{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Monetary Economics: Chapter 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From \"Monetary Economics: An Integrated Approach to Credit, Money, Income, Production and Wealth, 2nd ed\" by Wynne Godley and Marc Lavoie, 2012." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Simplest Model with Government Money, Model SIM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assumptions\n", "* No private money, only Government money (no private banks)\n", "* No profits, *pure labor economy*\n", "* Fixed price of labor, unlimited quantity of labor, thus the economy is not supply-constrained." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transactions matrix for Model SIM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "||1.Households|2.Production|3.Government|Σ|\n", "|-------|:------:|:--------:|:--:|-----|\n", "|1.Consumption|-C|+C||0|\n", "|2.Govt expenditures||+G|-G|0|\n", "|3.[Output]||[Y]|||\n", "|4.Factor income (wages)|+WB|-WB||0|\n", "|5.Taxes|-T||+T|0|\n", "|6.Change in the stock of money|-ΔH||+ΔH|0|\n", "|Σ|0|0|0|0|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition of terms\n", "\n", "* **C** : Consumption goods demand by households\n", "\n", "* **G** : Government expenditures\n", "\n", "* **Y** : National income\n", "\n", "* **WB** : Wage bill\n", "\n", "* **T** : Taxes\n", "\n", "* **ΔH** : Change in cash money\n", "\n", "In this model, people (as consumers and producers of income) have been separated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Behavioral (transactions) matrix for Model SIM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "||1.Households|2.Production|3.Government|Σ|\n", "|----------|:----------:|:----------:|:---:||\n", "|1.Consumption|-Cd|+Cs||0|\n", "|2.Govt expenditures||+Gs|-Gd|0|\n", "|3.[Output]||[Y]|||\n", "|4.Factor income (wages)|+W•Ns|-W•Nd||0|\n", "|5.Taxes|-Ts||+Td|0|\n", "|6.Change in the stock of money|-ΔHh||+ΔHs|0|\n", "|Σ|0|0|0|0|" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Differences from previous matrix:\n", "* Each transaction has a suffix, *s*, *d*, and *h*.\n", " * *s* supply\n", " * *d* demand\n", " * *h* household cash\n", "* The Wage Bill (WB) has been separated into two parts.\n", " * *W* Wage rate\n", " * *N* employment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Definition of terms\n", "* **Cd** : Consumption goods demand by households\n", "* **Cs** : Consumption goods supply by firms\n", "* **Gs** : Services supplied by the government\n", "* **Gd** : Services demanded from government \n", "* **Y** : National income\n", "* **W** : Wage rate\n", "* **Ns** : Supply of labor\n", "* **Nd** : Demand for labor\n", "* **Ts** : Taxes supplied\n", "* **Td** : Taxes demanded by government\n", "* **ΔHh** : Change in cash money held by households\n", "* **ΔHs** : Change in cash money supplied by the central bank" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model SIM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> From here, I will be building the model in code.\n", "> Because this is the first model, the Python code will\n", "> be explained in more detail also." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following piece of code is necessary to show the graphics inline for iPython notebooks. To view the graphs, matplotlib is required." ] }, { "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": [ "###### Preliminaries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to build the model, we must first start off by importing several modules that will be used to build the model. *pysolve* is a Python module that I have developed to make it easier to specify and solve linear models.