{
 "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 DIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dis_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('NHUC', desc='Normal historic unit costs')\n",
    "    model.var('P', desc='Price level')\n",
    "    model.var('Rl', desc='Interest rate on loans')\n",
    "    model.var('Rm', desc='Interest rate on deposits')\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('YDkhs', desc='Haig-Simons measure of real disposable income')\n",
    "    model.var('YDkhse', desc='Expected HS real disposable income')\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",
    "\n",
    "    model.param('ADD', desc='Spread of loans rate over the deposit rate')\n",
    "    model.param('PR', desc='Labor productivity')\n",
    "    model.param('Rlbar', desc='Rate of interest on bank loans, set exogenously')\n",
    "    model.param('W', desc='Wage rate')\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('P = (1 + phi)*NHUC')\n",
    "    model.add('NHUC = (1 - sigmat)*UC + sigmat*(1 + Rl(-1))*UC(-1)')\n",
    "    model.add('F = S - WB + IN - IN(-1) - Rl(-1)*IN(-1)')\n",
    "    \n",
    "    # The banking system\n",
    "    model.add('Ld = IN')\n",
    "    model.add('Ls = Ld')\n",
    "    model.add('Ms = Ls')\n",
    "    model.add('Rl = Rlbar')\n",
    "    model.add('Rm = Rl - ADD')\n",
    "    model.add('Fb = Rl(-1)*Ld(-1) - Rm(-1)*Mh(-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('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",
    "    return model\n",
    "\n",
    "dis_parameters = {'alpha0': 15,\n",
    "                  'alpha1': 0.8,\n",
    "                  'alpha2': 0.1,\n",
    "                  'beta': 0.75,\n",
    "                  'eps': 0.75,\n",
    "                  'gamma': 0.25,\n",
    "                  'phi': 0.25,\n",
    "                  'sigmat': 0.15}\n",
    "dis_exogenous = {'ADD': 0.02,\n",
    "                 'PR': 1,\n",
    "                 'Rlbar': 0.04,\n",
    "                 'W': 0.86}\n",
    "\n",
    "# Warning! If you wish to initialize the variables using equations.\n",
    "# the order in which they appear is important.  Ordinary Python\n",
    "# dictionaries are not ordered, so the values will be incorrect,\n",
    "# use a list of (name, equation) tuples or an OrderedDict()\n",
    "#\n",
    "dis_variables = [('UC', 'W/PR'),\n",
    "                 ('NHUC', '(1 + sigmat*Rlbar)*UC'),\n",
    "                 ('P', '(1+phi)*NHUC'),\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",
    "                 ('Rl', 'Rlbar'),\n",
    "                 ('Rm', 'Rl - ADD')]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model DIS, increase in the mark-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "phi = create_dis_model()\n",
    "phi.set_values(dis_parameters)\n",
    "phi.set_values(dis_exogenous)\n",
    "phi.set_values(dis_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(15):\n",
    "    phi.solve(iterations=200, threshold=1e-6)\n",
    "\n",
    "# shock the system\n",
    "phi.set_values({'phi': 0.3})\n",
    "\n",
    "for _ in range(40):\n",
    "    phi.solve(iterations=100, threshold=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 9.1  Evolution of (Haig-Simons) real disposable income and of real\n",
    "    consumption following a one-shot increase in the costing margin'''\n",
    "ydkhsdata = [s['YDkhs'] for s in phi.solutions[5:]]\n",
    "ckdata = [s['Ck'] for s in phi.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, 79.8)\n",
    "\n",
    "axes.plot(ydkhsdata, linestyle='-', color='r')\n",
    "axes.plot(ckdata, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(13, 79.74, 'Real consumption')\n",
    "plt.text(12, 79.4, 'Haig-Simons real disposable income')\n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model DIS, Increase in the propensity to save out of disposable income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigmat = create_dis_model()\n",
    "sigmat.set_values(dis_parameters)\n",
    "sigmat.set_values(dis_exogenous)\n",
    "sigmat.set_values(dis_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(15):\n",
    "    sigmat.solve(iterations=200, threshold=1e-6)\n",
    "\n",
    "# shock the system\n",
    "sigmat.set_values({'sigmat': 0.25})\n",
    "\n",
    "for _ in range(40):\n",
    "    sigmat.solve(iterations=100, threshold=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.2"
   ]
  },
  {
   "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 9.2  Evolution of (Haig-Simons) real disposable income and of real\n",
    "    consumption following an increase in the target inventories to sales ratio'''\n",
    "ydkhsdata = [s['YDkhs'] for s in sigmat.solutions[5:]]\n",
    "ckdata = [s['Ck'] for s in sigmat.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, 84.8)\n",
    "\n",
    "axes.plot(ydkhsdata, linestyle='-', color='r')\n",
    "axes.plot(ckdata, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(15, 81.5, 'Real consumption')\n",
    "plt.text(8, 83, 'Haig-Simons')\n",
    "plt.text(8, 82.8, 'real disposable')\n",
    "plt.text(8, 82.6, 'income')\n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 9.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 9.3  Evolution of the desired increase in physical inventories and of\n",
    "    the change in realized inventories, following an increase in the target\n",
    "    inventories to sales ratio'''\n",
    "inkdata = list()\n",
    "inkedata = list()\n",
    "\n",
    "for i in range(5, len(sigmat.solutions)):\n",
    "    s = sigmat.solutions[i]\n",
    "    s_1 = sigmat.solutions[i-1]\n",
    "    \n",
    "    # to get the shape of the graph in the book,\n",
    "    # use INkt - INk\n",
    "    inkdata.append(s['INk'] - s_1['INk'])\n",
    "    inkedata.append(s['INke'] - s_1['INke'])\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(-0.5, 2.1)\n",
    "\n",
    "axes.plot(inkdata, linestyle='-', color='r')\n",
    "axes.plot(inkedata, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(13, 0.2, 'Change in')\n",
    "plt.text(13, 0.1, 'realized inventories')\n",
    "plt.text(14, 1.0, 'Desired increase')\n",
    "plt.text(14, 0.9, 'in physical inventories')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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