{ "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 pysolve3.model import Model\n", "from pysolve3.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": [ "<table><tr><th>Period</th><th>1</th><th>2</th><th>3</th><th>∞</th></tr><tr><td>G</td><td>20.0</td><td>20.0</td><td>20.0</td><td>20.0</td></tr><tr><td>Y</td><td>0.0</td><td>29.6</td><td>39.8</td><td>100.0</td></tr><tr><td>T</td><td>0.0</td><td>5.9</td><td>8.0</td><td>20.0</td></tr><tr><td>YD</td><td>16.0</td><td>23.7</td><td>31.9</td><td>80.0</td></tr><tr><td>YDe</td><td>0.0</td><td>16.0</td><td>23.7</td><td>80.0</td></tr><tr><td>C</td><td>0.0</td><td>9.6</td><td>19.8</td><td>80.0</td></tr><tr><td>ΔHs</td><td>0.0</td><td>14.1</td><td>12.0</td><td>0.0</td></tr><tr><td>ΔHh</td><td>0.0</td><td>14.1</td><td>12.0</td><td>0.0</td></tr><tr><td>H</td><td>0.0</td><td>14.1</td><td>26.1</td><td>80.0</td></tr><tr><td>ΔHd</td><td>0.0</td><td>6.4</td><td>11.5</td><td>0.0</td></tr><tr><td>Hd</td><td>0.0</td><td>6.4</td><td>17.9</td><td>80.0</td></tr></table>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import HTML\n", "import numpy\n", "from pysolve3.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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "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=False, right=False)\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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kVBQO/KazFUSkhElJT2HlnpWs3beWNfvWsHZ/6PbpgU9zeZvL+fHAj9z3yX3EVYyjTZ029Grai5s630TdKnUBuKnzTdyceHO++/yl5FM48FswCOXKQYUKBU8rIlKMjiYfZfXe1azau4pVe1bRP74/Q9sOZcuhLXR7tRsADkdCrQTa1W1H5XKVATiv2Xns+vUu6letn2cAiI2Jjeh6SPFTOPCbemQUkTALpgRZvXc1sTGxnNPoHFLSU2jzYhu2HN6SNU2lcpWoVbkWQ9sOJaFWAtOunUa7uu04q/ZZVCpXKcf8KpWrRKVqlXIvRkoRhQO/BQIKByJS7J6f/zzfbP2GFbtXsP7AegxjaJuhzBw5kwqxFRjaZigNqzWkQ/0OdKjXgZa1Wmb94i8XU47h7Yf7vAbiJ4UDv6nlQETOwPHU43y/53uW71rO8t3LWbF7BRViK/DZzZ8B8P6a99lxdAeJDRO5sdONdGrQKUeHPi9e+qJfpUsUUDjwm8KBiBQgmBJk+e7lrNqziju73QnALTNuYdrqaQBUq1CNzg0657i4z+e3fJ7n1f9ECkP/OX4LBnWmgoicZN7meYxfNp7vdn7Hmn1ryLAMAIa1G0b9qvW5t+e9jOw4ksSGicTXjCfGxeR4voKBFIX+e/wWDEKtWn5XISI+SMtIY9WeVSzcvpCF2xaycPtC3h7+Nh3rd2Tzoc18tvEzujXuxvCzh9OtcTe6NepGvSr1AOjboq/P1UtppnDgt2AQmjb1uwoRiYDtR7ZTPrY89avWZ/7W+Qz+12CCqaFrndSpXIdeTXuRmp4KwM2JNzOqyygfq5WyTOHAbzpbQaRUSs9IZ+nOpXz909d8s/UbFmxbwPaj2xk7cCz/2/d/aVOnDbd1vY1eTXrRq2mvrH4HMuXeTSASSQoHftMBiSKlwtHkoyzYtoAMy+Disy4mLSONvhP6kpyeTHzNeC5ocQG9mvTiolYXAVCnSh1euOQFn6sWyZvCgd8UDkSi1ofrPuTTDZ/y9davSdqVRIZlcG7Tc7n4rIupWK4iH93wEe3qtqNJXBO/SxU5LQoHfsrIgOPHdbaCSBTYG9zLvM3zWLlnJU8OeBKA15a+xmcbP6N30978X9//o0/zPvRu2jvrOYNaDvKrXJEiUTjw07FjoVu1HIiUSEt2LOFfy//F55s/Z+WelQBUr1CdB899kBqVavDa0NeoVakW5WPL+1ypSPFSOPBTIBC6VTgQ8V0wJcgXW75gzsY5/E/P/yGhVgIrdq/gtaWv0ad5H27oeAMDEgbQrVG3rDBQv2p9n6sWCQ+FAz9ldtescCDii/3H9vPGsjeYvWE2X//0NSnpKVSMrUi/+H4k1EpgZMeR3NjpRiqWq+h3qSIRpXDgJ4UDkYjaE9zDpxs+pV7VelzU6iLSLZ1HPnuEjvU7cl/P+7io1UX0ad6HyuVDXRNn3oqUNQoHflI4EAm7b376ho9+/IjZG2azdOdSAK5tfy0XtbqI+lXrs/s3u6lXtZ7PVYqULAoHfsoMBzpbQaTYHD5xmKRdSfSL7wfAbz79DUt2LOHcpufy9ICnufisi+nasGvW9AoGIidTOPCTWg5EisXGgxuZtXYWs9bN4ostXxDjYtj3231Ur1idCcMm0KhaI2pUquF3mSJRQ+HATzpbQeSMpGekYxjlYsrx8uKXGfPxGADOrns2D/Z+kKFth1KlfBUA2tVt52epIlFJ4cBPajkQKbS0jDS+3PIl01dP5/017/PSJS9xTftruLDlhTx/8fMMbTOUVrVb+V2mSKmgcOAnhQORAgVTgjzwyQPMWDuDfcf2UaV8FS5tfWnWJYlb12nNA3Ue8LlKkdJF3X75SeGgzKhWQg86XbVqFW3atOH48eNZ4y677DKmTJniW03Jacl8uO5DJiybAECV8lVYvGMxg1sO5t3r3mXvb/cy7dppOS5TLCLFSy0HfgoGoXx5qFDB70qkjOrQoQNXX301Y8eO5emnn2bGjBmkpqYycuTIiNaRmp7K7A2zmbJyCrPWzuJoylHOqn0Wo7qMwjnHsruW5ejOWETCSy0HflKPjGXOvHnz6N+/P8OHD6ddu3bceOONmBkAixcv5rzzziMxMZGePXty9OhRTpw4wa233kqnTp3o2rUrc+fOBWDixIlceeWVDB48mPj4eF566SWee+45unbtSu/evTlw4AAAGzZsYMiQIXTr1o2+ffuyZs2ak2p67LHHmDZtGklJSTzyyCP8/e9/j8hrYWZZ6/7wZw8zdMpQPln/Cdd1uI6Pb/iYVWNWZQUCBQORCMt8g/r5161bNyuTbr3VrEkTv6uQCKhataqZmc2dO9fi4uJs69atlp6ebr1797avvvrKkpOTLSEhwRYtWmRmZocPH7bU1FT7y1/+YrfeequZmf3www/WrFkzO378uE2YMMFatWplR44csT179lhcXJy9/PLLZmb2wAMP2PPPP29mZgMHDrR169aZmdmCBQtswIABedY3c+ZMq169uj3++OPhfBnMzGz1ntX26JxHLWFcgn3707dmZrZqzyqbuWamJaclh335ImVcob6XC9yt4JwbD1wO7DGzjt64PwNDgRRgA3CrmR3yHvsdcDuQDtxnZrPDlGuin1oOyqSePXvStGlTALp06cLmzZupUaMGjRo1okePHgDExcUB8PXXX3PvvfcC0K5dO1q0aMG6desAGDBgANWrV6d69erUqFGDoUOHAtCpUydWrFhBIBDg22+/5dprr81adnJycp41DR06lJo1azJmzJiwrHMwJcg/l/yTyd9PZtmuZcS4GC5seSExLtR42b5ee9rXax+WZYvI6SvMMQcTgZeAN7ON+xT4nZmlOef+CPwOeNg51x64HugANAY+c861MbP04i27lFA4KJMqVvy5E5/Y2FjS0tKKPJ+YmJis+zExMaSlpZGRkUHNmjVJSkoq1PxiYmKIiSm+PY3HU4+z6dAm2tdrT7mYcjz91dO0rt2acRePY0THETSs1rDYliUixavATwIz+xI4kGvcf80s8xNtAdDUGx4GvG1myWa2CVgP9CzGekuXYFCXThYA2rZty86dO1m8eDEAR48eJS0tjb59+zJ58mQA1q1bx08//UTbtm0LNc+4uDgSEhKYNm0aENqFuHz58vCsQDZLdy7lno/uofFzjRn29jDMjIrlKrL+3vUsunMR9/e+X8FApIQrjp8JtwH/8YabAFuzPbbNGyd5UcuBeCpUqMA777zDvffeS2JiIoMHD+bEiROMGTOGjIwMOnXqxIgRI5g4cWKOFoOCTJ48mTfeeIPExEQ6dOjABx98ELZ1+GjdR3R9pSvdXu3G+KTxXNb6Ml65/JWsx+tUqRO2ZYtI8XLmHS18yomciwc+zDzmINv4R4HuwNVmZs65l4AFZvZv7/E3gP+Y2fQ85jkaGA3QvHnzblu2bCniqkShDh3g7LNh+kkvj0iJl2EZfL7pc9rXa0/j6o15d/W7PPP1M9ze9XZGdhxJrcq1/C5RRE5WqFN/zvg6B865UYQOVBxkPyeM7UCzbJM19cadxMxeBV4F6N69e8EJpTQKBNRyIFFnd2A3ry19jTeWvcHmQ5t5esDTPHrBo1x99tVc0/4av8sTkWJwRuHAOTcEeAjoZ2bHsj00E3jLOfccoQMSWwOLilxlaaXdChJFMiyDWz+4lSnfTyE1I5VBCYN4ZuAzXHX2VYCuRSBSmhTmVMYpQH+grnNuG/A4obMTKgKfeh8IC8zsbjNb5ZybCqwG0oB7dKbCKSgcSAl3LPUY8zbP49LWlxLjYoh1sYzpMYYxPcbQpk4bv8sTkTApMByYWV7XUX3jFNOPBcYWpagyIT0dTpzQ2QpSIm04sIGXl7zM+GXjOXjiIOvvXU+r2q0YP2y836WJSASobwW/HPP2xqjlQEqQ9QfWc/8n9/OfH/9DbEwsV599Nff0uIeWtVr6XZqIRJDCgV/UI6OUEMdTj7MzsJOWtVpSq1Itftj7A4/1e4zR3UbTuHpjv8sTER8oHPglEAjdKhyIT/Yf288/Fv+DFxe9SHzNeBbesZA6Veqw4b4NOrhQpIxTOPCLWg7EJ5sObuK5+c8xPmk8x1KPcclZl/Db836b9biCgYgoHPhF4UAizMxwzvHfDf/lle9e4cbON/Lrc39Nx/odC36yiJQpCgd+yQwHOltBwsjM+PjHj/nL/L8w/Ozh3NPzHm5OvJnL21xOkzhd2VxE8qZw4Be1HEgYZVgGM9bM4KkvnmL57uU0jWtKtQqhIFq5fGWalFcwEJH8KRz4RQckShjd+sGtvLn8TVrXbs3EYRO5odMNlI8t73dZIhIlFA78opYDKUYZlsH01dMZmDCQulXqMipxFBe1vIjrO15PbEys3+WJSJQpji6b5UwoHEgxSM9I5+2Vb9Pp5U6MmD6CCcsmADAgYQA3dr5RwUBEzohaDvyicCBFNOX7KTz15VOs2beG9vXa8/Y1bzO8/XC/yxKRUkDhwC/BIFSoAOW1H1jOzNTVUykXU46pw6dyTftriHFqCBSR4qFw4Bf1yCinad7meTz6+aNMGDaBNnXaMP6K8dSoVEOhQESKnT5V/BIIKBxIoSzftZxLJ1/KgEkD+OnwT2w7sg2AWpVrKRiISFio5cAvajmQApgZo2eN5o1lb1CzUk3+PPjP3NPjHiqXr+x3aSJSyikc+EXhQPJxJPkIcRXjcM5Rr2o9Hjr/IR4+/2FqVa7ld2kiUkYoHPhF4UByCaYE+dvCv/HHb/7IjBEzGJAwgGcGPeN3WSJSBikc+CUYhHr1/K5CSgAz463v3+Khzx5ix9EdXNH2ChpXb+x3WSJShikc+CUYhPh4v6uQEuDKd65k5tqZdG/cnbeveZu+Lfr6XZKIlHEKB37R2Qpl2v5j+7PONrjm7GsY1nYYo7qM0tkHIlIi6JPILzrmoExKy0jjxYUvctaLZ2Vd6vjmxJu5rettCgYiUmKo5cAvCgdlzuebPuf+T+5n5Z6VXNjyQs5tdq7fJYmI5EnhwA/p6ZCcDNWq+V2JRMhv//tb/jL/L8TXjOf9Ee8zrO0wnHN+lyUikieFAz+o06UyITktGcOoVK4Sg1oOIq5iHL857ze6iJGIlHjayekHhYNS75ufvqHrK1156ounABhy1hB+3+/3CgYiEhUUDvwQCIRuFQ5KncMnDjPmozH0mdCHYGqQPs37+F2SiMhp024FP6jloFSat3keN753I7sCu3ig1wP8YeAfqFZBx5WISPRROPCDwkGpVLdKXRpXb8yMETPo0aSH3+WIiJwxhQM/ZIYDna0Q1TIsgzeWvkHSriT+ftnf6Vi/I4vuWKSzEEQk6umYAz+o5SDqrd23lgGTBjD6w9H8sO8HTqSdAFAwEJFSQeHADwoHUSstI43/99X/o/M/O/P97u8Zf8V45tw8h0rlKvldmohIsdFuBT/obIWotTe4lz99+yeuaHsFL13yEg2qNfC7JBGRYqdw4Ae1HEQVM2Pm2pkMbTuURtUbsfzu5TSLa6ZdCCJSamm3gh8UDqLGrsAuhk4ZypXvXMm7q98FoHmN5goGIlKqqeXAD8EgVKwI5fTyl2Tv/fAeo2eNJpgaZNzF47im/TV+lyQiEhH6dvKDemQs8X732e949ptnOafROfz7qn9zdr2z/S5JRCRiFA78oHBQYpkZzjkuPutiysWU4/f9fk+F2Ap+lyUiElEKB34IBBQOSpgTaSd4dM6jVCxXkWcGPUP/+P70j+/vd1kiIr7QAYl+UMtBibL+wHrOe+M8nlvwHEeSj2BmfpckIuIrtRz4QeGgxJi2ahq3z7ydcjHl+OD6D7ii7RV+lyQi4rsCWw6cc+Odc3uccyuzjbvWObfKOZfhnOuebXy8c+64cy7J+/tnuAqPasGg+lUoAbYd2cZN799E+3rtWXbXMgUDERFPYVoOJgIvAW9mG7cSuBp4JY/pN5hZl6KXVoqp5cBX+4/tp06VOjSNa8qcm+fQs0lPHXQoIpJNgS0HZvYlcCDXuB/MbG3YqirtdECib95d/S6tXmjF1FVTAejTvI+CgYhILuE4IDHBObfMOfeFc65vGOYf/dRyEHEp6Snc/5/7GT5tOG3rtqVnk55+lyQiUmIV9wGJO4HmZrbfOdcNmOH0o1jhAAAgAElEQVSc62BmR3JP6JwbDYwGaN68eTGXUcIpHETU5kObuW7adSzesZj7e93Pnwb/Sa0FIiKnUKwtB2aWbGb7veHvgA1Am3ymfdXMuptZ93r16hVnGSVbWhqkpCgcRND8rfNZt38d7133HuOGjFMwEBEpQLG2HDjn6gEHzCzdOdcSaA1sLM5lRL3MTpd0tkJYZVgGy3ctp2ujrozsNJLBrQZTt0pdv8sSEYkKhTmVcQowH2jrnNvmnLvdOXeVc24bcC7wkXNutjf5BcAK51wSMB2428wO5D3nMko9MobdkeQjXPXOVZz7xrlsPBjKpgoGIiKFV2DLgZmNzOeh9/OY9l3g3aIWVaoFAqFbhYOw2HBgA1e8fQVr963l+YufJ6Fmgt8liYhEHV0hMdLUchA2czbO4dpp1+KcY/ZNsxnUcpDfJYmIRCWFg0hTOAib/274L42rN+aD6z+gVe1WfpcjIhK11PFSpOmAxGKVkp7Cj/t/BOCZQc8w//b5CgYiIkWkcBBpajkoNrsDuxk4aSD9J/UnkBIgNiaW6hWr+12WiEjU026FSFM4KBZLdy7lyrevZN+xfUwYNoFqFdQSIyJSXNRyEGk6W6HIpq6aSp/xfQD45rZvGNFxhM8ViYiULmo5iDS1HBSJmTExaSLnNDqHd697lwbVGvhdkohIqaNwEGkKB2ckPSOdoylHqVmpJlOvnUr5mPJULFfR77JEREol7VaItGAQKlWC2Fi/K4kax1OPc+20a7noXxeRkp5CtQrVFAxERMJI4SDS1CPjaTlw/ACD/zWYGWtmcGOnG9VpkohIBGi3QqQpHBTalkNbuGTyJWw4uIG3h7/NdR2u87skEZEyQeEg0gIBhYNCGvXBKHYc3cF/b/ov/eL7+V2OiEiZoXAQaWo5KLQ3rniDY6nH6Fi/o9+liIiUKTrmINIUDk7p3yv+ze0f3I6Z0bJWSwUDEREfKBxEWjCofhXyYGb86Zs/8Yv3f8HGQxs5lnrM75JERMos7VaINLUcnCTDMvjVJ7/ihUUvMKLDCCZdOUmnKoqI+EgtB5GmcHCSBz55gBcWvcADvR7grWveUjAQEfGZWg4iTWcrnOSqdldRt0pdfn/B73HO+V2OiEiZp5aDSFPLAQCp6al8sv4TAAYkDOCxfo8pGIiIlBAKB5GUmhr6K+PhIDktmeHThnPp5EtZtWeV3+WIiEgu2q0QSZmdLpXhsxWOpR7j6neuZvaG2fz90r/ToX4Hv0sSEZFcFA4iqYz3yBhICTB0ylC+2PwFb1zxBrd1vc3vkkREJA8KB5EUCIRuy2g4+HDdh3y15Sv+ddW/uLHzjX6XIyIi+VA4iKQy2nJgZjjnuL7j9XRp2IV2ddv5XZKIiJyCDkiMpDIYDvYG99JvYj8WbV8EoGAgIhIF1HIQSWUsHOwK7GLQm4PYeHAjh04c8rscEREpJIWDSCpDZyvsDe5l4KSB/HT4J/5z43/oH9/f75JERKSQFA4iqYy0HBw+cZiL/n0Rmw5t4pMbP6FffD+/SxIRkdOgYw4iqYycrVC5fGXa12vP+yPeVzAQEYlCajmIpFLecnA89TjB1CB1q9Rl8tWT/S5HRETOkFoOIikzHFSp4m8dYZCSnsK1066l/8T+pKSn+F2OiIgUgcJBJAWDUKkSxMb6XUmxSs9I56b3buKjHz/i3p73UiG2gt8liYhIESgcRFIwWOrOVMiwDO6YdQfTVk/jrxf9lbu63+V3SSIiUkQKB5FUCrtrfuarZ5iYNJEn+j3Bg+c+6Hc5IiJSDHRAYiQFAqUuHNze9Xaqlq/KA70f8LsUEREpJmo5iKRS1HLwwZoPSMtIo1H1Rvzq3F/hnPO7JBERKSYKB5FUSsLBuAXjuPKdK/nnkn/6XYqIiISBwkEklYIDEicmTeRXs3/F1Wdfzd3d7/a7HBERCQOFg0iK8paDTzd8yp2z7uTClhfy1tVvUS5Gh6yIiJRGCgeRFMXh4ETaCUZ9MIr29drz7nXvUrFcRb9LEhGRMNFPv0iK4rMVKpWrxKyRs6hftT5xFeP8LkdERMKowJYD59x459we59zKbOOudc6tcs5lOOe655r+d8659c65tc65i8NRdNSKwpaDwycO89b3bwFwTqNzaBrX1OeKREQk3AqzW2EiMCTXuJXA1cCX2Uc659oD1wMdvOf8wzlXuq4VfKZSUiAtLarCQUp6CsOnDeeWGbfw4/4f/S5HREQipMBwYGZfAgdyjfvBzNbmMfkw4G0zSzazTcB6oGexVBrtMjtdipKzFcyM0bNG89nGz3ht6Gu0rtPa75JERCRCivuAxCbA1mz3t3njJMq6a35i3hNMWj6JJ/o9waguo/wuR0REIsi3sxWcc6Odc0ucc0v27t3rVxmRE0XhYOWelTz15VPc2uVWHuv3mN/liIhIhBX32QrbgWbZ7jf1xp3EzF4FXgXo3r27FXMdJU8gELqNgnDQsX5HPv3Fp/Rr0U+XRRYRKYOKu+VgJnC9c66icy4BaA0sKuZlRKcoaDlYvms58zbPA+DClhdSPra8vwWJiIgvCmw5cM5NAfoDdZ1z24DHCR2g+CJQD/jIOZdkZheb2Srn3FRgNZAG3GNm6WGrPpqU8HCw9fBWLn3rUiqXq8wP9/ygYCAiUoYVGA7MbGQ+D72fz/RjgbFFKapUKsFnKxxJPsKlb11KICXAJzd+omAgIlLG6QqJkVJCWw4yLIOb3ruJH/b+wCc3fUKnBp38LklERHymcBApJTQcvLPyHWatm8WLl7zIhS0v9LscEREpARQOIqWEnq0wouMIqlaoytA2Q/0uRURESgj1yhgpmS0HVar4W4dnzb41bD60mRgXwxVtr9ApiyIikkUtB5ESDELlyhDjfx47ePwgQ6cMpVK5Siy/ezkxzv+aRESk5FA4iJRgsEScqZCekc4N793AlkNbmHvLXAUDERE5icJBpJSQ7pof/fxRPln/Ca9c/grnNz/f73JERKQE0s/GSAkEfA8Hs9bO4o/f/JG7u93N6G6jfa1FRERKLoWDSCkBLQcDEgbweL/H+dslf/O1DhERKdm0WyFSfAwHB44foEJsBapVqMYT/Z/wpQYREYkeajmIFJ/CQVpGGtdOu5ZBbw4iwzIivnwREYk+CgeR4tPZCr/972/5fNPn3NPjHp2ZICIihaJvi0jxoeXgzeVvMm7hOO7vdT83J94c0WWLiEj0UjiIlAifrbBi9wru+vAuBsQP4M+D/xyx5YqISPRTOIgEs4i3HFQtX5ULW17I28PfVhfMIiJyWnS2QiSkpEB6esTCgZnRqnYrZo2cFZHliYhI6aKWg0jI7HQpAgckTkqaxPBpwwmkBMK+LBERKZ0UDiIhMxyEueXgh70/MObjMRw4foDK5SqHdVkiIlJ6KRxEQgTCwfHU44yYPoIq5asw+erJxMbEhm1ZIiJSuumYg0gIeE38YQwHv5r9K77f8z0f3/Axjas3DttyRESk9FPLQSSEueVgT3AP7/3wHg+d9xCXtL4kLMsQEZGyQy0HkRDmcFC/an1W/HIFdSrXCcv8RUSkbFHLQSSE6WyF5LRkXv3uVdIz0mlYraGuZyAiIsVC4SASwtRy8Mhnj3DXh3fx1U9fFet8RUSkbFM4iIQwhIOZa2cybuE47u15L/3j+xfbfEVERBQOIqGYz1b46fBPjJoxinManaN+E0REpNgpHERCZstB5aJfmMjMuGXGLaRlpPHO8HeoWK5ikecpIiKSnc5WiIRgEKpUgZiiZzHnHM8MfIY9wT2cVfusYihOREQkJ4WDSAgGi+VMhWBKkKoVqnJus3OLoSgREZG8abdCJBRDd83BlCBdX+nK2C/HFlNRIiIieVM4iIRiCAcPffoQ6w+sp0/zPsVUlIiISN4UDiIhEChSOPh0w6f8Y8k/eKD3A/SL71eMhYmIiJxM4SASitBycOjEIW6beRvt6rZj7EDtUhARkfDTAYmREAxCnTPr92DJjiUcST7Cu9e9S+XyRT8VUkREpCAKB5FQhLMVLmx5IT898BM1KtUo5qJERETypt0KkXAGuxX2Bvfy1vdvYWYKBiIiElEKB5FwmuHAzLj7o7u59YNb2XJ4SxgLExEROZl2K4Sb2WmfrfDW92/x3g/v8eygZ4mvGR++2kRERPKgloNwS06GjIxCh4PtR7bzP//5H85tei6/Oe83YS5ORETkZAoH4XYa3TWbGXfMuoOU9BQmXTmJ2JjYMBcnIiJysgLDgXNuvHNuj3NuZbZxtZ1znzrnfvRua3nj+zvnDjvnkry/x8JZfFTIDAeFOFvBOcdd3e7i75f+ndZ1Woe5MBERkbwVpuVgIjAk17hHgDlm1hqY493P9JWZdfH+niqeMqNYIVsOMiwDgCvbXcmoLqPCXJSIiEj+CgwHZvYlcCDX6GHAJG94EnBlMddVegQCodtThIMMy2DIv4fwwsIXIlSUiIhI/s70mIMGZrbTG94FNMj22LnOueXOuf845zoUrbxSoBAtBy8vfplPN35KjYq6noGIiPivyKcympk558y7uxRoYWYB59ylwAwgz53nzrnRwGiA5s2bF7WMkqsQ4WDq6ql0bdiVmxNvjlBRIiIi+TvTloPdzrlGAN7tHgAzO2JmAW/4Y6C8c65uXjMws1fNrLuZda9Xr94ZlhEFCggHaRlpLN6+mAtaXIBzLoKFiYiI5O1Mw8FM4BZv+BbgAwDnXEPnfcM553p6899f1CKjWgFnK3y/+3uOpx2nd9PeESxKREQkfwXuVnDOTQH6A3Wdc9uAx4FnganOuduBLcB13uTDgV8659KA48D1ZmYnz7UMKaDloFblWvxf3/+jb/O+ESxKREQkfwWGAzMbmc9Dg/KY9iXgpaIWVaoUcLZCfM14/jDwDxEsSERE5NR0hcRwCwbBOahcOc+H52+dTyAlEOGiRERE8qdwEG7BIFSpEgoIuRw4foDzxp/Hiwtf9KEwERGRvCkchFswmO/BiIu2LwLQwYgiIlKiKByEWzCY7/EGC7YtIMbF0L1x9wgXJSIikj+Fg3ArIBx0rN+R6hWrR7goERGR/CkchFsgkGc4yLAMFm5fSO8m2qUgIiIlS5EvnywFOEXLweybZlO1/Kl7axQREYk0hYNwCwYhj8tDx7gYejbp6UNBIiIip6bdCuGWz9kK01dPZ9baWT4UJCIicmpqOQi3fHYrjP1qLPWq1GNo26E+FCUiIpI/tRyEWx7hIJgSZMXuFbq+gYiIlEgKB+FklufZCkt2LCHDMhQORESkRFI4CKcTJ0IBIVc4WLBtAQC9mvTyoyoREZFTUjgIp3y6a161dxWta7emTpU6PhQlIiJyajogMZwyw0GusxUmXTmJA8cP+FCQiIhIwdRyEE75tBw459RqICIiJZbCQTjlEQ5mr5/NLTNuYf+x/T4VJSIicmoKB+EUCIRus4eDDbOZumoqcRXjfCpKRETk1BQOwimPloMF2xbQrVE3yseW96koERGRU1M4CKdc4SA5LZmlO5fq+gYiIlKiKRyEU66zFZbvXk5yerLCgYiIlGgKB+GUq+XgSPIR2tdrr4sfiYhIiabrHIRTrgMSL2x5IavGrPKxIBERkYKp5SCcgkFwDipVAsDMfC5IRESkYAoH4ZTZI6Nz7Anuoc6f6jBt1TS/qxIRETklhYNwytZd88JtCzl44iANqzX0uSgREZFTUzgIp2Aw60yFBdsWEOti6da4m89FiYiInJrCQThlbznYvpDEholUKV/F56JEREROTeEgnAIBqFqV9Ix0Fm1fRO8mur6BiIiUfDqVMZy83Qon0k4wpscYBiYM9LsiERGRAikchFMwCA0aULVCVZ698Fm/qxERESkU7VYIJ6/lYMOBDRxPPe53NSIiIoWicBBO3gGJV75zJcOnDfe7GhERkUJROAinYJAjVcuxas8q9acgIiJRQ+EgXMwgGGRxtcMYpp4YRUQkaigchMvx42DGggp7AejZpKfPBYmIiBSOzlYIF6+75gUx2zm71tnUrFTT54JEREQKR+EgXLxw8PvaV7F/0Hk+FyMiIlJ4Cgfh4oWDnrU7QetLfC5GRESk8HTMQbgEgyxrCDNSVpCanup3NSIiIoWmcBAugQATusJNm/6Kc87vakRERAqtUOHAOTfeObfHObcy27jazrlPnXM/ere1vPHOOfeCc269c26Fc+6ccBVfogWDLGgKPWp2oFyM9t6IiEj0KGzLwURgSK5xjwBzzKw1MMe7D3AJ0Nr7Gw28XPQyo8/xowdZ1hB61+/qdykiIiKnpVA/ac3sS+dcfK7Rw4D+3vAkYB7wsDf+TTMzYIFzrqZzrpGZ7SyOggsrdeF8vtm58KTxCZUa0qJSQ05kpLDgyOqTHj+rchOaVqxHIP04S46uPenxdlWa07BCbQ6nBVgWWH/S4x2qxFOvQk3mfP8BaZWgd2NdGVFERKJLUdq7G2T7wt8FNPCGmwBbs023zRsX0XBwZMxtDLhizUnjx86B//0KdtWEAQ+c/LwXPoZ7F8Gm+jBgzMmPT5gBo5JgVTMYcPvJj09/B675AY51gNhroHfrAcWwNiIiIpFTLDvDzcycc3Y6z3HOjSa024HmzZsXRxk5xP1jPHPzajno1RAqNaRhRgpz82o56N0EKtYjIf04c/NqOTi3OVSoTYe0AHPzajk4Nx4q1OTC1COsqO5o0LBVsayPiIhIpLhQ638hJgztVvjQzDp699cC/c1sp3OuETDPzNo6517xhqfkni6/eXfv3t2WLFlStDURERGRghTq9LminMo4E7jFG74F+CDb+Ju9sxZ6A4cjfbyBiIiInLlC7VZwzk0hdPBhXefcNuBx4FlgqnPudmALcJ03+cfApcB64BhwazHXLCIiImFU2LMVRubz0KA8pjXgnqIUJSIiIv7RFRJFREQkB4UDERERyUHhQERERHJQOBAREZEcFA5EREQkB4UDERERyUHhQERERHJQOBAREZEcFA5EREQkB4UDERERyUHhQERERHJQOBAREZEcFA5EREQkB4UDERERyUHhQERERHJQOBAREZEcFA5EREQkB4UDERERyUHhQERERHJQOBAREZEcFA5EREQkB4UDERERyUHhQERERHJQOBAREZEcnJn5XQPOub3AljDMui6wLwzz9ZvWK7povaKL1iu6aL1Ozz4zG1LQRCUiHISLc26JmXX3u47ipvWKLlqv6KL1ii5ar/DQbgURERHJQeFAREREcijt4eBVvwsIE61XdNF6RRetV3TReoVBqT7mQERERE5faW85EBERkdNUKsOBc26Ic26tc269c+4Rv+spTs65zc65751zSc65JX7Xc6acc+Odc3uccyuzjavtnPvUOfejd1vLzxrPRD7r9YRzbru3zZKcc5f6WePpcs41c87Ndc6tds6tcs7d742P6u11ivWK9u1VyTm3yDm33FuvJ73xCc65hd7n4jvOuQp+13o6TrFeE51zm7Jtry5+13omnHOxzrllzrkPvfu+bq9SFw6cc7HA34FLgPbASOdce3+rKnYDzKxLlJ++MxHIfa7tI8AcM2sNzPHuR5uJnLxeAM9726yLmX0c4ZqKKg34tZm1B3oD93jvqWjfXvmtF0T39koGBppZItAFGOKc6w38kdB6nQUcBG73scYzkd96Afw22/ZK8q/EIrkf+CHbfV+3V6kLB0BPYL2ZbTSzFOBtYJjPNUkuZvYlcCDX6GHAJG94EnBlRIsqBvmsV1Qzs51mttQbPkroA6wJUb69TrFeUc1CAt7d8t6fAQOB6d74aNxe+a1X1HPONQUuA1737jt83l6lMRw0AbZmu7+NUvCGz8aA/zrnvnPOjfa7mGLWwMx2esO7gAZ+FlPM/sc5t8Lb7RBVze/ZOefiga7AQkrR9sq1XhDl28trok4C9gCfAhuAQ2aW5k0SlZ+LudfLzDK311hvez3vnKvoY4lnahzwEJDh3a+Dz9urNIaD0q6PmZ1DaLfJPc65C/wuKBwsdBpNqfhVALwMtCLUFLoT+Ku/5ZwZ51w14F3gATM7kv2xaN5eeaxX1G8vM0s3sy5AU0Ktqe18LqlY5F4v51xH4HeE1q8HUBt42McST5tz7nJgj5l953ct2ZXGcLAdaJbtflNvXKlgZtu92z3A+4Te+KXFbudcIwDvdo/P9RQLM9vtfahlAK8RhdvMOVee0BfoZDN7zxsd9dsrr/UqDdsrk5kdAuYC5wI1nXPlvIei+nMx23oN8XYPmZklAxOIvu11PnCFc24zod3gA4G/4fP2Ko3hYDHQ2jvSswJwPTDT55qKhXOuqnOueuYwcBGw8tTPiiozgVu84VuAD3yspdhkfoF6riLKtpm3//MN4Aczey7bQ1G9vfJbr1Kwveo552p6w5WBwYSOp5gLDPcmi8btldd6rckWUB2h/fJRtb3M7Hdm1tTM4gl9X31uZjfi8/YqlRdB8k49GgfEAuPNbKzPJRUL51xLQq0FAOWAt6J13ZxzU4D+hHoe2w08DswApgLNCfXSeZ2ZRdXBffmsV39CTdQGbAbuyravvsRzzvUBvgK+5+d9ov9LaP981G6vU6zXSKJ7e3UmdABbLKEfgFPN7Cnv8+NtQk3vy4CbvF/bUeEU6/U5UA9wQBJwd7YDF6OKc64/8Bszu9zv7VUqw4GIiIicudK4W0FERESKQOFAREREclA4EBERkRwUDkRERCQHhQMRERHJQeFAREREclA4EBERkRyiNhzk0ef6s9741yPdRbNz7g2vj/EVzrnp3rXa85qus3NuvtcX+ffOuUoFzHeic274qaYJB+dcl5LSh33uWpxzVzjnir1rYOdcnhdNcc59W9zLKg7OuQ7OuXXeleIyx33knBtZzMvpn9m/fK7xubfLE8653xTzsq8szveyc26zc65uEefxgHOuSnFNV4Q6RjnnXipgmnjn3A3Z7nd3zr1QDMue7Jz7Zbb7vbzPvvLe/Se8W1fAfNp5n93LnHOtTjFdwLuNd84V6eqHzrnGzrnpBU8pURsOPNn7XH8EwMzuMLPVRZ1xtmtaF8avzCzRzDoDPwH/k8/8/k3o6l0dCF01L7WodYZJF6BEhANy1WJmM83s2Ugt3MzOi9SyToeZrQLeAx6F0BcpUN7MpkSohEj8j1wJRDToF8IDQGG+9As7XTjFA1nhwMyWmNl9xTDfB4HfepczjgFeAsYAA51zY4Eqzrk7CL0Gp3IlMN3MuprZhmKoq0BmtsPMIv6DKyqZWVT+AU8Qusxk7vHzgO7e8O3AOmARoQ5UXvLGTwSGZ3tOwLvtT+hyqjOBdd64m7znJwGvALGnqMkR6tHt4TweuxT492muY1adhC7j+v+8OpYA5wCzCXXFene2+r8EPgLWAv8EYrzHXvaetwp4MtsyegDfAsu99axBKODs9ZY1IldNlQh1bvI9oUt6DvDGjyL0ZfUJ8CPwp3zWaTPwJLDUm0c7b3xPYL43z2+BtkCF3LV4y8ncjvHA58AKYA7QPNvr9oI3n43ZXsNq3nSZyx6W+38gj3qz/2/MI9S/+hpgMj9fYTT3a1i9gNdpBqFudDcTCpIPetMsAGp707XyXsvvCP1Ptsujtiredu7i1dQ6j2nivecv9f7OK8T6DPHGLfVexw9zzTOv7fIEMN6b50bgvmzTF/geAp4FVnvb8i/AecABYJP3vFb5vSbAUEKXcl4GfEaoK2kIdXv7X0L/868TusRzXeApQj0wZi57LHB/rnqqEnofLSd0rf4RwH1AirdN5+b3vspnuosI/X8vBaYB1fJ4De7L9hq87Y2r7f2/rPD+Pzpn+z8q6PNsAXDYe/1+5W3zDwuYb77bMVetYwj9f48B3sg2/gIgmTw+A/P4PNxFqDOhzNfoQe+1Xplr+2SuTzywsoDPoY+yrcsy4DFv+CngzlzzGEU+n1nk891Rlv58L+CMCw/9E2/3/vGTgIu98fOA7kBjQh++tYHyhD5MChMOgkCCd/9sYBahX2QA/wBuzqeeCYSupT8XqJLH4w8A/yL0hb4UeKgQ65hVp7cuv/SGn/fe1NUJXVN8d7b6TwAtCV1//NNsz8/80on1XqPOhD7kNwI9vMfiCPXZMCq/NwPwa0L9VUCom9SfvDfqKG9eNbz7W4BmeTx/M3CvNzwGeD37sr3hC4F3veEctZDzQ3EWcIs3fBswI9vrNo1Qy1h7YL03vhwQ5w3XBdbz8xdiYcLBYUK9o8UQ+qDvc4rX8FSv0/ps2+4wP4e75/E+FAmFmNbecC9CnbHkVd9Q4AjwRD6PVwEqecOtgSUFrE8lYKs3rSPUd8KHecw393Z5glBAqui9tvsJve8KfA8R+hJfm21b1MznfZrnawLUyvbcO4C/esMv8POXw2WE+kqoS+gLYqk3PoZQwK6Tq6ZrgNey3a+R7f+3brbxJ72vck/nLfNLoKp3/+HMunItcwdQMddr8CLwuDc8EEjK432Q+3XK/j/7YbbxWfdPMd88t2MetcYQCmSbMl87Qh0hjQX+7G2H+3M/L9c8nsD7gQd0I/RFX5VQiF8FdM21PvH8/MWe3/vrEeAeQp9Di4HZ3jRzCf3gyD6PUeTxmcUpvjvK0t/pNJ2XRM+b2V/yeawn8IV5HcE456YBbQoxz0VmtskbHkTon3axt/usMvl0S2tmtzrnYgm96UYQCgvZlSP04dsDOAbMcc59Z2ZzClFTpszeJb8n9MvjKHDUOZec2VuZV/9GyOoEqA+hX4fXOedGe3U0IvSlacBOM1vsrcMR73mnqqGPt46Y2Rrn3BZ+fl3nmNlhbx6rgRaEvmhyy+zy9zvgam+4BjDJOdfaq6t8wS8H52Z7/r+AP2V7bIaFutxd7Zxr4I1zwDPOuQsIdbTTBGhA6BdMYSwys20AzrkkQh80h8n7NTzV6zQ327Y7TOjLE0LbtbN3zMp5wLRs26JiXgWZ2Szn3CFCX7p5KQ+85JzrAqST8z2Q1/oEgFk+MAUAABC5SURBVE1m9qM3/t/A6IJfGgA+slDHMMnOuT2EXtvCvIcOEwq1b3jHN+R1jMOpXpOmwDte73wVCH1hQehX7NUAZvaRc+6gN7zZObffOdfVq3GZme3Ptcjvgb865/5I6Av1q3zWOa/31Ypc0/T2xn/j1V6BUBjLbQUw2Tk3g9Cvegi9367x6v7cOVfHOReXTy2n41TzzWs7bsv+ZDPLcM69QqiVNvO1+8zMPnXOPWFmrxd0zEEe9bxvZkEA59x7QF9Cv/7zmz6v99dXhFpgNhFqRRjsHfuRYGZrnXPxueaT12dWXc7su6NUifZwcKbS8I638PaZVcj2WDDbsAMmmdnvCjNTM0t3zr0NPMTJ4WAb8KWZ7fOW+zGhXQOnEw4ye+TKyDaceT9zW1ruspxzCf+/vfMPmqsq7/jnmyC/QzBBFAc1tbbjCBGUhKoYMAyjZSwKSmSmEYztqFiEgg22TqREiC3KjA5hsP4IDEXAQdQEBpQfQkISICQkISGBBCwELUIFDShgDCRP/3jOzXt333vv3n13lzf75vnMvJPN7jnPfc6Pe/acc88+X2AmvrrdLOlKfKbcbfI+baO8f/25IM2F+JfmSekGXtRFX7JBajq+Wj/CzF6W66e3Uw91y9eOnXxbZu04CnjOzA6vaW87A6qCzZyD72gdluxuKfGjk/JU2Wt5D5nZK5KOxCcSJ+OPWo5tSlZVJ5cC3zSzG+WqdrNr+DoPXzm+Ad9Gb/bpEUnvxre/50i6w8wuyKdp474ScLuZtTos+mF8QnMCMEvSxBrlgOrxbCjU7RcN/c7SctzMZuf//yqzAt85fgzfOT0Af5ywsiR9t++BEUO/H0isYgVwjKTXpsOAH899tglfzQB8hPJV6h3AyZIOBJA0TtJb8gnkvC17nextKLB1KzBR0t7Jn2Pw54vd5khJf5EGiVOApfhW94vA82kVfXxKuxE4SNLk5P+Y5Nsf8W3vIpbgX7JI+mtcrndjF/weiz8mAh+0M6p8uQfXPyf5VLa6y1/jt2liMBVfJXRKWR0OuZ7S7sPjkqal/JJ02BD9G4vvbGwHTsW3v6vYAEzInR4v+0Krapc8de6hffFt+5/hk5msrDuu0aJO8n3nUznTi0kH8iQdjz9+yJiPn62YjN+bDUh6I/CSmV2Nb5O/u6DcZfdVc7plwFG5cWKf1Cfy1xuFP4ZbiD92GItvr+f70QeAZ7PdqRybKB7P6t7HZXa7gqSr0uSviiXAiWl83Ac4ier7ufD+MrOt+G7lNHx3Zgk+gVvchstV3x27DCN2cmBmTwL/gR8ouRu/gZ5PH38fb/w1+Nb0iyU2HgK+AtwmaS0+Ez2oKZnw7fAH8a3Ig/DDL9nP7i5ItjYD38Q73gP4M8+bu1LYRlbgp4cfxrfW5pvZGnx7bgNwLV4fpBvpFODSVBe34yufhcA75D8zOqXJ/reBUam81wEzrDsa498A/lPSahpn71W+nAl8OrXNqcA/t7jGNcCk5PtpFE/i2qKiDjutp+nAPyab64GPDtHFbwOfSnbeTklfzzCzLfhjhJslraLkMRrV7ZK3V+ceGgPclD5fih9MA9eyP1cDP3Urq5PZ+OOGlcCzObtfBY6WtB5/vPCrnF9bUxl+ZGbbClyfCCyXP245H5iT3v8ecIukhWX3VUG6Z/AJ7w9TGe/F2yLPaODq1F9WA3PN7LlUtiNSvotonPxklI1na4Ft8p9Zn9OUp47dbvFO/DxFKWa2Cj87sRw/yzDPzMoeKUD1/bUEXwT8Kb0+mNYLh7wvVd8duwzZIZ4RiaR9zeyFNPubjx9gmT/cfvWKtAKYaWZ/N9y+BMHOTFqprwKmZecrgu4jP8dwuZlNG25f2mFX++4oYsTuHCRmp5n/OnwVvaBF+iAIRjjywEq/xA+jxcSgh5jZH/ptYpDY5b87RvTOQRAEQRAE7TPSdw6CIAiCIGiTmBwEQRAEQdBATA6CIAiCIGhgl54caCdV3KuiH33uNZL2l/RPw+1HK5RTLszacbh8l7SXpLvkUT3rpH+9pGslPSZppVxd9KQu+LFJrlD6gKT703u7S1qs9sTP8jY7Vu/bmRhqH1ENpczmNMM1vrRqM5Woplakj3GyQ3bpyYF1qLiXArG8qnXYqc+9ZDjqI7E/rtPQN+TasW3fu1TP/wD8tPk3/pKmShrT9J7w09qLzeytZnYEHnzq4A59yJhqrqw6CXbEILgDjx8RvIr9e2ceX9phpJRjOOnryYGkBWkVs14e3zybgT4s6fvp/duU07xvyv9CqzySTpNrla+R9IOUdqOkq/CfubwppfukpOVpBfTdbEVW4uM+km5ONtdlQWTKbLTrc0Ge85LPSyX9MLd6/WK6/jpJZ6f3LpJ0Ri5vfrU7yL+C+phSUZcTJG2QdKWkR+S68MdJulvSo0pR1CrqsqzMFwF/mdJfXFD+Ir8np3bdM7XHekmH5ny8Jl3rx/LY7EPxC0mzUlmX4sIvDe3Y7LuaVlCSZqY2GNTvKvwp7F9NTAduKHh/Gq5Il+dYYKuZfSd7w8yeMLNLC/J3iwWkCHh5JJ0r6az0+luS7kyvj5V0TS7p6JL2KOvDde+lXvelhvEmXXZQ/67IX9jfmspQmkbSC2X9p0V5isaSMjuDxsTEbkW267RBQZq2x/Zc3qKydDR29SV11Jl21j8GFNH2wgfM8bh4zCvA4emzHwGfLMmfV/salAc4BJftzNTVxqW024H35OyUKs+V+DhI8a3KRjs+F6SfjEdk3BOPRPcoHk60UAUt/d2Vy/8QPgEq9K+5Pqr8yn02EZ+YrsTj2guPdregRV2WtdMEktJaQfmr7M3B5YEvA76cu4YBR6X/X5Hqayh+ZXW8Nx5q95cMqNANUpor+f9MPJpdcz1X+VOoKJj7/+7A0wV19be4ct9XSAqC6f2zcJGzOvfkEgaUUvN/x5WkfxwPRrQS+Gzu/dHAMwXp3wNcn7vWcjxc8PnA51q0R1UfrnMv9bQvUTDelPSJsvyl/S2XtzINLrxVpkhZVp6ysaTMTtm4Pch2wbjXlXGyoq7LypLZGdLY1Y9//S4ycZYGnnu+CZeZfRpXlXsgvb8Sb9hWFOV5LT4QPQtgZr+XR/x6wsyW5fJWKc8V+ThI8U3SqRU22vG5maOAG8zD4m6RlCkAFqqgmdlcSQfKY8u/DthsZr+W9IUS/xYX1EeVX4+b2YPpmuvxQDQmD4M6oUVdltleWlFHVfYuwMNNb8G/ADN+bWZZKNyr02dbhuDXAXgdv5TKeyOdka/nqnK1UhQ8AHiuwP4UM5slaSMe7veyIickXYb3n61mNjn/mZlNabNM7zezJ+XaC7dL2mBmi81FzLZKGmOuYJmxEg/7ux8umrMKF9qZQmMbFrXH/pT34Tr3Uq/70liaxpuSOivzYxyt+9uUGmmq+k9ReV6mWFHxlhI7ZeN2ke1m1d3aSrk5ao3t6fMydcgb6Xzs6iv6dnIgDxV8HPBeM3tJ0iIGFNGalbYKtwibaCdPc3z6QuW5Mh+tQPEN2Fxko4s+t8P1uDreG/C45VBexgkMro8qv1opErZS8Wu3zFX2xuOrg9fgfScrxyBlyx74VcYOhb1EXuWvlmJoUf+yRkXBPzXZRdKxuO4BZvaUpP0k7WUen349OfEZMztD0gHA/c3XlrSEYrGfmWb2iwJfn0z//lbSfFxqPRPJ2YNGFUnMRbMexycv9+D6AVOBt+F6IhlF7VHVh+u0X0/7kqQzC+wWUZb/7Jr5K2nRf4rKU9sO3rZl43Yd220p5Sa6dW92Onb1Ff185mAsvqp9SdLb8e3GbnMnME3SeABJ40rSlSnPFfqoYsW3lup1Q+Ru4AT589B9gUx3oUoF7Tr8wNnJ+EShqozdZijXqVKfq7L3XeA8XJDp67k8b5b03vT67/GdiaH4tRiv473kh/xOqOH7/wEHShovaQ8G2qt2uUr61w7MRcBGS8pPEI42s0W5//83LmYFfh/sKenzuc8Lnweb2RTzw4XNf4MmBvJn0mOy18AH8W1m0j33rJm9XHCZvNLeEuB0YLWZtQr32mkf7nVfKhtvmvtIWf46/a1lmhb9p6g8hWNJiZ2qcbvIdjPdGofK6rpddche+LZT0Lc7B/iW1emSHsalcJe1SN82ZrZe0teAuyRtw9XSZheke0hSpjw3Ct9mO6PCx4nAxZK2p7Sfr7DxRIdlWJG2DtfiXzwPAs+b2Sq5/vzylHSHCloq9xjgSTN7qkUZn+7EvwJ/264HM/ud/GDQOuDnZnZuK3uSjgFeNrNr5YeG7kmr58fwtjpD0hX4mYv/SoNZu36tknQdsAbfXlxRx3e5kudyXIa4UDmyRT0N6l8FJm7Dt1B/Ien9wKIm+7+R/4RuDzP7s6QTgW9J+hLwDL4y/teystfk9cB8+RbsbsC1ZnZL+mwqUKZaugSYBdxrZi9K2kKNAbzTPtzrvmRmywrGmxklfaQsf6v+1rJPUt1/yspzJU1jiaQPFdh5kPJxe5Dtum1Am+Nkydg+o2xclO8utbLZkzF8uAhthV0ADSiM7Y2vHD5rLpEaNJEGgZvM7NBhdqWnyLd7zzGzUyXdVJJsHPADMxs0SPca+bPefzOzR17ta3eLkdaXRlp5gmr6eecgqM/35Ep0e+LPxGJisIuTVkgLJY22nUziW9LuwIJ+nhgEQb8TOwdBEARBEDTQzwcSgyAIgiDoATE5CIIgCIKggZgctEDS6ZJO64KdafIwngslTZI0t0v+bZL/5jwIgiAIusKIOHMgaTcze2W4/ahC0i3AHDOriuY3FLubgElZpK8gCIIg6JS+3TmQC2B8R9J9wDfkAVWukIterJb00ZRuhlzo4/a0yv6CXFhjtaRlWfALSZ+RtEIuwvETDQiK5IWHFkn6errGI5KmpPcP0YDYxlpJf9Xk67/jvym/XC6u84Hs52OSLkmfI+lDcqnaUZJel/xYkf6OSmnGy8VD1kuah0flCoIgCIKu0beTg8TBwPvM7It4UJQ7zexIPIDKxfIIVwCHAh/DRYi+hkftehdwLy5YAi5fO9nMDsPDsDYr02Xslq5xNi72Ah6h7RIzOxyP8/6/+Qwp9Oj9wPR8gJ7El4FTJE0F5gKfNrPtwCW42M1kPHTtvJT+fGCpmR0CzAfeXKeigiAIgqAu/R7n4Hob0KP/IPCRbJWP/6Y/++JcaC7e8kdJz+PKWeDRut6ZXh8qaQ4uzrIvcGvJNX+a/s2Ls9wLzJJ0MD7JeLRuAVJ0sc/gwYnOMbP/SR8dB7wjRY8D2E8e/vhofKKDmd0saXPdawVBEARBHfp9ctAsRPNxM9uYTyDpb2gtmAFwJXCima2RNAOXri0iy7sty5vCpt4HfBj4maTPmdmdbZRjIvA74I2590bh8rwNwjO5yUIQBEEQ9IR+f6yQ51bgTKVvT0nvajP/GOApSa8BpreTUdJbgcfMbC5wAwO7EXXyvgX4F1wz/Pg0mQGPfX9mLt3h6eViXJQEScfj0qNBEARB0DVG0uTgQlwuda1ca/vCNvOfB9yHqxgWit1U8AlgnaQH8PMNV9XJlCYyl+Nytr/BzznMk6vlnQVMSgccH8LPNQB8FTg6lfFjwK/a9DUIgiAIKhkRP2UMgiAIgqB7jKSdgyAIgiAIukBMDoIgCIIgaCAmB0EQBEEQNBCTgyAIgiAIGojJQRAEQRAEDcTkIAiCIAiCBmJyEARBEARBAzE5CIIgCIKggf8HHlq6UUt2/UMAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "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=False, right=False)\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", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "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=False, right=False)\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", "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 }