{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Economics: Chapter 6"
   ]
  },
  {
   "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model OPENM3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_openm3_model():\n",
    "    model = Model()\n",
    "\n",
    "    model.set_var_default(0)\n",
    "    model.var('BcbN', desc='Bills held by the Central Bank in Country N')\n",
    "    model.var('BcbS', desc='Bills held by the Central Bank in Country S')\n",
    "    model.var('BhN', desc='Bills held by households, Country N')\n",
    "    model.var('BhS', desc='Bills held by households, Country S')\n",
    "    model.var('BsN', desc='Supply of government bills in Country N')\n",
    "    model.var('BsS', desc='Supply of government bills in Country S')\n",
    "    model.var('CN', desc='Consumption, Country N')\n",
    "    model.var('CS', desc='Consumption, Country S')\n",
    "    model.var('HhN', desc='Cash held by households, Country N')\n",
    "    model.var('HhS', desc='Cash held by households, Country S')\n",
    "    model.var('HsN', desc='Supply of cash in Country N')\n",
    "    model.var('HsS', desc='Supply of cash in Country S')\n",
    "    model.var('IMN', desc='Imports, Region N')\n",
    "    model.var('IMS', desc='Imports, Region S')\n",
    "    model.var('ORN', desc='Gold holding by Central bank in Country N')\n",
    "    model.var('ORS', desc='Gold holding by Central bank in Country S')\n",
    "    model.var('PgN', desc='Price of gold in Country N')\n",
    "    model.var('PgS', desc='Price of gold in Country S')\n",
    "    model.var('RN', desc='Interest rate on bills in Country N')\n",
    "    model.var('RS', desc='Interest rate on bills in Country S')\n",
    "    model.var('TN', desc='Tax payments, Country N')\n",
    "    model.var('TS', desc='Tax payments, Country S')\n",
    "    model.var('VN', desc='Household wealth, Country N')\n",
    "    model.var('VS', desc='Household wealth, Country S')\n",
    "    model.var('XN', desc='Exports, Country N')\n",
    "    model.var('XS', desc='Exports, Country S')\n",
    "    model.var('XR', desc='Exchange rate (units of currency S for one unit of currency N)')\n",
    "    model.var('YN', desc='National income, Country N')\n",
    "    model.var('YS', desc='National income, Country S')\n",
    "    model.var('YDN', desc='National disposable income, Country N')\n",
    "    model.var('YDS', desc='National disposable income, Country S')\n",
    "    \n",
    "    model.var('alpha1N', desc='Propensity to consume out of income, Country N')\n",
    "    model.var('alpha1S', desc='Propensity to consume out of income, Country S')\n",
    "\n",
    "    model.set_param_default(0)\n",
    "    model.param('alpha10N', desc='Propensity to consume out of income, Country N, exogenous')\n",
    "    model.param('alpha10S', desc='Propensity to consume out of income, Country S, exogenous')\n",
    "    model.param('alpha2N', desc='Propensity to consume out of wealth, Country N')\n",
    "    model.param('alpha2S', desc='Propensity to consume out of wealth, Country S')\n",
    "    model.param('iotaN', desc='Parameter linking the propensity to consume to the interest rate for Country N')\n",
    "    model.param('iotaS', desc='Parameter linking the propensity to consume to the interest rate for Country S')\n",
    "    model.param('lambda0N', desc='Parameter in asset demand function, Country N')\n",
    "    model.param('lambda0S', desc='Parameter in asset demand function, Country S')\n",
    "    model.param('lambda1N', desc='Parameter in asset demand function, Country N')\n",
    "    model.param('lambda1S', desc='Parameter in asset demand function, Country S')\n",
    "    model.param('lambda2N', desc='Parameter in asset demand function, Country N')\n",
