{
 "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 pysolve3.model import Model\n",
    "from pysolve3.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": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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=False, right=False)\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": {},
     "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=False, right=False)\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": {},
     "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=False, right=False)\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",
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}