{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monetary Economics: Chapter 5"
   ]
  },
  {
   "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",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pysolve.model import Model\n",
    "from pysolve.utils import is_close,round_solution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model LP1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lp1_model():\n",
    "    model = Model()\n",
    "\n",
    "    model.set_var_default(0)\n",
    "    model.var('Bcb', desc='Government bills held by the Central Bank')\n",
    "    model.var('Bd', desc='Demand for government bills')\n",
    "    model.var('Bh', desc='Government bills held by households')\n",
    "    model.var('Bs', desc='Government bills supplied by government')\n",
    "    model.var('BLd', desc='Demand for government bonds')\n",
    "    model.var('BLh', desc='Government bonds held by households')\n",
    "    model.var('BLs', desc='Supply of government bonds')\n",
    "    model.var('CG', desc='Capital gains on bonds')\n",
    "    model.var('CGe', desc='Expected capital gains on bonds')\n",
    "    model.var('C', desc='Consumption')\n",
    "    model.var('ERrbl', desc='Expected rate of return on bonds')\n",
    "    model.var('Hd', desc='Demand for cash')\n",
    "    model.var('Hh', desc='Cash held by households')\n",
    "    model.var('Hs', desc='Cash supplied by the central bank')\n",
    "    model.var('Pbl', desc='Price of bonds')\n",
    "    model.var('Pble', desc='Expected price of bonds')\n",
    "    model.var('Rb', desc='Interest rate on government bills')\n",
    "    model.var('Rbl', desc='Interest rate on government bonds')\n",
    "    model.var('T', desc='Taxes')\n",
    "    model.var('V', desc='Household wealth')\n",
    "    model.var('Ve', desc='Expected household wealth')\n",
    "    model.var('Y', desc='Income = GDP')\n",
    "    model.var('YDr', desc='Regular disposable income of households')\n",
    "    model.var('YDre', desc='Expected regular disposable income of households')\n",
    "\n",
    "    model.set_param_default(0)\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('chi', desc='Weight of conviction in expected bond price')\n",
    "    model.param('lambda10', desc='Parameter in asset demand function')\n",
    "    model.param('lambda12', desc='Parameter in asset demand function')\n",
    "    model.param('lambda13', desc='Parameter in asset demand function')\n",
    "    model.param('lambda14', desc='Parameter in asset demand function')\n",
    "    model.param('lambda20', desc='Parameter in asset demand function')\n",
    "    model.param('lambda22', desc='Parameter in asset demand function')\n",
    "    model.param('lambda23', desc='Parameter in asset demand function')\n",
    "    model.param('lambda24', desc='Parameter in asset demand function')\n",
    "    model.param('lambda30', desc='Parameter in asset demand function')\n",
    "    model.param('lambda32', desc='Parameter in asset demand function')\n",
    "    model.param('lambda33', desc='Parameter in asset demand function')\n",
    "    model.param('lambda34', desc='Parameter in asset demand function')\n",
    "    model.param('theta', desc='Tax rate')\n",
    "\n",
    "    model.param('G', desc='Government goods')\n",
    "    model.param('Rbar', desc='Exogenously set interest rate on govt bills')\n",
    "    model.param('Pblbar', desc='Exogenously set price of bonds')\n",
    "\n",
    "    model.add('Y = C + G')                                  # 5.1\n",
    "    model.add('YDr = Y - T + Rb(-1)*Bh(-1) + BLh(-1)')      # 5.2\n",
    "    model.add('T = theta *(Y + Rb(-1)*Bh(-1) + BLh(-1))')    # 5.3\n",
    "    model.add('V - V(-1) = (YDr - C) + CG')                 # 5.4\n",
    "    model.add('CG = (Pbl - Pbl(-1))*BLh(-1)')\n",
    "    model.add('C = alpha1*YDre + alpha2*V(-1)')\n",
    "    model.add('Ve = V(-1) + (YDre - C) + CG')\n",
    "    model.add('Hh = V - Bh - Pbl*BLh')\n",
    "    model.add('Hd = Ve - Bd - Pbl*BLd')\n",
    "    model.add('Bd = Ve*lambda20 + Ve*lambda22*Rb' +\n",
    "              '- Ve*lambda23*ERrbl - lambda24*YDre')\n",
