{
 "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 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": {},
     "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=False, right=False)\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": {},
     "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=False, right=False)\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": {},
     "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=False, right=False)\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": []
  }
 ],
 "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",
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