\n", "\n", "The first line will import the main Model class. The second line imports several utility functions that will prove useful." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pysolve.model import Model\n", "from pysolve.utils import is_close,round_solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Create the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step when developing a pysolve model is to create the model. This is just an empty model for now, but we will be adding the rest of the information to this." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "model = Model()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Define the variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second step is to define the (endogenous) variables. These are the variables that we are allowed to manipulate within the model. This is pretty straigtforward.\n", "\n", "As a useful step, I define the default value for all variables. This can be changed on an individual basis. This is the value that the variable will start off with if nothing is changed." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "model.set_var_default(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we create the variables used by the sim. Most of these have been explained above." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "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('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');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an aside, multiple variables can be created by the following code. But the above is more descriptive.\n", "\n", "```python\n", "model.vars('Y', 'YD', 'Ts', 'Td', 'Hs', 'Hh', 'Gs', 'Cs',\n", " 'Cd', 'Ns', 'Nd')\n", "```\n", "\n", "The value of the variables may also be changed mid-iteration. They will then be used to seed the value of the next iteration.\n", "\n", "For example\n", "```python\n", "varx = model.var('x')\n", "\n", "# ... later\n", "varx.value = 22\n", "# this will also work\n", "model.variables['x'].value = 22\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aside: the semicolon ';' at the end of the last line of code is an iPython artifact, and is not needed by the python code. It is used to suppress output by the iPython interpreter." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Define the parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is to define the parameters. I do not differentiate between exogenous variables and parameters since both are set outside of the model. The solver will not be able to change these values. However, the user may change these values between calls to the solver.\n", "\n", "Like the variables, there is a call that may be made to set a default value for all parameters, but I will be creating the parameters with their default values. The call would look like this\n", "```python\n", "model.set_parameter_initial(1.0)\n", "```\n", "\n", "In addition the parameter values could be changed like this:\n", "```python\n", "Gd = model.param('Gd', initial=10)\n", "# ...\n", "# at some later time\n", "Gd.value = 20\n", "# or this would work also\n", "model.parameters['Gd'].value = 20\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some of the parameters (alpha1, alpha2 and theta) have not been explained yet, but will be explained when we add the equations that use them." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "model.param('Gd', desc='Government goods, demand', default=20.)\n", "model.param('W', desc='Wage rate', default=1.)