    "    model.add('BLd = (Ve*lambda30 - Ve*lambda32*Rb ' +\n",
    "              '+ Ve*lambda33*ERrbl - lambda34*YDre)/Pbl')\n",
    "    model.add('Bh = Bd')\n",
    "    model.add('BLh = BLd')\n",
    "    model.add('Bs - Bs(-1) = (G + Rb(-1)*Bs(-1) + ' +\n",
    "              'BLs(-1)) - (T + Rb(-1)*Bcb(-1)) - (BLs - BLs(-1))*Pbl')\n",
    "    model.add('Hs - Hs(-1) = Bcb - Bcb(-1)')\n",
    "    model.add('Bcb = Bs - Bh')\n",
    "    model.add('BLs = BLh')\n",
    "    model.add('ERrbl = Rbl + chi * (Pble - Pbl) / Pbl')\n",
    "    model.add('Rbl = 1./Pbl')\n",
    "    model.add('Pble = Pbl')\n",
    "    model.add('CGe = chi * (Pble - Pbl)*BLh')\n",
    "    model.add('YDre = YDr(-1)')\n",
    "    model.add('Rb = Rbar')\n",
    "    model.add('Pbl = Pblbar')\n",
    "\n",
    "    return model\n",
    "\n",
    "lp1_parameters = {'alpha1': 0.8,\n",
    "                  'alpha2': 0.2,\n",
    "                  'chi': 0.1,\n",
    "                  'lambda20': 0.44196,\n",
    "                  'lambda22': 1.1,\n",
    "                  'lambda23': 1,\n",
    "                  'lambda24': 0.03,\n",
    "                  'lambda30': 0.3997,\n",
    "                  'lambda32': 1,\n",
    "                  'lambda33': 1.1,\n",
    "                  'lambda34': 0.03,\n",
    "                  'theta': 0.1938}\n",
    "lp1_exogenous = {'G': 20,\n",
    "                 'Rbar': 0.03,\n",
    "                 'Pblbar': 20}\n",
    "lp1_variables = {'V': 95.803,\n",
    "                 'Bh': 37.839,\n",
    "                 'Bs': 57.964,\n",
    "                 'Bcb': 57.964 - 37.839,\n",
    "                 'BLh': 1.892,\n",
    "                 'BLs': 1.892,\n",
    "                 'Hs': 20.125,\n",
    "                 'YDr': 95.803,\n",
    "                 'Rb': 0.03,\n",
    "                 'Pbl': 20}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Interest rate shock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lp1 = create_lp1_model()\n",
    "lp1.set_values(lp1_parameters)\n",
    "lp1.set_values(lp1_exogenous)\n",
    "lp1.set_values(lp1_variables)\n",
    "\n",
    "for _ in range(15):\n",
    "    lp1.solve(iterations=100, threshold=1e-6)\n",
    "\n",
    "# shock the system\n",
    "lp1.set_values({'Rbar': 0.04,\n",
    "                'Pblbar': 15})\n",
    "for _ in range(45):\n",
    "    lp1.solve(iterations=100, threshold=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 5.2  Evolution of the wealth to disposable income ratio, following an increase\n",
    "    in both the short-term and long-term interest rates, with model LP1'''\n",
    "data = [s['V']/s['YDr'] for s in lp1.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(0.89, 1.01)\n",
    "\n",
    "axes.plot(data, 'k')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, 0.98, 'Wealth to disposable income ratio')\n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.3"
   ]
  },
  {
   "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 5.3  Evolution of the wealth to disposable income ratio, following an increase\n",
    "    in both the short-term and long-term interest rates, with model LP1'''\n",
    "ydrdata = [s['YDr'] for s in lp1.solutions[5:]]\n",
    "cdata = [s['C'] for s in lp1.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(92.5, 101.5)\n",
    "\n",
    "axes.plot(ydrdata, 'k')\n",
    "axes.plot(cdata, linestyle='--', color='r')\n",
    "\n",
    "# add labels\n",
    "plt.text(16, 98, 'Disposable')\n",
    "plt.text(16, 97.6, 'income')\n",
    "plt.text(22, 95, 'Consumption')\n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.4"
   ]
  },
  {
   "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 5.4  Evolution of the bonds to wealth ration and the bills to wealth ratio,\n",
    "    following an increase from 3% to 4% in the short-term interest rate, while the\n",
    "    long-term interest rates moves from 5% to 6.67%, with model LP1'''\n",
    "bhdata = [s['Bh']/s['V'] for s in lp1.solutions[5:]]\n",
    "pdata = [s['Pbl']*s['BLh']/s['V'] for s in lp1.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(0.382, 0.408)\n",
