{
 "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"
   ]
  },
  {
   "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": {
      "needs_background": "light"
     },
     "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='off', right='off')\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": {
      "needs_background": "light"
     },
     "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='off', right='off')\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 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}