\n", "model.param('alpha1', desc='Propensity to consume out of income', default=0.6)\n", "model.param('alpha2', desc='Propensity to consume o of wealth', default=0.4)\n", "model.param('theta', desc='Tax rate', default=0.2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Define the equations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Adding an equation is just adding the textual form of the equation. There are some restrictions. Linear systems only." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "model.add('Cs = Cd')\n", "model.add('Gs = Gd')\n", "model.add('Ts = Td')\n", "model.add('Ns = Nd');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These four equations imply that demand equals supply for this period, no supply constraints of any kind." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "model.add('YD = (W*Ns) - Ts');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Disposable income (*YD*) is the wages earned by households minus taxes." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "model.add('Td = theta * W * Ns');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Taxes are a fixed proportion (*theta*) of income. *theta* is decided by the government and is exogenous to the model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "model.add('Cd = alpha1*YD + alpha2*Hh(-1)');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a consumption function, the rates at which housholds consume. This is a combination of consumption of inherited wealth (*Hh(-1)*) and post-tax income (*YD*)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "model.add('Hs - Hs(-1) = Gd - Td');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This comes from the transaction-flow matrix and represents the governments budget constraint. Government expenditures that are not paid for by taxes (*Gd-Td*), must be covered by differences in the money supply." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "model.add('Hh - Hh(-1) = YD - Cd');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The difference in the cash that households carry is the difference between their income and their consumption." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "model.add('Y = Cs + Gs');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The determination of national income." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "model.add('Nd = Y/W');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The determination of employment." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have 11 equations and 11 unknowns. **Each of the eleven unknowns has been set on the left-hand side of an equation** (This implies that we can use the Gauss-Seidel algorithm to iterate to a solution, convergence is not guaranteed but we can try.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Solve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have set the default for all of the variables to 0, and that will be used as an initial solution." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "model.solve(iterations=100, threshold=1e-4);" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Y : 38.5\n", "T : 7.7\n", "YD : 30.8\n", "C : 18.5\n", "Hs-Hs(-1) : 12.3\n", "Hh-Hh(-1) : 12.3\n", "H : 12.3\n" ] } ], "source": [ "prev = round_solution(model.solutions[-2], decimals=1)\n", "solution = round_solution(model.solutions[-1], decimals=1)\n", "print(f\"Y : {solution['Y']}\")\n", "print(f\"T : {solution['Ts']}\")\n", "print(f\"YD : {solution['YD']}\")\n", "print(f\"C : {solution['Cs']}\")\n", "print(f\"Hs-Hs(-1) : {solution['Hs'] - prev['Hs']}\")\n", "print(f\"Hh-Hh(-1) : {solution['Hh'] - prev['Hh']}\")\n", "print(f\"H : {solution['Hh']}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The code for the full model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make the model easier to manipulate, I will encapsulate model creation into a single function." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def create_sim_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('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", "\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 out 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*YD + alpha2*Hh(-1)') # 3.7, 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('Y = Cs + Gs') # 3.10\n", " model.add('Nd = Y/W') # 3.11\n", " \n", " return model\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run the simulation using the model." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Y : 38.5\n", "T : 7.7\n", "YD : 30.8\n", "C : 18.5\n", "Hs-Hs(-1) : 12.3\n", "Hh-Hh(-1) : 12.3\n", "H : 12.3\n" ] } ], "source": [ "model = create_sim_model()\n", "model.set_values({'alpha1': 0.6,\n", " 'alpha2': 0.4,\n", " 'theta': 0.2,\n", " 'Gd': 20,\n", " 'W': 1})\n", "model.solve(iterations=100, threshold=1e-5)\n", "\n", "prev = round_solution(model.solutions[-2], decimals=1)\n", "solution = round_solution(model.solutions[-1], decimals=1)\n", "print(f\"Y : {solution['Y']}\")\n", "print(f\"T : {solution['Ts']}\")\n", "print(f\"YD : {solution['YD']}\")\n", "print(f\"C : {solution['Cs']}\")\n", "print(f\"Hs-Hs(-1) : {solution['Hs'] - prev['Hs']}\")\n", "print(f\"Hh-Hh(-1) : {solution['Hh'] - prev['Hh']}\")\n", "print(f\"H : {solution['Hh']}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Steady-state solution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now generate the steady-state solution by iterating until the solutions converge." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Y : 100.0\n", "T : 20.0\n", "YD : 80.0\n", "C : 80.0\n", "Hs-Hs(-1) : 0.0\n", "Hh-Hh(-1) : 0.0\n", "H : 80.0\n" ] } ], "source": [ "steady_state = create_sim_model()\n", "steady_state.set_values({'alpha1': 0.6,\n", " 'alpha2': 0.4,\n", " 'theta': 0.2,\n", " 'Gd': 20,\n", " 'W': 1})\n", "for _ in range(100):\n", " steady_state.solve(iterations=100, threshold=1e-5)\n", "\n", " prev_soln = steady_state.solutions[-2]\n", " soln = steady_state.solutions[-1]\n", " if is_close(prev_soln, soln, atol=1e-4):\n", " break\n", "\n", "prev = round_solution(steady_state.solutions[-2], decimals=1)\n", "solution = round_solution(steady_state.solutions[-1], decimals=1)\n", "print(f\"Y : {solution['Y']}\")\n", "print(f\"T : {solution['Ts']}\")\n", "print(f\"YD : {solution['YD']}\")\n", "print(f\"C : {solution['Cs']}\")\n", "print(f\"Hs-Hs(-1) : {solution['Hs'] - prev['Hs']}\")\n", "print(f\"Hh-Hh(-1) : {solution['Hh'] - prev['Hh']}\")\n", "print(f\"H : {solution['Hh']}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Table 3.4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also generate table 3.4" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Period123∞
G20.020.020.020.0
Y0.038.547.9100.0
T0.07.79.620.0
YD0.030.838.380.0
C0.018.527.980.0
ΔHs0.012.310.40.0
ΔHh0.012.310.40.0
H0.012.322.780.0
" ], "text/plain": [ "" ] }, "execution_count": 20, "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'), ('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", "s = generate_html_table(['Period', '1', '2', '3', '∞'], data)\n", "HTML(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scenario: Model SIM, increase government expenditures" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "step_model = create_sim_model()\n", "step_model.set_values({'alpha1': 0.6,\n", " 'alpha2': 0.4,\n", " 'theta': 0.2,\n", " 'Gd': 20,\n", " 'W': 1})\n", "\n", "# Use the steady state solution as a starting point\n", "step_model.solutions = steady_state.solutions[-2:]\n", "for i in range(45):\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 3.