{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model PC "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model PC is introduced in chapter 4 of {cite:t}`GodleyLavoie2006MonetaryEconomicsIntegrated` \"Monetary Economics: An Integrated Approach to Credit, Money, Income, Production and Wealth\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Module Contents\n",
    "\n",
    "As with all `MacroStat` models, PC is divided into Variables, Parameters (fixed constants), Scenarios, and the Behavior (model initialization and steps). The module-level documentation, such as all variables/parameters/scenarios and their notation or the behavioral equations associated with each function of `BehaviorPC.py` can be seen in:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{eval-rst}\n",
    ".. toctree::\n",
    "    :maxdepth: 2\n",
    "\n",
    "    Variables <GL06PC/variables.rst>\n",
    "    Parameters <GL06PC/parameters.rst>\n",
    "    Equations <GL06PC/equations.rst>\n",
    "    Scenarios <GL06PC/scenarios.rst>\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The remainder of this page gives an introduction to the model, notes on how it is implemented in `MacroStat` and then shows some of the model dynamics by replicating the relevant graphs of Godley and Lavoie (2006)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transaction Flow Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{csv-table} Accounting Transaction Matrix for Model PC\n",
    ":file: GL06PC/transaction_matrix.csv\n",
    ":header-rows: 2\n",
    ":stub-columns: 1\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Balance Sheet Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{csv-table} Balance Sheet for Model PC\n",
    ":file: GL06PC/balance_sheet.csv\n",
    ":header-rows: 2\n",
    ":stub-columns: 1\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation in MacroStat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing these eleven equations to the `MacroStat` framework, we consider that there are:\n",
    "\n",
    "1. Three parameters (fixed constants): $\\alpha_1$, $\\alpha_2$, and $\\theta$ (see [Parameters](GL06PC/parameters.rst))\n",
    "2. Two scenario variables : $G_d(t)$ and $W(t)$ (see [Scenarios](GL06PC/scenarios.rst))\n",
    "3. The remaining 14 tracked series are variables (see [Variables](GL06PC/variables.rst))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Behavioral Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model PC imposes that the assumptions of the household on income are correct and firms supply all goods. For the implementation of the behavioral equations (see [Behavior](GL06PC/behavior.rst)), most prior implementations have made use of some form of linear solver or iteration until the system is solved. To simplify the implementation in Macrostat, we can note that the system can be solved analytically for a given timestep as follows:\n",
    "\n",
    "Substitute the consumption equation into the national income equation to obtain\n",
    "```{math}\n",
    "Y(t) = \\alpha_1 YD(t) + \\alpha_2 V(t-1) + G(t)\n",
    "```\n",
    "where $G(t)$ is exogenous and $V(t-1)$ is determined. Substituting further the disposable income equation and tax equation we can obtain\n",
    "```{math}\n",
    "Y(t) = \\alpha_1(1-\\theta)\\left(Y(t) + r(t-1)B_h(t-1)\\right) + \\alpha_2 V(t-1) + G(t)\n",
    "```\n",
    "which can be rearranged to yield a solution for $Y(t)$\n",
    "```{math}\n",
    ":label: gl06_pc_analyticalSolutionY\n",
    "Y(t) = \\frac{\\alpha_1(1-\\theta)r(t-1)B_h(t-1) + \\alpha_2 V(t-1) + G(t)}{1 - \\alpha_1(1-\\theta)}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore, for a given period $t$ we can solve the system by solving, in order:\n",
