{
 "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 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 OPENG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_openg_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('GN', desc='Government expenditure, Region N')\n",
    "    model.var('GS', desc='Government expenditure, Region 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.set_param_default(0)\n",
    "    model.param('alpha1N', desc='Propensity to consume out of income, Country N')\n",
    "    model.param('alpha1S', desc='Propensity to consume out of income, Country S')\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('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('Pgbar', desc='Price of gold, set exogenously')\n",
    "    model.param('RbarN', desc='Interest rate on bills set exogenously in Country N')\n",
    "    model.param('RbarS', desc='Interest rate on bills set exogenously in Country S')\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 = RbarN')\n",
    "    model.add('RS = RbarS')\n",
    "    model.add('GN = GN(-1) + phiN*(ORN(-1) - ORN(-2))*PgN(-1)')\n",
    "    model.add('GS = GS(-1) + phiS*(ORS(-1) - ORS(-2))*PgS(-1)')\n",
    "    \n",
    "    return model\n",
    "\n",
    "openg_parameters = {'alpha1N': 0.6,\n",
    "                    'alpha1S': 0.7,\n",
    "                    'alpha2N': 0.4,\n",
    "                    'alpha2S': 0.3,\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': 0.25,\n",
    "                    'phiS': 0.25,\n",
    "                    'thetaN': 0.2,\n",
    "                    'thetaS': 0.2}\n",
    "openg_exogenous = {'Pgbar': 1,\n",
    "                   'RbarN': 0.025,\n",
    "                   'RbarS': 0.025,\n",
    "                   'XRbar': 1}\n",
    "openg_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",
    "                   'GN': 20,\n",
    "                   'GS': 20,\n",
    "                   'ORN': 10,\n",
    "                   'ORS': 10,\n",
    "                   'PgN': 1,\n",
    "                   'PgS': 1,\n",
    "                   'RN': 0.025,\n",
    "                   'RS': 0.025,\n",
    "                   'VN': 86.487,\n",
    "                   'VS': 86.487,\n",
    "                   'HhN': 86.487 - 64.865,\n",
    "                   'HhS': 86.487 - 64.865,\n",
    "                   'HsN': 86.487 - 64.865,\n",
    "                   'HsS': 86.487 - 64.865,\n",
    "                   'XR': 1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model OPENG, increase in propensity to import of country S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "muS = create_openg_model()\n",
    "muS.set_values(openg_parameters)\n",
    "muS.set_values(openg_exogenous)\n",
    "muS.set_values(openg_variables)\n",
    "\n",
    "# run to convergence\n",
    "# Give the system more time to reach a steady state\n",
    "for _ in range(40):\n",
    "    muS.solve(iterations=100, threshold=1e-6)\n",
    "\n",
    "muS.solutions = muS.solutions[25:]\n",
    "\n",
    "# shock the system\n",
    "muS.set_values({'muS': 0.20781})\n",
    "\n",
    "for _ in range(40):\n",
    "    muS.solve(iterations=100, threshold=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 6.12"
   ]
  },
  {
   "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 6.12  Evolution of the balances of the South country - net acquisition\n",
    "    of financial assets by the household sector, government budget balances,\n",
    "    trade balance - following an increase in the South propensity to import, with\n",
    "    fiscal policy reacting to changes in gold reserves'''\n",
    "vsdata = list()\n",
    "govdata = list()\n",
    "tradedata = list()\n",
    "\n",
    "for i in range(5, len(muS.solutions)):\n",
    "    s = muS.solutions[i]\n",
    "    s_1 = muS.solutions[i-1]\n",
    "    vsdata.append(s['VS'] - s_1['VS'])\n",
    "    govdata.append(s['TS'] -(s['GS'] + s['RS']*s_1['BhS']))\n",
    "    tradedata.append(s['XS'] - s['IMS'])\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(-1.3, 0.4)\n",
    "\n",
    "axes.plot(vsdata, linestyle='-', color='r')\n",
    "axes.plot(govdata, linestyle=':', color='b', linewidth=2)\n",
    "axes.plot(tradedata, linestyle='--', color='g')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, -0.3, 'Change in household wealth')\n",
    "plt.text(18, 0.2, 'Trade account')\n",
    "plt.text(13, -.9, 'Government account')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 6.13"
   ]
  },
  {
   "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 6.13  Evolution of GDP in the North and South regions, following an\n",
    "    increase in the South propensity to import, with fiscal policy reacts\n",
    "    in the gold reserves'''\n",
    "yndata = [s['YN'] for s in muS.solutions[5:]]\n",
    "ysdata = [s['YS'] 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=False, right=False)\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.spines['right'].set_visible(False)\n",
    "axes.set_ylim(99, 114)\n",
    "\n",
    "axes.plot(yndata, linestyle='-', color='r')\n",
    "axes.plot(ysdata, linestyle='--', color='b')\n",
    "\n",
    "# add labels\n",
    "plt.text(25, 111, 'North country GDP')\n",
    "plt.text(25, 102, 'South country GDP')\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",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}