Chapter 9 Model DISINF1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Economics: Chapter 9"
   ]
  },
  {
   "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 DISINF1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_disinf1_model():\n",
    "    model = Model()\n",
    "\n",
    "    model.set_var_default(0)\n",
    "    model.var('Ck', desc='Real consumption')\n",
    "    model.var('C', desc='Consumption at current prices')\n",
    "    model.var('F', desc='Realized firm profits')\n",
    "    model.var('Fb', desc='Realized bank profits')\n",
    "    model.var('IN', desc='Stock of inventories at current costs')\n",
    "    model.var('INk', desc='Real inventories')\n",
    "    model.var('INke', desc='Expected real inventories')\n",
    "    model.var('INkt', desc='Target level of real inventories')\n",
    "    model.var('Ld', desc='Demand for loans')\n",
    "    model.var('Ls', desc='Supply of loans')\n",
    "    model.var('Mh', desc='Deposits held by households')\n",
    "    model.var('Mhk', desc='Real alue of deposits held by households')\n",
    "    model.var('Ms', desc='Supply of deposits')\n",
    "    model.var('N', desc='Employment level')\n",
    "    model.var('omegat', desc='Target real wage rate')\n",
    "    model.var('P', desc='Price level')\n",
    "    model.var('PIC', desc='Inflation rate of unit costs')\n",
    "    model.var('Rl', desc='Interest rate on loans')\n",
    "    model.var('Rm', desc='Interest rate on deposits')\n",
    "    model.var('RRc', desc='Real interest rate on bank loans')\n",
    "    model.var('S', desc='Sales at current prices')\n",
    "    model.var('Sk', desc='Real sales')\n",
    "    model.var('Ske', desc='Expected real sales')\n",
    "    model.var('UC', desc='Unit costs')\n",
    "    model.var('WB', desc='The wage bill')\n",
    "    model.var('Yk', desc='Real output')\n",
    "    model.var('YD', desc='Disposable income')\n",
    "    model.var('YDk', desc='Real disposable income')\n",
    "    model.var('YDkhs', desc='Haig-Simons measure of real disposable income')\n",
    "    model.var('YDkhse', desc='Expected HS real disposable income')\n",
    "    model.var('W', desc='Wage rate')\n",
    "\n",
    "    model.set_param_default(0)\n",
    "    model.param('alpha0', desc='Autonomous consumption')\n",
    "    model.param('alpha1', desc='Propensity to consume out of income')\n",
    "    model.param('alpha2', desc='Propensity to consume out of wealth')\n",
    "    model.param('beta', desc='Parameter in expectation formations on real sales')\n",
    "    model.param('eps', desc='Parameter in expectation formations on real disposable income')\n",
    "    model.param('gamma', desc='Speed of adjustment of inventories to the target level')\n",
    "    model.param('phi', desc='Mark-up on unit costs')\n",
    "    model.param('sigmat', desc='Target inventories to sales ratio')\n",
    "    model.param('omega0', desc='Exogenous component of the target real wage rate')\n",
    "    model.param('omega1', desc='Relation between the target real wage rate and productivity')\n",
    "    model.param('omega2', desc='Relation between the target real rate and the unemploment gap')\n",
    "    model.param('omega3', desc='Speed of adjustment of the wage rate')\n",
    "\n",
    "    model.param('ADD', desc='Spread of loans rate over the deposit rate')\n",
    "    model.param('Nfe', desc='Full employment level')\n",
    "    model.param('PR', desc='Labor productivity')\n",
    "    model.param('Rlbar', desc='Rate of interest on bank loans, set exogenously')\n",
    "    model.param('RRcbar', desc='Real interest rate on bank loans, set exogenously')\n",
    "\n",
    "\n",
    "    # The production decision\n",
    "    model.add('Yk = Ske + INke - INk(-1)')\n",
    "    model.add('INkt = sigmat*Ske')\n",
    "    model.add('INke = INk(-1) + gamma*(INkt - INk(-1))')\n",
    "    model.add('INk - INk(-1) = Yk - Sk')\n",
    "    model.add('Ske = beta*Sk(-1) + (1-beta)*Ske(-1)')\n",
    "    model.add('Sk = Ck')\n",
    "    model.add('N = Yk / PR')\n",
    "    model.add('WB = N*W')\n",
    "    model.add('UC = WB/Yk')\n",
    "    model.add('IN = INk*UC')\n",
    "    \n",
    "    # The pricing decision\n",
    "    model.add('S = P*Sk')\n",
    "    model.add('F = S - WB + IN - IN(-1) - Rl(-1)*IN(-1)')\n",
    "    model.add('P = (1 + phi)*(1+RRc*sigmat)*UC')\n",
    "    \n",
    "    # The banking system\n",
    "    model.add('Ld = IN')\n",
    "    model.add('Ls = Ld')\n",
    "    model.add('Ms = Ls')\n",
    "    model.add('Rm = Rl - ADD')\n",
    "    model.add('Fb = Rl(-1)*Ld(-1) - Rm(-1)*Mh(-1)')\n",
    "    model.add('PIC = (UC/UC(-1)) - 1')\n",
    "    model.add('RRc = RRcbar')\n",
    "    model.add('Rl = (1 + RRc)*(1 + PIC) - 1')\n",
    "    \n",
    "    # The consumption decision\n",
    "    model.add('YD = WB + F + Fb + Rm(-1)*Mh(-1)')\n",
    "    model.add('Mh - Mh(-1) = YD - C')\n",
    "    model.add('YDkhs = Ck + (Mhk - Mhk(-1))')\n",
    "    model.add('YDk = YD/P')\n",
    "    model.add('C = Ck*P')\n",