    "    model.param('lambda2S', desc='Parameter in asset demand function, Country S')\n",
    "    model.param('muN', desc='Import propensity, Country N')\n",
    "    model.param('muS', desc='Import propensity, Country S')\n",
    "    model.param('phiN', desc='Parameter in fiscal policy reaction function, Country N')\n",
    "    model.param('phiS', desc='Parameter in fiscal policy reaction function, Country S')\n",
    "    model.param('thetaN', desc='Tax rate in Country N')\n",
    "    model.param('thetaS', desc='Tax rate in Country S')\n",
    "\n",
    "    model.param('GN', desc='Government expenditure, Region N')\n",
    "    model.param('GS', desc='Government expenditure, Region S')\n",
    "    model.param('Pgbar', desc='Price of gold, set exogenously')\n",
    "    model.param('XRbar', desc='Exchange rate, set exogenously')\n",
    "\n",
    "    model.add('YN = CN + GN + XN - IMN')\n",
    "    model.add('YS = CS + GS + XS - IMS')\n",
    "    model.add('IMN = muN * YN')\n",
    "    model.add('IMS = muS * YS')\n",
    "    model.add('XN = IMS/XR')\n",
    "    model.add('XS = IMN*XR')\n",
    "    model.add('YDN = YN - TN + RN(-1)*BhN(-1)')\n",
    "    model.add('YDS = YS - TS + RS(-1)*BhS(-1)')\n",
    "    model.add('TN = thetaN * (YN + RN(-1)*BhN(-1))')\n",
    "    model.add('TS = thetaS * (YS + RS(-1)*BhS(-1))')\n",
    "    model.add('VN - VN(-1) = YDN - CN')\n",
    "    model.add('VS - VS(-1) = YDS - CS')\n",
    "    model.add('CN = alpha1N*YDN + alpha2N*VN(-1)')\n",
    "    model.add('CS = alpha1S*YDS + alpha2S*VS(-1)')\n",
    "    model.add('HhN = VN - BhN')\n",
    "    model.add('HhS = VS - BhS')\n",
    "    model.add('BhN = VN*(lambda0N + lambda1N*RN - lambda2N*(YDN/VN))')\n",
    "    model.add('BhS = VS*(lambda0S + lambda1S*RS - lambda2S*(YDS/VS))')\n",
    "    model.add('BsN - BsN(-1) = (GN + RN(-1)*BsN(-1)) - (TN + RN(-1)*BcbN(-1))')\n",
    "    model.add('BsS - BsS(-1) = (GS + RS(-1)*BsS(-1)) - (TS + RS(-1)*BcbS(-1))')\n",
    "    model.add('BcbN = BsN - BhN')\n",
    "    model.add('BcbS = BsS - BhS')\n",
    "    model.add('ORN - ORN(-1)= (HsN - HsN(-1) - (BcbN - BcbN(-1)))/PgN')\n",
    "    model.add('ORS - ORS(-1)= (HsS - HsS(-1) - (BcbS - BcbS(-1)))/PgS')\n",
    "    model.add('HsN = HhN')\n",
    "    model.add('HsS = HhS')\n",
    "    model.add('PgN = Pgbar')\n",
    "    model.add('PgS = PgN*XR')\n",
    "    model.add('XR = XRbar')\n",
    "    model.add('RN = RN(-1) - phiN*((ORN(-1) - ORN(-2))*PgN(-1))/ORN(-1)')\n",
    "    model.add('RS = RS(-1) - phiS*((ORS(-1) - ORS(-2))*PgS(-1))/ORS(-1)')\n",
    "    model.add('alpha1N = alpha10N - iotaN*RN(-1)')\n",
    "    model.add('alpha1S = alpha10S - iotaS*RS(-1)')\n",
    "    \n",
    "    return model\n",
    "\n",
    "openm3_parameters = {'alpha10N': 0.6125,\n",
    "                     'alpha10S': 0.7125,\n",
    "                     'alpha2N': 0.4,\n",
    "                     'alpha2S': 0.3,\n",
    "                     'iotaN': 0.5,\n",
    "                     'iotaS': 0.5,\n",
    "                     'lambda0N': 0.635,\n",
    "                     'lambda0S': 0.67,\n",
    "                     'lambda1N': 5,\n",
    "                     'lambda1S': 6,\n",
    "                     'lambda2N': 0.01,\n",
    "                     'lambda2S': 0.07,\n",
    "                     'muN': 0.18781,\n",
    "                     'muS': 0.18781,\n",
    "                     'phiN': 0.005,\n",
    "                     'phiS': 0.005,\n",
    "                     'thetaN': 0.2,\n",
    "                     'thetaS': 0.2}\n",
    "openm3_exogenous = {'Pgbar': 1,\n",
    "                    'GN': 20,\n",
    "                    'GS': 20,\n",
