{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Monetary Economics: Chapter 3, Model SIMEX" ] }, { "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": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def create_simex_model():\n", " model = Model()\n", "\n", " model.set_var_default(0)\n", " model.var('Cd', desc='Consumption goods demand by households')\n", " model.var('Cs', desc='Consumption goods supply')\n", " model.var('Gs', desc='Government goods, supply')\n", " model.var('Hd', desc='Cash money demanded by households')\n", " model.var('Hh', desc='Cash money held by households')\n", " model.var('Hs', desc='Cash money supplied by the government')\n", " model.var('Nd', desc='Demand for labor')\n", " model.var('Ns', desc='Supply of labor')\n", " model.var('Td', desc='Taxes, demand')\n", " model.var('Ts', desc='Taxes, supply')\n", " model.var('Y', desc='Income = GDP')\n", " model.var('YD', desc='Disposable income of households')\n", " model.var('YDe', desc='Expected disposable income')\n", "\n", " model.set_param_default(0)\n", " model.param('Gd', desc='Government goods, demand')\n", " model.param('W', desc='Wage rate')\n", " model.param('alpha1', desc='Propensity to consume out of income')\n", " model.param('alpha2', desc='Propensity to consume o of wealth')\n", " model.param('theta', desc='Tax rate')\n", "\n", " model.add('Cs = Cd') # 3.1\n", " model.add('Gs = Gd') # 3.2\n", " model.add('Ts = Td') # 3.3\n", " model.add('Ns = Nd') # 3.4\n", " model.add('YD = (W*Ns) - Ts') # 3.5\n", " model.add('Td = theta * W * Ns') # 3.6, theta < 1.0\n", " model.add('Cd = alpha1*YDe + alpha2*Hh(-1)') # 3.7E, 0 < alpha2 < alpha1 < 1\n", " model.add('Hs - Hs(-1) = Gd - Td') # 3.8\n", " model.add('Hh - Hh(-1) = YD - Cd') # 3.9\n", " model.add('Hd - Hs(-1) = YDe - Cd') # 3.18\n", " model.add('Y = Cs + Gs') # 3.10\n", " model.add('Nd = Y/W') # 3.11\n", " model.add('YDe = YD(-1)') # 3.20\n", " \n", " return model\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Steady state solution" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "steady_state = create_simex_model()\n", "steady_state.set_values({'alpha1': 0.6,\n", " 'alpha2': 0.4,\n", " 'theta': 0.2,\n", " 'Gd': 20,\n", " 'W': 1})\n", "\n", "# Set the value so that YD(-1) gets calculated correctly\n", "steady_state.variables['YD'].value = steady_state.evaluate('Gd*(1-theta)')\n", "steady_state.variables['YD'].default = steady_state.evaluate('Gd*(1-theta)')\n", "\n", "for _ in range(100):\n", " steady_state.solve(iterations=100, threshold=1e-5)\n", "\n", " if is_close(steady_state.solutions[-2], steady_state.solutions[-1], atol=1e-4):\n", " break" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Period123
G20.020.020.020.0
Y0.029.639.8100.0
T0.05.98.020.0
YD16.023.731.980.0
YDe0.016.023.780.0
C0.09.619.880.0
ΔHs0.014.112.00.0
ΔHh0.014.112.00.0
H0.014.126.180.0
ΔHd0.06.411.50.0
Hd0.06.417.980.0
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "import numpy\n", "from pysolve.utils import generate_html_table\n", "\n", "data = list()\n", "for var in [('Gd', 'G'), ('Y', 'Y'), ('Ts', 'T'), ('YD', 'YD'), \n", " ('YDe', 'YDe'), ('Cs', 'C')]:\n", " rowdata = list()\n", " rowdata.append(var[1])\n", " for i in [0, 1, 2, -1]:\n", " rowdata.append(str(numpy.round(steady_state.solutions[i][var[0]], decimals=1)))\n", " data.append(rowdata)\n", "\n", "for var in [('Hs', 'ΔHs'), ('Hh', 'ΔHh')]:\n", " rowdata = list()\n", " rowdata.append(var[1])\n", " rowdata.append(str(numpy.round(steady_state.solutions[0][var[0]], decimals=1)))\n", " for i in [1, 2, -1]:\n", " rowdata.append(str(numpy.round(steady_state.solutions[i][var[0]] - \n", " steady_state.solutions[i-1][var[0]], decimals=1)))\n", " data.append(rowdata)\n", "\n", "for var in [('Hh', 'H')]:\n", " rowdata = list()\n", " rowdata.append(var[1])\n", " for i in [0, 1, 2, -1]:\n", " rowdata.append(str(numpy.round(steady_state.solutions[i][var[0]], decimals=1)))\n", " data.append(rowdata)\n", "\n", "for var in [('Hd', 'ΔHd')]:\n", " rowdata = list()\n", " rowdata.append(var[1])\n", " rowdata.append(str(numpy.round(steady_state.solutions[0][var[0]], decimals=1)))\n", " for i in [1, 2, -1]:\n", " rowdata.append(str(numpy.round(steady_state.solutions[i][var[0]] - \n", " steady_state.solutions[i-1][var[0]], decimals=1)))\n", " data.append(rowdata)\n", "\n", "for var in [('Hd', 'Hd')]:\n", " rowdata = list()\n", " rowdata.append(var[1])\n", " for i in [0, 1, 2, -1]:\n", " rowdata.append(str(numpy.round(steady_state.solutions[i][var[0]], decimals=1)))\n", " data.append(rowdata)\n", "\n", "s = generate_html_table(['Period', '1', '2', '3', '∞'], data)\n", "HTML(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Figure 3.5" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "caption = '''\n", " Figure 3.5 Disposable income and expected disposable income starting from scratch\n", " with delayed expectations (Table 3.6) - Model SIMEX '''\n", "\n", "# Add an extra iteration to show YDe starting from zero\n", "yddata = [0] + [s['YD'] for s in steady_state.solutions]\n", "ydedata = [0] + [s['YDe'] for s in steady_state.solutions]\n", "\n", "fig = plt.figure()\n", "axes = fig.add_axes([0.1, 0.1, 1.0, 1.0])\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, 85)\n", "axes.set_xlim(-2, 50)\n", "\n", "axes.plot(yddata, linestyle='--', color='g') # plot YD\n", "axes.plot(ydedata, linestyle=':', linewidth=2, color='r') # plot YDe\n", "plt.axhline(y=80, color='k')\n", "\n", "# add labels\n", "plt.text(2, 72, 'Disposable')\n", "plt.text(2, 68, 'Income')\n", "plt.text(10, 60, 'Expected disposable income')\n", "\n", "fig.text(0.1, -0.05, caption);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model with fixed expected disposable income" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the model with fixed YDe." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def create_simex_yde_model():\n", " model = Model()\n", "\n", " model.set_var_default(0)\n", " model.var('Cd', desc='Consumption goods demand by households')\n", " model.var('Cs', desc='Consumption goods supply')\n", " model.var('Gs', desc='Government goods, supply')\n", " model.var('Hd', desc='Cash money demanded by households')\n", " model.var('Hh', desc='Cash money held by households')\n", " model.var('Hs', desc='Cash money supplied by the government')\n", " model.var('Nd', desc='Demand for labor')\n", " model.var('Ns', desc='Supply of labor')\n", " model.var('Td', desc='Taxes, demand')\n", " model.var('Ts', desc='Taxes, supply')\n", " model.var('Y', desc='Income = GDP')\n", " model.var('YD', desc='Disposable income of households')\n", " model.var('YDe', desc='Expected disposable income')\n", "\n", " model.param('Gd', desc='Government goods, demand')\n", " model.param('W', desc='Wage rate')\n", " model.param('YDstar', desc='Exogenously fixed expected disposable income')\n", " model.param('alpha1', desc='Propensity