    "\n",
    "axes.plot(bhdata, 'k')\n",
    "axes.plot(pdata, linestyle='--', color='r')\n",
    "\n",
    "# add labels\n",
    "plt.text(14, 0.3978, 'Bonds to wealth ratio')\n",
    "plt.text(17, 0.39, 'Bills to wealth ratio')\n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model LP2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lp2_model():\n",
    "    model = Model()\n",
    "\n",
    "    model.set_var_default(0)\n",
    "    model.var('Bcb', desc='Government bills held by the Central Bank')\n",
    "    model.var('Bd', desc='Demand for government bills')\n",
    "    model.var('Bh', desc='Government bills held by households')\n",
    "    model.var('Bs', desc='Government bills supplied by government')\n",
    "    model.var('BLd', desc='Demand for government bonds')\n",
    "    model.var('BLh', desc='Government bonds held by households')\n",
    "    model.var('BLs', desc='Supply of government bonds')\n",
    "    model.var('CG', desc='Capital gains on bonds')\n",
    "    model.var('CGe', desc='Expected capital gains on bonds')\n",
    "    model.var('C', desc='Consumption')\n",
    "    model.var('ERrbl', desc='Expected rate of return on bonds')\n",
    "    model.var('Hd', desc='Demand for cash')\n",
    "    model.var('Hh', desc='Cash held by households')\n",
    "    model.var('Hs', desc='Cash supplied by the central bank')\n",
    "    model.var('Pbl', desc='Price of bonds')\n",
    "    model.var('Pble', desc='Expected price of bonds')\n",
    "    model.var('Rb', desc='Interest rate on government bills')\n",
    "    model.var('Rbl', desc='Interest rate on government bonds')\n",
    "    model.var('T', desc='Taxes')\n",
    "    model.var('TP', desc='Target proportion in households portfolio')\n",
    "    model.var('V', desc='Household wealth')\n",
    "    model.var('Ve', desc='Expected household wealth')\n",
    "    model.var('Y', desc='Income = GDP')\n",
    "    model.var('YDr', desc='Regular disposable income of households')\n",
    "    model.var('YDre', desc='Expected regular disposable income of households')\n",
    "    model.var('z1', desc='Switch parameter')\n",
    "    model.var('z2', desc='Switch parameter')\n",
    "\n",
    "    model.set_param_default(0)\n",
    "    model.param('add', desc='Random shock to expectations')\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='Adjustment parameter in price of bills')\n",
    "    model.param('betae', desc='Adjustment parameter in expectations')\n",
    "    model.param('bot', desc='Bottom value for TP')\n",
    "    model.param('chi', desc='Weight of conviction in expected bond price')\n",
    "    model.param('lambda10', desc='Parameter in asset demand function')\n",
    "    model.param('lambda12', desc='Parameter in asset demand function')\n",
    "    model.param('lambda13', desc='Parameter in asset demand function')\n",
    "    model.param('lambda14', desc='Parameter in asset demand function')\n",
    "    model.param('lambda20', desc='Parameter in asset demand function')\n",
    "    model.param('lambda22', desc='Parameter in asset demand function')\n",
    "    model.param('lambda23', desc='Parameter in asset demand function')\n",
    "    model.param('lambda24', desc='Parameter in asset demand function')\n",
    "    model.param('lambda30', desc='Parameter in asset demand function')\n",
    "    model.param('lambda32', desc='Parameter in asset demand function')\n",
    "    model.param('lambda33', desc='Parameter in asset demand function')\n",
    "    model.param('lambda34', desc='Parameter in asset demand function')\n",
    "    model.param('theta', desc='Tax rate')\n",
    "    model.param('top', desc='Top value for TP')\n",
    "\n",
    "    model.param('G', desc='Government goods')\n",
    "    model.param('Pblbar', desc='Exogenously set price of bonds')\n",
    "    model.param('Rbar', desc='Exogenously set interest rate on govt bills')\n",
    "\n",
    "    model.add('Y = C + G')                                  # 5.1\n",
    "    model.add('YDr = Y - T + Rb(-1)*Bh(-1) + BLh(-1)')      # 5.2\n",