    "                    'XRbar': 1}\n",
    "openm3_variables = {'BcbN': 11.622,\n",
    "                    'BcbS': 11.622,\n",
    "                    'BhN': 64.865,\n",
    "                    'BhS': 64.865,\n",
    "                    'BsN': 76.486,\n",
    "                    'BsS': 76.486,\n",
    "                    'ORN': 10,\n",
    "                    'ORS': 10,\n",
    "                    'VN': 86.487,\n",
    "                    'VS': 86.486,\n",
    "                    'HhN': 86.487 - 64.865,\n",
    "                    'HhS': 86.486 - 64.865,\n",
    "                    'HsN': 86.487 - 64.865,\n",
    "                    'HsS': 86.486 - 64.865,\n",
    "                    'RN': 0.025,\n",
    "                    'RS': 0.025,\n",
    "                    'PgN': 1,\n",
    "                    'PgS': 1,\n",
    "                    'XR': 1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model OPENM3, increase in propensity to import of country S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "muS = create_openm3_model()\n",
    "muS.set_values(openm3_parameters)\n",
    "muS.set_values(openm3_exogenous)\n",
    "muS.set_values(openm3_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(15):\n",
    "    muS.solve(iterations=100, threshold=1e-6)\n",
    "\n",
    "# shock the system\n",
    "muS.set_values({'muS': 0.2})\n",
    "\n",
    "for _ in range(40):\n",
    "    muS.solve(iterations=100, threshold=1e-6)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 6.16"
   ]
  },
  {
   "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 6.16  Evolution of interest rates, following an increase in the South propensity\n",
    "    to import, with interest rates acting on propensities to consume and reacting to changes\n",
    "    in gold reserves'''\n",
    "rndata = [s['RN'] for s in muS.solutions[5:]]\n",
    "rsdata = [s['RS'] for s in muS.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(-0.008, 0.05)\n",
    "\n",
    "axes.plot(rndata, linestyle='-', color='r')\n",
    "axes.plot(rsdata, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(22, 0.044, 'South interest rate')\n",
    "plt.text(32, 0.023, 'North interest rate')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 6.17"
   ]
  },
  {
   "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 6.17  Evolution of trade accounts and government balances, following\n",
    "    an increase in the South propensity to import, with interest rates acting\n",
    "    on propensities to consume and reacting to changes in gold reserves'''\n",
    "tradeNdata = list()\n",
    "tradeSdata = list()\n",
    "govtNdata = list()\n",
    "govtSdata = list()\n",
    "\n",
    "for i in range(6, len(muS.solutions)):\n",
    "    s = muS.solutions[i]\n",
    "    s_1 = muS.solutions[i-1]\n",
    "    tradeNdata.append(s['XN'] - s['IMN'])\n",
    "    tradeSdata.append(s['XS'] - s['IMS'])\n",
    "    govtNdata.append(s['TN'] - (s['GN'] + s['RN']*s_1['BhN']))\n",
    "    govtSdata.append(s['TS'] - (s['GS'] + s['RS']*s_1['BhS']))\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(-1.6, 1.5)\n",
    "\n",
    "axes.plot(tradeNdata, linestyle='-', color='k')\n",
    "axes.plot(govtNdata, linestyle=':', color='r', linewidth=3)\n",
    "axes.plot(tradeSdata, linestyle='--', color='g')\n",
    "axes.plot(govtSdata, linestyle='-.', color='b', linewidth=2)\n",
    "\n",
    "# add labels\n",
    "plt.text(11, 0.8, 'North trade account')\n",
    "plt.text(12.5, 0.2, 'North government')\n",
    "plt.text(12.5, 0.1, 'account')\n",
    "plt.text(33, -0.6, 'South trade account')\n",
    "plt.text(29, -1.2, 'South government account')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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