to consume out of income')\n", " model.param('alpha2', desc='Propensity to consume o of wealth')\n", " model.param('theta', desc='Tax rate')\n", "\n", " model.add('Cs = Cd') # 3.1\n", " model.add('Gs = Gd') # 3.2\n", " model.add('Ts = Td') # 3.3\n", " model.add('Ns = Nd') # 3.4\n", " model.add('YD = (W*Ns) - Ts') # 3.5\n", " model.add('Td = theta * W * Ns') # 3.6, theta < 1.0\n", " model.add('Cd = alpha1*YDe + alpha2*Hh(-1)') # 3.7E, 0 < alpha2 < alpha1 < 1\n", " model.add('Hs - Hs(-1) = Gd - Td') # 3.8\n", " model.add('Hh - Hh(-1) = YD - Cd') # 3.9\n", " model.add('Hd - Hs(-1) = YDe - Cd') # 3.18\n", " model.add('Y = Cs + Gs') # 3.10\n", " model.add('Nd = Y/W') # 3.11\n", " model.add('YDe = YDstar') # 3.20\n", " \n", " return model\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Use the steady state solution as a starting point\n", "step_model = create_simex_yde_model()\n", "\n", "step_model.set_values({'Gd': 20,\n", " 'W': 1,\n", " 'YDstar': 80,\n", " 'alpha1': 0.6,\n", " 'alpha2': 0.4,\n", " 'theta': 0.2})\n", "\n", "# start from the steady-state equilibrium\n", "step_model.set_values({'YD': 80,\n", " 'YDe': 80,\n", " 'Cd': 80,\n", " 'Cs': 80,\n", " 'Gs': 20,\n", " 'Y': 100,\n", " 'Nd': 100,\n", " 'Ns': 100,\n", " 'Td': 20,\n", " 'Ts': 20,\n", " 'Hh': 80,\n", " 'Hs': 80,\n", " 'Hd': 80,\n", " 'YD': 'Gd*(1-theta)'})\n", "\n", "for i in range(40):\n", " step_model.solve(iterations=100, threshold=1e-5)\n", " if i == 2:\n", " step_model.parameters['Gd'].value += 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Figure 3.6" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "caption = '''\n", " Figure 3.6 Impact on national income Y and the steady state solution Y*, following\n", " an increase in government expenditures ($\\\\bigtriangleup$G = 5) when expected disposable income\n", " remains fixed'''\n", " \n", "gdata = [s['Gd']/s['theta'] for s in step_model.solutions]\n", "ydata = [s['Y'] for s in step_model.solutions]\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(97, 129)\n", "\n", "axes.plot(gdata, 'r') # plot G/theta\n", "axes.plot(ydata, linestyle='--', color='g') # plot Y\n", "\n", "# add labels\n", "plt.text(10, 126, 'Steady-state solution Y*')\n", "plt.text(15, 120, 'Income Y')\n", "fig.text(0.1, -0.1, caption);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Figure 3.7" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "caption = '''\n", " Figure 3.7 Evolution of wealth, consumption and disposable income following an\n", " increase in government expenditures ($\\\\bigtriangleup$G = 5), when expected disposable income\n", " remains fully fixed.'''\n", "hdata = [s['Hh'] for s in step_model.solutions]\n", "yddata = [s['YD'] for s in step_model.solutions]\n", "cdata = [s['Cd'] for s in step_model.solutions]\n", "ydedata = [s['YDe'] for s in step_model.solutions]\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(75, 135)\n", "\n", "axes.plot(hdata, color='r') # plot H\n", "axes.plot(yddata, linestyle='-.', color='b') # plot YD\n", "axes.plot(cdata, linestyle='--', color='g') # plot C\n", "axes.plot(ydedata, linestyle=':', linewidth=2, color='k') # plot YDe\n", "\n", "# add labels\n", "plt.text(10, 114, 'Wealth H')\n", "plt.text(15, 98, 'Disposable income YD')\n", "plt.text(17, 90, 'Consumption C')\n", "plt.text(15, 82, 'Expected disposable income')\n", "fig.text(0.1, -.1, caption);\n" ] }, { "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 }