    "    model.add('T = theta *(Y + Rb(-1)*Bh(-1) + BLh(-1))')    # 5.3\n",
    "    model.add('V - V(-1) = (YDr - C) + CG')                 # 5.4\n",
    "    model.add('CG = (Pbl - Pbl(-1))*BLh(-1)')\n",
    "    model.add('C = alpha1*YDre + alpha2*V(-1)')\n",
    "    model.add('Ve = V(-1) + (YDre - C) + CG')\n",
    "    model.add('Hh = V - Bh - Pbl*BLh')\n",
    "    model.add('Hd = Ve - Bd - Pbl*BLd')\n",
    "    model.add('Bd = Ve*lambda20 + Ve*lambda22*Rb' +\n",
    "              '- Ve*lambda23*ERrbl - lambda24*YDre')\n",
    "    model.add('BLd = (Ve*lambda30 - Ve*lambda32*Rb ' +\n",
    "              '+ Ve*lambda33*ERrbl - lambda34*YDre)/Pbl')\n",
    "    model.add('Bh = Bd')\n",
    "    model.add('BLh = BLd')\n",
    "    model.add('Bs - Bs(-1) = (G + Rb(-1)*Bs(-1) + BLs(-1))' +\n",
    "              ' - (T + Rb(-1)*Bcb(-1)) - Pbl*(BLs - BLs(-1))')\n",
    "    model.add('Hs - Hs(-1) = Bcb - Bcb(-1)')\n",
    "    model.add('Bcb = Bs - Bh')\n",
    "    model.add('BLs = BLh')\n",
    "    model.add('ERrbl = Rbl + ((chi * (Pble - Pbl))/ Pbl)')\n",
    "    model.add('Rbl = 1./Pbl')\n",
    "    model.add('Pble = Pble(-1) - betae*(Pble(-1) - Pbl) + add')\n",
    "    model.add('CGe = chi * (Pble - Pbl)*BLh')\n",
    "    model.add('YDre = YDr(-1)')\n",
    "    model.add('Rb = Rbar')\n",
    "    model.add('Pbl = (1 + z1*beta - z2*beta)*Pbl(-1)')\n",
    "    model.add('z1 = if_true(TP > top)')\n",
    "    model.add('z2 = if_true(TP < bot)')\n",
    "    model.add('TP = (BLh(-1)*Pbl(-1))/(BLh(-1)*Pbl(-1) + Bh(-1))')\n",
    "\n",
    "    return model\n",
    "\n",
    "lp2_parameters = {'alpha1': 0.8,\n",
    "                  'alpha2': 0.2,\n",
    "                  'beta': 0.01,\n",
    "                  'betae': 0.5,\n",
    "                  'chi': 0.1,\n",
    "                  'lambda20': 0.44196,\n",
    "                  'lambda22': 1.1,\n",
    "                  'lambda23': 1,\n",
    "                  'lambda24': 0.03,\n",
    "                  'lambda30': 0.3997,\n",
    "                  'lambda32': 1,\n",
    "                  'lambda33': 1.1,\n",
    "                  'lambda34': 0.03,\n",
    "                  'theta': 0.1938,\n",
    "                  'bot': 0.495,\n",
    "                  'top': 0.505 }\n",
    "lp2_exogenous = {'G': 20,\n",
    "                 'Rbar': 0.03,\n",
    "                 'Pblbar': 20,\n",
    "                 'add': 0}\n",
    "lp2_variables = {'V': 95.803,\n",
    "                 'Bh': 37.839,\n",
    "                 'Bs': 57.964,\n",
    "                 'Bcb': 57.964 - 37.839,\n",
    "                 'BLh': 1.892,\n",
    "                 'BLs': 1.892,\n",
    "                 'Hs': 20.125,\n",
    "                 'YDr': 95.803,\n",
    "                 'Rb': 0.03,\n",
    "                 'Pbl': 20,\n",
    "                 'Pble': 20,\n",
    "                 'TP': 1.892*20/(1.892*20+37.839), # BLh*Pbl/(BLh*Pbl+Bh)\n",
    "                 'z1': 0,\n",
    "                 'z2': 0}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: interest rate shock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lp2_bill = create_lp2_model()\n",
    "\n",
    "lp2_bill.set_values(lp2_parameters)\n",
    "lp2_bill.set_values(lp2_exogenous)\n",
    "lp2_bill.set_values(lp2_variables)\n",
    "\n",
    "lp2_bill.set_values({'z1': lp2_bill.evaluate('if_true(TP > top)'),\n",
    "                     'z2': lp2_bill.evaluate('if_true(TP < bot)')})\n",
    "\n",
    "for _ in range(10):\n",
    "    lp2_bill.solve(iterations=100, threshold=1e-4)\n",
    "\n",
    "# shock the system\n",
    "lp2_bill.set_values({'Rbar': 0.035})\n",
    "\n",
    "for _ in range(45):\n",
    "    lp2_bill.solve(iterations=100, threshold=1e-4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 5.5  Evolution of the long-term interest rate (the bond yield), following an\n",
    "    increase in the short-term interest rate (the bill rate), as a result of the response of\n",
    "    the central bank and the Treasury, with Model LP2.'''\n",
    "rbdata = [s['Rb'] for s in lp2_bill.solutions[5:]]\n",