    "1. Eq. {eq}`gl06_pc_analyticalSolutionY` for national income $Y(t)$\n",
    "2. Eq. {eq}`gl06_pc_eq403_taxes` for taxes $T(t)$\n",
    "3. Eq. {eq}`gl06_pc_eq402_disposableIncome` for disposable income $YD(t)$\n",
    "4. Eq. {eq}`gl06_pc_eq405_consumption` for consumption $C(t)$\n",
    "5. Eq. {eq}`gl06_pc_eq404_wealth` for wealth $V(t)$\n",
    "6. Eq. {eq}`gl06_pc_eq407_householdBondHoldings` for household bill holdings $B_h(t)$\n",
    "7. Eq. {eq}`gl06_pc_eq406_householdDeposits` for household depositis (residual) $H_h(t)$\n",
    "8. Eq. {eq}`gl06_pc_eq408_governmentBillIssuance` for the government budget resulting in bill issuance $B_s(t)$\n",
    "9. Eq. {eq}`gl06_pc_eq410_centralBankBills` for the Central Bank holding of bills $B_{CB}(t)$\n",
    "10. Eq. {eq}`gl06_pc_eq409_moneyIssuance` for the level of cash money $H_s(t)$\n",
    "\n",
    "This is implemented as such in the [Behavior](GL06SIM/behavior.rst) class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparatory Steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import importlib\n",
    "import logging\n",
    "import sys\n",
    "\n",
    "# Import the necessary libraries for plotting\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "\n",
    "# Import the MacroStat get_model function\n",
    "from macrostat.models import get_model\n",
    "\n",
    "# Custom matplotlib style for the documentation\n",
    "plt.style.use(\"../../macrostat.mplstyle\")\n",
    "# We show the logging output in the notebook\n",
    "importlib.reload(logging)\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we can run the model without any shocks to see the convergence to the steady state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:Starting simulation. Scenario: 0\n"
     ]
    }
   ],
   "source": [
    "GL06PCClass = get_model(\"GL06PC\")\n",
    "model = GL06PCClass()\n",
    "model.simulate()\n",
    "output = model.variables.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can also check that the variables are healthy, which means that the redundant equations hold and that all the assets and liabilities are positive. For model PC, the redundant equation is that the household money stock equals the central bank money stock. \n",
    "\n",
    ":::{Note}\n",
    "In numerical implementations, due to floating point precision it is unlikely that the redundant equation will hold exactly. Therefore, we check that the absolute percentage error is less than a given tolerance, in this case 1e-5. We use the absolute percentage error to appropriately scale the error for different magnitudes of the variables.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.variables.check_health(tolerance=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convergence to the Steady State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following the derivations of section 4.5 in {cite:t}`GodleyLavoie2006MonetaryEconomicsIntegrated`, we compute the steady state as\n",
    "\n",
    "```{math}\n",
    "\\begin{align}\n",
    "G^\\star(t) &= G(t)\\\\\n",
    "r^\\star(t) &= r(t)\\\\\n",
    "\\alpha_3 &= \\frac{1-\\alpha_1}{\\alpha_2}\\\\\n",
    "YD^\\star(t) &= \\frac{G^\\star(t)}{\\frac{\\theta}{1-\\theta} - r^\\star(t)\\cdot\\left(\\left(\\lambda_0 + \\lambda_1 r^\\star(t) \\right)\\alpha_3 - \\lambda_2\\right)}\\\\\n",
    "C^\\star(t) &= YD^\\star(t)\\\\\n",
    "Y^\\star(t) &= C^\\star(t) + G^\\star(t)\\\\\n",
    "V^\\star(t) &= \\alpha_3 YD^\\star(t)\\\\\n",
    "B_h^\\star(t) &= \\left(\\left(\\lambda_0 + \\lambda_1 r^\\star(t) \\right)\\alpha_3 - \\lambda_2\\right)\\cdot YD^\\star(t)\\\\\n",
