{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Economics: Chapter 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This line configures matplotlib to show figures embedded in the notebook, \n",
    "# instead of opening a new window for each figure. More about that later. \n",
    "# If you are using an old version of IPython, try using '%pylab inline' instead.\n",
    "%matplotlib inline\n",
    "\n",
    "from pysolve.model import Model\n",
    "from pysolve.utils import is_close,round_solution\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model BMWK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_bmwk_model():\n",
    "    model = Model()\n",
    "\n",
    "    model.set_var_default(0)\n",
    "    model.var('AF', desc='Amortization funds')\n",
    "    model.var('Cd', desc='Consumption goods demand by households')\n",
    "    model.var('Cs', desc='Consumption goods supply')\n",
    "    model.var('DA', desc='Depreciation allowance')\n",
    "    model.var('K', desc='Stock of capital')\n",
    "    model.var('Kt', desc='Target stock of capital')\n",
    "    model.var('Ld', desc='Demand for bank loans')\n",
    "    model.var('Ls', desc='Supply of bank loans')\n",
    "    model.var('Id', desc='Demand for investment goods')\n",
    "    model.var('Is', desc='Supply of investment goods')\n",
    "    model.var('Mh', desc='Bank deposits held by households')\n",
    "    model.var('Ms', desc='Supply of bank deposits')\n",
    "    model.var('Nd', desc='Demand for labor')\n",
    "    model.var('Ns', desc='Supply of labor')\n",
    "    model.var('Rl', desc='Rate of interest on bank loans')\n",
    "    model.var('Rm', desc='Rate of interest on bank deposits')\n",
    "    model.var('W', desc='Wage rate')\n",
    "    model.var('WBd', desc='Wage bill - demand')\n",
    "    model.var('WBs', desc='Wage bill - supply')\n",
    "    model.var('Y', desc='Income = GDP')\n",
    "    model.var('YD', desc='Disposable income of households')\n",
    "    \n",
    "    model.set_param_default(0)\n",
    "    model.param('alpha0', desc='Exogenous component in consumption')\n",
    "    model.param('alpha1r', desc='Propensity to consume out of interest income')\n",
    "    model.param('alpha1w', desc='Propensity to consume out of wage income')\n",
    "    model.param('alpha2', desc='Propensity to consume out of wealth')\n",
    "    model.param('delta', desc='Depreciation rate')\n",
    "    model.param('gamma', desc='Speed of adjustment of capital to its target value')\n",
    "    model.param('kappa', desc='Capital-output ratio')\n",
    "\n",
    "    model.param('PR', desc='Labor productivity')\n",
    "    model.param('Rlbar', desc='Rate of interest on bank loans, set exogenously')\n",
    "\n",
    "    # Basic behavioural equations\n",
    "    model.add('Cs = Cd')\n",
    "    model.add('Is  = Id')\n",
    "    model.add('Ns = Nd')\n",
    "    model.add('Ls - Ls(-1) = Ld - Ld(-1)')\n",
    "    model.add('Y = Cs + Is')\n",
    "    model.add('WBd = Y - Rl(-1)*Ld(-1) - AF')\n",
    "    model.add('AF = delta * K(-1)')\n",
    "    model.add('Ld - Ld(-1) = Id - AF')\n",
    "    \n",
    "    # Transactions of households\n",
    "    model.add('YD = WBs + Rm(-1)*Mh(-1)')\n",
    "    model.add('Mh - Mh(-1) = YD - Cd')\n",
    "    \n",
    "    # Transactions of the banks\n",
    "    model.add('Ms - Ms(-1) = Ls - Ls(-1)')\n",
    "    model.add('Rm = Rl')\n",
    "    \n",
    "    # The wage bill\n",
    "    model.add('WBs = W * Ns')\n",
    "    model.add('Nd = Y / PR')\n",
    "    model.add('W = WBd / Nd')\n",
    "    \n",
    "    # Household behavior\n",
    "    model.add('Cd = alpha0 + alpha1w*WBs + alpha1r*Rm(-1)*Mh(-1) + alpha2*Mh(-1)')\n",
    "    \n",
    "    # The investment beahavior\n",
    "    model.add('K - K(-1) = Id - DA')\n",
    "    model.add('DA = delta * K(-1)')\n",
    "    model.add('Kt = kappa * Y(-1)')\n",
    "    model.add('Id = gamma * (Kt - K(-1)) + DA')\n",
    "    \n",
    "    # The behaviour of banks\n",
    "    model.add('Rl = Rlbar')    \n",
    "\n",
    "    return model\n",
    "\n",
    "bmwk_parameters = {'alpha0': 25,\n",
    "                   'alpha1r': 0.5,\n",
    "                   'alpha1w': 0.75,\n",
    "                   'alpha2': 0.1,\n",
    "                   'delta': 0.1,\n",
    "                   'gamma': 0.15,\n",
    "                   'kappa': 1}\n",
    "bmwk_exogenous = {'PR': 1,\n",
    "                  'Rlbar': 0.04}\n",
    "bmwk_variables = {'Mh': 185.2,\n",
    "                  'Ms': 185.2,\n",
    "                  'Ld': 185.2,\n",
    "                  'Ls': 185.2,\n",
    "                  'K': 185.2,\n",
    "                  'Y': 185.2,\n",
    "                  'Rl': 0.04,\n",
    "                  'Rm' : 0.04,\n",
    "                  'W': 0.86,\n",
    "                  'Cd': 1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model BMWK, increase in the interest rate on loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "interest = create_bmwk_model()\n",
    "interest.set_values(bmwk_parameters)\n",
    "interest.set_values(bmwk_exogenous)\n",
    "interest.set_values(bmwk_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(15):\n",
    "    interest.solve(iterations=200, threshold=1e-5)\n",
    "\n",
    "# shock the system\n",
    "interest.set_values({'Rlbar': 0.05})\n",
    "\n",
    "for _ in range(40):\n",
    "    interest.solve(iterations=100, threshold=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 7.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 7.8  Evolution of Gross Domestic Income $(Y)_{t}$, following an\n",
    "    increase in the interest rate, in model BMWK'''\n",
    "data = [s['Y'] for s in interest.solutions[5:]]\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\n",
    "axes.tick_params(top='off', right='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "axes.set_ylim(180, 186)\n",
    "\n",
    "axes.plot(data, linestyle='-', color='b')\n",
    "# add labels\n",
    "plt.text(20, 182, 'National income')\n",
    "         \n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}