    "pbldata = [1./s['Pbl'] for s in lp2_bill.solutions[5:]]\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.1, 0.9])\n",
    "axes.tick_params(top='off', right='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.set_ylim(0.029, 0.036)\n",
    "axes.plot(rbdata, linestyle='--', color='r')\n",
    "\n",
    "\n",
    "axes2 = axes.twinx()\n",
    "axes2.spines['top'].set_visible(False)\n",
    "axes2.set_ylim(0.0495, 0.052)\n",
    "\n",
    "axes2.plot(pbldata, 'k')\n",
    "\n",
    "# add labels\n",
    "plt.text(12, 0.0518, 'Short-term interest rate')\n",
    "plt.text(15, 0.0513, 'Long-term interest rate')\n",
    "fig.text(0.05, 1.05, 'Bill rate')\n",
    "fig.text(1.15, 1.05, 'Bond yield')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 5.6  Evolution of the target proportion (TP), that is the share of bonds in the\n",
    "    government debt held by households, following an increase in the short-term interest\n",
    "    rate (the bill rate) and the response of the central bank and of the Treasury, \n",
    "    with Model LP2'''\n",
    "tpdata = [s['TP'] for s in lp2_bill.solutions[5:]]\n",
    "topdata = [s['top'] for s in lp2_bill.solutions[5:]]\n",
    "botdata = [s['bot'] for s in lp2_bill.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.set_ylim(0.490, 0.506)\n",
    "\n",
    "axes.plot(topdata, color='k')\n",
    "axes.plot(botdata, color='k')\n",
    "axes.plot(tpdata, linestyle='--', color='r')\n",
    "\n",
    "# add labels\n",
    "plt.text(30, 0.5055, 'Ceiling of target range')\n",
    "plt.text(30, 0.494, 'Floor of target range')\n",
    "plt.text(10, 0.493, 'Share of bonds')\n",
    "plt.text(10, 0.4922, 'in government debt')\n",
    "plt.text(10, 0.4914, 'held by households')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Shock to the bond price expectations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lp2_bond = create_lp2_model()\n",
    "\n",
    "lp2_bond.set_values(lp2_parameters)\n",
    "lp2_bond.set_values(lp2_exogenous)\n",
    "lp2_bond.set_values(lp2_variables)\n",
    "lp2_bond.set_values({'z1': 'if_true(TP > top)',\n",
    "                     'z2': 'if_true(TP < bot)'})\n",
    "\n",
    "for _ in range(10):\n",
    "    lp2_bond.solve(iterations=100, threshold=1e-5)\n",
    "    \n",
    "# shock the system\n",
    "lp2_bond.set_values({'add': -3})\n",
    "lp2_bond.solve(iterations=100, threshold=1e-5)\n",
    "lp2_bond.set_values({'add': 0})\n",
    "\n",
    "for _ in range(43):\n",
    "    lp2_bond.solve(iterations=100, threshold=1e-4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 5.7  Evolution of the long-term interest rate, following an anticipated fall in\n",
    "    the price of bonds, as a consequence of the response of the central bank and of the\n",
    "    Treasury, with Model LP2'''\n",
    "pbldata = [1./s['Pbl'] for s in lp2_bond.solutions[5:]]\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 0.9, 0.9])\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(0.0497, 0.0512)\n",
    "\n",
    "axes.plot(pbldata, linestyle='--', color='k')\n",
    "\n",
    "# add labels\n",
    "plt.text(15, 0.0509, 'Long-term interest rate')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 5.8  Evolution of the expected and actual bond prices, following an anticipated\n",
    "    fall in the price of bonds, as a consequence of the response of the central bank and of\n",
    "    the Treasury, with Model LP2'''\n",
    "pbldata = [s['Pbl'] for s in lp2_bond.solutions[5:]]\n",
    "pbledata = [s['Pble'] for s in lp2_bond.solutions[5:]]\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 0.9, 0.9])\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(16.5, 21)\n",
    "\n",
    "axes.plot(pbldata, linestyle='--', color='k')\n",
    "axes.plot(pbledata, linestyle='-', color='r')\n",
    "\n",
    "# add labels\n",
    "plt.text(8, 20, 'Actual price of bonds')\n",
    "plt.text(10, 19, 'Expected price of bonds')\n",
    "fig.text(0.1, -.1, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 5.9  Evolution of the target proportion (TP), that is the share of bonds in the\n",