    "T^\\star(t) &= \\theta\\cdot \\left(Y^\\star(t) + r^\\star(t) B_h^\\star(t)\\right)\\\\\n",
    "H_h^\\star(t) &= V^\\star(t) - B_h^\\star(t)\\\\\n",
    "B_s^\\star(t) &= \\frac{r^\\star(t) B_{CB}^\\star(t) + T^\\star(t) - G^\\star(t)}{r^\\star(t)}\\\\\n",
    "B_{CB}^\\star(t) &= B_s^\\star(t) - B_h^\\star(t)\\\\\n",
    "H_s^\\star(t) &= H_{s}(t-1) + (B_{CB}(t) - B_{CB}(t-1))\n",
    "\\end{align}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:Computing theoretical steady state. Scenario: 0\n"
     ]
    }
   ],
   "source": [
    "model.compute_theoretical_steady_state(scenario=0)\n",
    "steadystate = model.variables.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfo = output.loc[:30]\n",
    "dfs = steadystate.loc[:30]\n",
    "\n",
    "fig, axs = plt.subplots(ncols=2, nrows=3, figsize=(8, 8))\n",
    "\n",
    "# National Income and Consumption\n",
    "axs[0,0].plot(dfo.index, dfo['NationalIncome'], color='k', label=r'National Income $Y$')\n",
    "axs[0,0].step(x=dfs.index,y=dfs[\"NationalIncome\"], color='r', linestyle='--', label=r'Steady State Income $Y^\\star$')\n",
    "axs[0,0].legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), frameon=False)\n",
    "axs[0,0].set_title('National Income')\n",
    "axs[0,1].plot(dfo.index, dfo['ConsumptionHousehold'], color='k', label=r'Consumption $C$')\n",
    "axs[0,1].plot(dfo.index, dfo['DisposableIncome'], color='g', linestyle='-.', label=r'Disposable Income $YD$')\n",
    "axs[0,1].step(x=dfs.index,y=dfs[\"ConsumptionHousehold\"], color='r', linestyle='--', label=r'Steady State Consumption $C^\\star$')\n",
    "axs[0,1].legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), frameon=False)\n",
    "axs[0,1].set_title('Consumption')\n",
    "\n",
    "# Wealth and Savings\n",
    "axs[1,0].plot(dfo.index, dfo['Wealth'], color='k', label=r'Wealth $V$')\n",
    "axs[1,0].step(x=dfs.index,y=dfs[\"Wealth\"], color='r', linestyle='--', label=r'Steady State Wealth $V^\\star$')\n",
    "axs[1,0].legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), frameon=False)\n",
    "axs[1,0].set_title('Wealth')\n",
    "axs[1,1].plot(dfo.index, dfo['Wealth'].diff(), color='k', label=r'Savings, $\\Delta V$')\n",
    "axs[1,1].axhline(y=0, color='r', linestyle='--', label=r'Steady State Savings $\\Delta V^\\star=0$')\n",
    "axs[1,1].legend(loc='upper center', bbox_to_anchor=(0.5, -0.1), frameon=False)\n",
    "axs[1,1].set_title('Savings')\n",
    "\n",
    "# Money Share and Bills Share\n",
    "share_bills = steadystate['HouseholdBillStock'] / steadystate['Wealth']\n",
    "axs[2,0].plot(output.index, output['HouseholdMoneyStock'] / output['Wealth'], color='k', linestyle='-', label='Money Share $H_h/V$')\n",
    "axs[2,0].step(x=share_bills.index,y=1-share_bills, color='r', linestyle='--', label='Steady State Share')\n",
    "axs[2,0].legend(loc='center right', frameon=False)\n",
    "axs[2,0].set_xlim(0,40)\n",
    "axs[2,0].set_title('Money Share')\n",
    "axs[2,0].yaxis.set_major_formatter(PercentFormatter(1))\n",
    "\n",
    "# Right panel - Bills share\n",
    "axs[2,1].plot(output.index, output['HouseholdBillStock'] / output['Wealth'], color='k', linestyle='-', label='Bills Share $B_h/V$')\n",
    "axs[2,1].step(x=share_bills.index,y=share_bills, color='r', linestyle='--', label='Steady State Share')\n",
    "axs[2,1].legend(loc='center right', frameon=False)\n",
    "axs[2,1].set_xlim(0,40)\n",
    "axs[2,1].set_title('Bills Share')\n",
    "axs[2,1].yaxis.set_major_formatter(PercentFormatter(1))\n",
    "\n",
    "\n",
    "fig.suptitle('Figure PC.1: Model convergence to the steady state')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
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