    "    model.add('Mhk = Mh/P')\n",
    "    model.add('Ck = alpha0 + alpha1*YDkhse + alpha2*Mhk(-1)')\n",
    "    model.add('YDkhse = eps*YDkhs(-1) + (1 - eps)*YDkhse(-1)')\n",
    "    \n",
    "    # The inflation process\n",
    "    model.add('omegat = omega0 + omega1*PR + omega2*(N/Nfe)')\n",
    "    model.add('W = W(-1)*(1 + omega3*(omegat(-1)-(W(-1)/P(-1))))')\n",
    "\n",
    "    return model\n",
    "\n",
    "disinf1_parameters = [('alpha0', 15),\n",
    "                      ('alpha1', 0.8),\n",
    "                      ('alpha2', 0.1),\n",
    "                      ('beta', 0.9),\n",
    "                      ('eps', 0.8),\n",
    "                      ('gamma', 0.25),\n",
    "                      ('phi', 0.24),\n",
    "                      ('sigmat', 0.2),\n",
    "                      ('omega1', 1),\n",
    "                      ('omega2', 1.2),\n",
    "                      ('omega0', '0.8 - omega1*PR - omega2'),\n",
    "                      ('omega3', 0.3)]\n",
    "disinf1_exogenous = [('ADD', 0.02),\n",
    "                     ('PR', 1),\n",
    "                     ('RRcbar', 0.04)]\n",
    "disinf1_variables = [('W', 1),\n",
    "                     ('UC', 'W/PR'),\n",
    "                     ('P', '(1+phi)*(1+RRcbar*sigmat)*UC'),\n",
    "                     ('YDkhs', 'alpha0/(1-alpha1-alpha2*sigmat*UC/P)'),\n",
    "                     ('Ck', 'YDkhs'),\n",
    "                     ('Sk', 'Ck'),\n",
    "                     ('INk', 'sigmat*Sk'),\n",
    "                     ('IN', 'INk*UC'),\n",
    "                     ('Ld', 'IN'),\n",
    "                     ('Mh', 'Ld'),\n",
    "                     ('Mhk', 'Mh/P'),\n",
    "                     ('Ms', 'Mh'),\n",
    "                     ('Ls', 'Ld'),\n",
    "                     ('Ske', 'Sk'),\n",
    "                     ('YDkhse', 'YDkhs'),\n",
    "                     ('omegat', 'W/P'),\n",
    "                     ('Rl', '(1 + RRcbar) - 1'),\n",
    "                     ('Rm', 'Rl - ADD'),\n",
    "                     ('Nfe', 'Sk/PR')]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model DISINF1, increase in target wage rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "omega0 = create_disinf1_model()\n",
    "omega0.set_values(disinf1_parameters)\n",
    "omega0.set_values(disinf1_exogenous)\n",
    "omega0.set_values(disinf1_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(15):\n",
    "    omega0.solve(iterations=1000, threshold=1e-6)\n",
    "\n",
    "# shock the system\n",
    "omega0.set_values({'omega0': -1.35})\n",
    "\n",
    "for _ in range(40):\n",
    "    omega0.solve(iterations=100, threshold=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.4a"
   ]
  },
  {
   "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 9.4a  Evolution of (Haig-Simons) real disposable income and of real\n",
    "    consumption following an increase in the rate of inflation, in a variant\n",
    "    where households take capital gains and losses from inflation into account\n",
    "    in their expenditure decisions and inflation has no real effects.'''\n",
    "ydkhsdata = [s['YDkhs'] for s in omega0.solutions[5:]]\n",
    "ckdata = [s['Ck'] for s in omega0.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(79.3, 85)\n",
    "\n",
    "axes.plot(ydkhsdata, linestyle='-', color='r')\n",
    "axes.plot(ckdata, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(15, 81, 'Real consumption')\n",
    "plt.text(8, 82.2, 'Haig-Simons')\n",
    "plt.text(8, 82, 'real disposable')\n",
    "plt.text(8, 81.8, 'income')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.5a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 9.5a  Evolution of real wealth, following an increase in the rate\n",
    "    of inflation, in a variant where households take capital gains and losses\n",
    "    from inflation into account in their expenditure decisions and inflation\n",
    "    has no real effects.'''\n",
    "data = [s['Mhk'] for s in omega0.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(11, 15)\n",
    "\n",
    "axes.plot(data, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(15, 12.8, 'Real wealth')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.6a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 9.6a  Evolution of the rate of price inflation, following an increase\n",
    "    in the target real-wage of workers in a variant where households take capital \n",
    "    gains and losses from inflation into account in their expenditure decisions \n",
    "    and inflation has no real effects.'''\n",
    "data = list()\n",
    "\n",
    "for i in range(10, len(omega0.solutions)):\n",
    "    s = omega0.solutions[i]\n",
    "    s_1 = omega0.solutions[i-1]\n",
    "    \n",
    "    data.append((s['P']/s_1['P'])-1)\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(-0.01, .4)\n",
    "\n",
    "axes.plot(data, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(15, .03, 'Inflation rate')\n",
    "fig.text(0.1, -.15, 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
}