    "    government debt held by households, following an anticipated fall in the price of\n",
    "    bonds, as a consequence of the response of the central bank and of the Treasury, with\n",
    "    Model LP2'''\n",
    "tpdata = [s['TP'] for s in lp2_bond.solutions[5:]]\n",
    "botdata = [s['top'] for s in lp2_bond.solutions[5:]]\n",
    "topdata = [s['bot'] for s in lp2_bond.solutions[5:]]\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 0.9, 0.9])\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(0.47, 0.52)\n",
    "\n",
    "axes.plot(tpdata, linestyle='--', color='r')\n",
    "axes.plot(botdata, linestyle='-', color='k')\n",
    "axes.plot(topdata, linestyle='-', color='k')\n",
    "\n",
    "# add labels\n",
    "plt.text(30, 0.508, 'Ceiling of target range')\n",
    "plt.text(30, 0.491, 'Floor of target range')\n",
    "plt.text(10, 0.49, 'Share of bonds in')\n",
    "plt.text(10, 0.487, 'government debt')\n",
    "plt.text(10, 0.484, 'held by households')\n",
    "fig.text(0.1, -.15, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: Model LP1, propensity to consume shock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "lp1_alpha = create_lp1_model()\n",
    "lp1_alpha.set_values(lp1_parameters)\n",
    "lp1_alpha.set_values(lp1_exogenous)\n",
    "lp1_alpha.set_values(lp1_variables)\n",
    "\n",
    "for _ in range(10):\n",
    "    lp1_alpha.solve(iterations=100, threshold=1e-6)\n",
    "\n",
    "# shock the system\n",
    "lp1_alpha.set_values({'alpha1': 0.7})\n",
    "\n",
    "for _ in range(45):\n",
    "    lp1_alpha.solve(iterations=100, threshold=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model LP3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lp3_model():\n",
    "    model = Model()\n",
    "\n",
    "    model.set_var_default(0)\n",
    "    model.var('Bcb', desc='Government bills held by the Central Bank')\n",
    "    model.var('Bd', desc='Demand for government bills')\n",
    "    model.var('Bh', desc='Government bills held by households')\n",
    "    model.var('Bs', desc='Government bills supplied by government')\n",
    "    model.var('BLd', desc='Demand for government bonds')\n",
    "    model.var('BLh', desc='Government bonds held by households')\n",
    "    model.var('BLs', desc='Supply of government bonds')\n",
    "    model.var('CG', desc='Capital gains on bonds')\n",
    "    model.var('CGe', desc='Expected capital gains on bonds')\n",
    "    model.var('C', desc='Consumption')\n",
    "    model.var('ERrbl', desc='Expected rate of return on bonds')\n",
    "    model.var('Hd', desc='Demand for cash')\n",
    "    model.var('Hh', desc='Cash held by households')\n",
    "    model.var('Hs', desc='Cash supplied by the central bank')\n",
    "    model.var('Pbl', desc='Price of bonds')\n",
    "    model.var('Pble', desc='Expected price of bonds')\n",
    "    model.var('PSBR', desc='Public sector borrowing requirement (PSBR)')\n",
    "    model.var('Rb', desc='Interest rate on government bills')\n",
    "    model.var('Rbl', desc='Interest rate on government bonds')\n",
    "    model.var('T', desc='Taxes')\n",
    "    model.var('TP', desc='Target proportion in households portfolio')\n",
    "    model.var('V', desc='Household wealth')\n",
    "    model.var('Ve', desc='Expected household wealth')\n",
    "    model.var('Y', desc='Income = GDP')\n",
    "    model.var('YDr', desc='Regular disposable income of households')\n",
    "    model.var('YDre', desc='Expected regular disposable income of households')\n",
    "    model.var('z1', desc='Switch parameter')\n",
    "    model.var('z2', desc='Switch parameter')\n",
    "    model.var('z3', desc='Switch parameter')\n",
    "    model.var('z4', desc='Switch parameter')\n",
    "\n",
    "    # no longer exogenous\n",
    "    model.var('G', desc='Government goods')\n",
    "\n",
    "    model.set_param_default(0)\n",
    "    model.param('add', desc='Random shock to expectations')\n",
    "    model.param('add2', desc='Addition to the government expenditure setting rule')\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='Adjustment parameter in price of bills')\n",
