 1.0  # Normalized by permanent labor income\n",
    "    cns = SomeType.solution[t].cFunc(m)  # Consumption (normalized)\n",
    "    sav = inc - cns  # Flow of saving this period\n",
    "    savRte = sav / inc  # Saving Rate\n",
    "    return savRte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5ef117a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a matrix gathering useful data:\n",
    "# 't_now', 'aNrm_hist', 'cNrm_hist', employment-status in date t and date t-1,\n",
    "# aLvlGro_hist, Saving rate\n",
    "\n",
    "w, h = 1, LifeCyclePop.T_cycle\n",
    "giant_list = [[0 for x in range(w)] for y in range(h)]\n",
    "savRte_list = []\n",
    "\n",
    "\n",
    "# Suppress some disturbing but harmless warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "for t in range(1, LifeCyclePop.T_cycle + 1):\n",
    "    # aLvlGro[0] = 0 # set the first growth rate to 0, since there is no data for period 0\n",
    "    aLvlGroNow = np.log(\n",
    "        (LifeCyclePop.history[\"aNrm\"][t] * LifeCyclePop.history[\"pLvl\"][t])\n",
    "        / LifeCyclePop.history[\"aNrm\"][t - 1]\n",
    "        * LifeCyclePop.history[\"pLvl\"][t - 1]\n",
    "    )  # (10000,)\n",
    "\n",
    "    # Call the saving rate function defined above\n",
    "    savRte = savRteFunc(LifeCyclePop, LifeCyclePop.history[\"mNrm\"][t], t)\n",
    "\n",
    "    savRte_list.append(savRte)  # Add this period's saving rate to the list\n",
    "\n",
    "    # Create elements of matrix list\n",
    "    matrix_list = [0 for number in range(7)]\n",
    "    matrix_list[0] = t\n",
    "    matrix_list[1] = LifeCyclePop.history[\"aNrm\"][t]\n",
    "    matrix_list[2] = LifeCyclePop.history[\"cNrm\"][t]\n",
    "    matrix_list[3] = LifeCyclePop.history[\"TranShk\"][t]\n",
    "    matrix_list[4] = LifeCyclePop.history[\"TranShk\"][t - 1]\n",
    "    matrix_list[5] = aLvlGroNow\n",
    "    matrix_list[6] = savRte\n",
    "\n",
    "    giant_list[t - 1] = matrix_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "860dac0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct the level of assets A from a*p where a is the ratio to permanent income p\n",
    "# Remember 41 is \"years after entering workforce\" (=age 25); 66 is the year right after retirement\n",
    "LifeCyclePop.history[\"aLvl\"] = (\n",
    "    LifeCyclePop.history[\"aNrm\"] * LifeCyclePop.history[\"pLvl\"]\n",
    ")\n",
    "aGro41 = LifeCyclePop.history[\"aLvl\"][41] / LifeCyclePop.history[\"aLvl\"][40]\n",
    "aGro41NoU = aGro41[\n",
    "    aGro41[:] > 0.2\n",
    "]  # Throw out extreme outliers; don't want growth rates relative to 0 income!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbdee4c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the (truncated) distribution of growth rates of wealth between age 65 and 66 (=25 + 41)\n",
    "\n",
    "n, bins, patches = plt.hist(aGro41NoU, 50, density=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad55152c",
   "metadata": {},
   "source": [
    "# Saving Rates and Lifetime Income Growth\n",
    "\n",
    "We are interested in how income growth over the lifetime of the agent affects their saving rate and asset ratio $a=A/P$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3275d14f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Normalized Assets')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cumulative_income_first_half = np.sum(\n",
    "    LifeCyclePop.history[\"pLvl\"][0:20, :] * LifeCyclePop.history[\"TranShk\"][0:20, :], 0\n",
    ")\n",
    "cumulative_income_second_half = np.sum(\n",
    "    LifeCyclePop.history[\"pLvl\"][20:40, :] * LifeCyclePop.history[\"TranShk\"][20:40, :],\n",
    "    0,\n",
    ")\n",
    "lifetime_growth = cumulative_income_second_half / cumulative_income_first_half\n",
    "\n",
    "t = 39\n",
    "vigntiles = pd.qcut(lifetime_growth, 20, labels=False)\n",
    "savRte = savRteFunc(LifeCyclePop, LifeCyclePop.history[\"mNrm\"][t], t)\n",
    "savRtgueseByVigtile = np.zeros(20)\n",
    "assetsByVigtile = np.zeros(20)\n",
    "assetsNrmByVigtile = np.zeros(20)\n",
    "savRteByVigtile = np.zeros(20)\n",
    "for i in range(20):\n",
    "    savRteByVigtile[i] = np.mean(savRte[vigntiles == i])\n",
    "    assetsByVigtile[i] = np.mean(LifeCyclePop.history[\"aLvl\"][t][vigntiles == i])\n",
    "    assetsNrmByVigtile[i] = np.mean(LifeCyclePop.history[\"aNrm\"][t][vigntiles == i])\n",
    "plt.plot(np.array(range(20)), savRteByVigtile)\n",
    "plt.title(\"Saving Rate at age 65, by Vigntile of Lifetime Income Growth\")\n",
    "plt.xlabel(\"Vigntile of Lifetime Income Growth\")\n",
    "plt.ylabel(\"Savings Rate\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(np.array(range(20)), assetsByVigtile)\n",
    "plt.title(\"Assets at age 65, by Vigntile of Lifetime Income Growth\")\n",
    "plt.xlabel(\"Vigntile of Lifetime Income Growth\")\n",
    "plt.ylabel(\"Assets\")\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(np.array(range(20)), assetsNrmByVigtile)\n",
    "plt.title(\"Normalized Assets at age 65, by Vigntile of Lifetime Income Growth\")\n",
    "plt.xlabel(\"Vigntile of Lifetime Income Growth\")\n",
    "plt.ylabel(\"Normalized Assets\")"
   ]
  }
 ],
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