    "    model.param('betae', desc='Adjustment parameter in expectations')\n",
    "    model.param('bot', desc='Bottom value for TP')\n",
    "    model.param('chi', desc='Weight of conviction in expected bond price')\n",
    "    model.param('lambda10', desc='Parameter in asset demand function')\n",
    "    model.param('lambda12', desc='Parameter in asset demand function')\n",
    "    model.param('lambda13', desc='Parameter in asset demand function')\n",
    "    model.param('lambda14', desc='Parameter in asset demand function')\n",
    "    model.param('lambda20', desc='Parameter in asset demand function')\n",
    "    model.param('lambda22', desc='Parameter in asset demand function')\n",
    "    model.param('lambda23', desc='Parameter in asset demand function')\n",
    "    model.param('lambda24', desc='Parameter in asset demand function')\n",
    "    model.param('lambda30', desc='Parameter in asset demand function')\n",
    "    model.param('lambda32', desc='Parameter in asset demand function')\n",
    "    model.param('lambda33', desc='Parameter in asset demand function')\n",
    "    model.param('lambda34', desc='Parameter in asset demand function')\n",
    "    model.param('theta', desc='Tax rate')\n",
    "    model.param('top', desc='Top value for TP')\n",
    "\n",
    "    model.param('Pblbar', desc='Exogenously set price of bonds')\n",
    "    model.param('Rbar', desc='Exogenously set interest rate on govt bills')\n",
    "\n",
    "    model.add('Y = C + G')                                  # 5.1\n",
    "    model.add('YDr = Y - T + Rb(-1)*Bh(-1) + BLh(-1)')      # 5.2\n",
    "    model.add('T = theta *(Y + Rb(-1)*Bh(-1) + BLh(-1))')    # 5.3\n",
    "    model.add('V - V(-1) = (YDr - C) + CG')                 # 5.4\n",
    "    model.add('CG = (Pbl - Pbl(-1))*BLh(-1)')\n",
    "    model.add('C = alpha1*YDre + alpha2*V(-1)')\n",
    "    model.add('Ve = V(-1) + (YDre - C) + CG')\n",
    "    model.add('Hh = V - Bh - Pbl*BLh')\n",
    "    model.add('Hd = Ve - Bd - Pbl*BLd')\n",
    "    model.add('Bd = Ve*lambda20 + Ve*lambda22*Rb' +\n",
    "              '- Ve*lambda23*ERrbl - lambda24*YDre')\n",
    "    model.add('BLd = (Ve*lambda30 - Ve*lambda32*Rb ' +\n",
    "              '+ Ve*lambda33*ERrbl - lambda34*YDre)/Pbl')\n",
    "    model.add('Bh = Bd')\n",
    "    model.add('BLh = BLd')\n",
    "    model.add('Bs - Bs(-1) = (G + Rb(-1)*Bs(-1) + BLs(-1))' +\n",
    "              ' - (T + Rb(-1)*Bcb(-1)) - Pbl*(BLs - BLs(-1))')\n",
    "    model.add('Hs - Hs(-1) = Bcb - Bcb(-1)')\n",
    "    model.add('Bcb = Bs - Bh')\n",
    "    model.add('BLs = BLh')\n",
    "    model.add('ERrbl = Rbl + ((chi * (Pble - Pbl))/ Pbl)')\n",
    "    model.add('Rbl = 1./Pbl')\n",
    "    model.add('Pble = Pble(-1) - betae*(Pble(-1) - Pbl) + add')\n",
    "    model.add('CGe = chi * (Pble - Pbl)*BLh')\n",
    "    model.add('YDre = YDr(-1)')\n",
    "    model.add('Rb = Rbar')\n",
    "    model.add('Pbl = (1 + z1*beta - z2*beta)*Pbl(-1)')\n",
    "    model.add('z1 = if_true(TP > top)')\n",
    "    model.add('z2 = if_true(TP < bot)')\n",
    "    model.add('TP = (BLh(-1)*Pbl(-1))/(BLh(-1)*Pbl(-1) + Bh(-1))')\n",
    "    model.add('PSBR = (G + Rb*Bs(-1) + BLs(-1)) - (T + Rb*Bcb(-1))')\n",
    "    model.add('z3 = if_true((PSBR(-1)/Y(-1)) > 0.03)')\n",
    "    model.add('z4 = if_true((PSBR(-1)/Y(-1)) < -0.03)')\n",
    "    model.add('G = G(-1) - (z3 + z4)*PSBR(-1) + add2')\n",
    "\n",
    "    return model\n",
    "\n",
    "lp3_parameters = {'alpha1': 0.8,\n",
    "                  'alpha2': 0.2,\n",
    "                  'beta': 0.01,\n",
    "                  'betae': 0.5,\n",
    "                  'chi': 0.1,\n",
    "                  'lambda20': 0.44196,\n",
    "                  'lambda22': 1.1,\n",
    "                  'lambda23': 1,\n",
    "                  'lambda24': 0.03,\n",
    "                  'lambda30': 0.3997,\n",
    "                  'lambda32': 1,\n",
    "                  'lambda33': 1.1,\n",
    "                  'lambda34': 0.03,\n",
    "                  'theta': 0.1938,\n",
    "                  'bot': 0.495,\n",
    "                  'top': 0.505 }\n",
    "lp3_exogenous = {'Rbar': 0.03,\n",
    "                 'Pblbar': 20,\n",
    "                 'add': 0,\n",
    "                 'add2': 0}\n",
    "lp3_variables = {'G': 20,\n",
    "                 'V': 95.803,\n",
    "                 'Bh': 37.839,\n",
    "                 'Bs': 57.964,\n",
    "                 'Bcb': 57.964 - 37.839,\n",
    "                 'BLh': 1.892,\n",
    "                 'BLs': 1.892,\n",
    "                 'Hs': 20.125,\n",
    "                 'YDr': 95.803,\n",
    "                 'Rb': 0.03,\n",
    "                 'Pbl': 20,\n",
    "                 'Pble': 20,\n",
    "                 'PSBR': 0,\n",
    "                 'Y': 115.8,\n",
    "                 'TP': 1.892*20/(1.892*20+37.839), # BLh*Pbl/(BLh*Pbl+Bh)\n",
    "                 'z1': 0,\n",
    "                 'z2': 0,\n",
    "                 'z3': 0,\n",
    "                 'z4': 0}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scenario: LP3, decrease in propensity to consume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "lp3_alpha = create_lp3_model()\n",
    "lp3_alpha.set_values(lp3_parameters)\n",
    "lp3_alpha.set_values(lp3_exogenous)\n",
    "lp3_alpha.set_values(lp3_variables)\n",
    "\n",
    "for _ in range(10):\n",
    "    lp3_alpha.solve(iterations=100, threshold=1e-6)\n",
    "\n",
    "# shock the system\n",
    "lp3_alpha.set_values({'alpha1': 0.7})\n",
    "\n",
    "for _ in range(45):\n",
    "    lp3_alpha.solve(iterations=100, threshold=1e-6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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 5.10  Evolution of national income (GDP), following a sharp decrease in the\n",
    "    propensity to consume out of current income, with Model LP1'''\n",
    "ydata = [s['Y'] for s in lp1_alpha.solutions[5:]]\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 0.9, 0.9])\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(90, 128)\n",
    "\n",
    "axes.plot(ydata, linestyle='--', color='k')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, 110, 'Gross Domestic Product')\n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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 5.11  Evolution of national income (GDP), following a sharp decrease in the\n",
    "    propensity to consume out of current income, with Model LP3'''\n",
    "ydata = [s['Y'] for s in lp3_alpha.solutions[5:]]\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 0.9, 0.9])\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(90, 128)\n",
    "\n",
    "axes.plot(ydata, linestyle='--', color='k')\n",
    "\n",
    "# add labels\n",
    "plt.text(20, 110, 'Gross Domestic Product')\n",
    "fig.text(0.1, -.05, caption);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Figure 5.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "caption = '''\n",
    "    Figure 5.12  Evolution of pure government expenditures and of the government deficit\n",
    "    to national income ratio (the PSBR to GDP ratio), following a sharp decrease in the\n",
    "    propensity to consume out of current income, with Model LP3'''\n",
    "gdata = [s['G'] for s in lp3_alpha.solutions[5:]]\n",
    "ratiodata = [s['PSBR']/s['Y'] for s in lp3_alpha.solutions[5:]]\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 0.9, 0.9])\n",
    "axes.tick_params(top='off')\n",
    "axes.spines['top'].set_visible(False)\n",
    "axes.set_ylim(16, 20.5)\n",
    "axes.plot(gdata, linestyle='--', color='r')\n",
    "\n",
    "plt.text(5, 20.4, 'Pure government')\n",
    "plt.text(5, 20.15, 'expenditures (LHS)')\n",
    "plt.text(30, 18, 'Deficit to national')\n",
    "plt.text(30, 17.75, 'income ration (RHS)')\n",
    "\n",
    "axes2 = axes.twinx()\n",
    "axes2.tick_params(top='off')\n",
    "axes2.spines['top'].set_visible(False)\n",
    "axes2.set_ylim(-.01, 0.04)\n",
    "axes2.plot(ratiodata, linestyle='-', color='b')\n",
    "\n",
    "# add labels\n",
    "fig.text(0.1, 1.05, 'G')\n",
    "fig.text(0.9, 1.05, 'PSBR to Y ratio')\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
}