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the solution but with an permanent increase in government expenditures (+5) and starting from the steady state solution." ] }, { "cell_type": "code", "execution_count": 22, "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 3.1 Impact on national income Y and the steady state solution Y*,\n", " following a permanent increase in government expenditures ($\\\\bigtriangleup$G = 5).'''\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", "# Now graph G/theta and Y\n", "fig = plt.figure()\n", "axes = fig.add_axes([0.1, 0.1, 1.1, 1.1])\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(.1, -.1, caption);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Figure 3.2" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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33gtAQUEBo0aNonv37iQmJvLEE08A8NJLL9G3b1969uzJxRdfTGbhmbDAZ599xoABA+jcuTMffPDBITFnZGRw/fXX07dvX3r16sW7774b7mo6LBv2bKDvS33p/3J/3l75Nr/t9VuW37ScPq2K/IFxzmuVgpdAN26E7du959HRcPnlXkf6IiKRxDlXabfevXu7o0HdunUPmdaoUSO3bds2N3v2bHf++ec755wbNmyYmzdvnnPOubS0NJeXl+dmz57t4uLi3Lp161x+fr4788wz3bRp09zmzZvd8ccf73bs2OHy8vLckCFD3DvvvOMWL17szjzzzP3r2b17t3POudTU1P3T7r//fvf0008755wbOXKkO/vss11BQYFbs2aNO/bYY11WVtZBcd13333u1Vdf3b+8Tp06ufT09IqvqMOQnZftlm9f7pxzLic/xw2ZOMQ9t/A5tzd7b8kz3X23cw0aOLdvn/c8EKiESEVEyqXcOa/Gt2ALuWK6jDzttNO46667ePrpp9mzZw8xMd4e9X79+tG+fXuio6O58sormTdvHosWLWLw4ME0b96cmJgYrrrqKr744gvat2/P+vXrue222/j4449p0KABACtWrGDAgAEkJiYyefJkvv/++/3rveyyy4iKiqJTp060b9+eVatWHRTXzJkzeeyxx0hKSmLw4MFkZ2ezadOmMNZOaFl5WTyz4Bk6PN2BcyefS25BLrWia/H5yM+5ue/NNIhrcKBwTg488wxs2+Y9v/pqePppiIvznqubQhGpBpRggfXr1xMdHU2LIgNfjx07lpdffpmsrCxOPvnk/YnOiiQAMys2QQM0btyYpUuXMnjwYJ577jlu8M96HTVqFM8++yzLly/nL3/5C9nZ2Qctr+jygznneOutt0hOTiY5OZlNmzZxwgknHN7GH6GM3AzGfT2O9k+35/aPb6djk45MGjGJ2KhSjpX+/LPXcf7Uqd7znj1h5EjvelYRkWqixifYlJQUxowZw6233npIIlu3bh2JiYnce++99OnTZ3+CXbhwIT/99BOBQICpU6dy+umn079/f+bOnUtqaioFBQVMmTKFQYMGkZqaSiAQ4OKLL+bhhx/mu+++AyAtLY2EhATy8vKYPHnyQeudNm0agUCAdevWsX79erp06XLQ62effTbPPPPM/qS+ZMmScFVPSHM3zuUPM//Aic1PZM7IOcwZNYcz2p1xSF0yfTr89a/e444dYflyr19gEZFqqkaeRZyVlUVSUhJ5eXnExMRwzTXXcNdddx1S7sknn2T27NlER0fTrVs3zj33XL7++mtOOeUUxo4dy/Lly/ef8BQVFcWjjz7KkCFDcM5x3nnnMXz4cJYuXcp11123/0SoRx99FICHH36Y/v3706ZNGxITE0lLS9u/3i5dujBo0CC2b9/OCy+8QHzwkGnAAw88wB133EGPHj1wztG2bdtiT4YKB+ccU1ZMYWfmTm7rfxvndjyXxTcupner3qXP+OWX3u2++7yWahW1uEVEKouGqyunOXPm8Pjjj1daQjuafLvlW27/+Hbm/zyf01ufztxRc4myEnaCrFkDo0bBiy96l9xkZnodRUTV+J0mIhKZyn1yiH7tJKQdGTu44b0b6PtSX9buWst/LvxP6ckVoGlTyMg4cLlNnTpKriJSo6gFKyElb0vm5JdP5tZ+t/LAwAdoGN+w+ILTpnmd7k+c6J0J7JzOCBaR6qLiB1w3sy7A1KBJ7YE/A//1p7cFNgCXOed2lzcAOTrNXDeTeZvm8dCQh0g6Jomf7/yZ5nWblz7T1q2wahXs3QuNGim5ikiNFnKfnXNutXMuyTmXBPQGMoF3gLHALOdcJ2CW/1wiXHpuOr97/3ec/drZvPn9m2TkZgAUn1yzs+GBB+CTT7znt9wC8+d7yVVEpIYr70GxocA659xGYDgwyZ8+CRhRgXFJFfhq01f0fKEnL333Eveceg/JY5KpW6tuyTOYeZffzJ3rPY+O9m4iIlLuBHsFMMV/3NI5txXAv29R4lxHmXr16lV1CMX6/vvv6dy5M1lZWfunnX/++bzxxhtMnDiR5s2b06tXLzp16sTZZ5/N/PnzK2zd+3L2cf7r5+OcY+6oufzzV/8kPib+0IJ5efDcc959XBwsXAh//3uFxSEiUl2UOcGaWS3gQmBaeVZgZqPNbLGZLU5JSSlvfDXKiSeeyEUXXcQjjzwCwIwZM8jLy+MKfxDxyy+/nCVLlvDjjz8yduxYLrroIlauXHlE61y7ay3OORrENeD9K99n6ZilDGgzoOQZPv8cbr31wPBx9esf0fpFRCJBcC7zb6NDzVOeFuy5wHfOOf+6C7abWYK/4gRgR3EzOefGO+f6OOf6NG8e4iSZSjZnzhwGDx7MJZdcQteuXbnqqqv29460aNEiTj31VHr27Em/fv1IS0sjOzub6667jsTERHr16sXs2bMBmDhxIiNGjOCCCy6gXbt2PPvss4wbN45evXpx8skns2vXLsDrGeqcc86hd+/eDBgw4JA+hgH+/Oc/M23aNJKTkxk7dizPPfdcsbEPGTKE0aNHM378+MPa9vxAPo9++SjdnuvGpKXenv4BbQZQP66EhLl5s3d/9tmwYAFcfPFhrVdEJBIF5zL/FvLHtzwJ9koO7B4GeA8Y6T8eCRydY6aFsGTJEp588kl++OEH1q9fz1dffUVubi6XX345Tz31FEuXLuWzzz6jdu3a+5Pd8uXLmTJlCiNHjtzfh/CKFSt4/fXXWbhwIffffz916tRhyZIlnHLKKfz3v/8FYPTo0TzzzDN8++23PP7449x8882HxFOnTh0ef/xxBg4cyBVXXEGnTp1KjP2kk04qNkmH8su+Xxg0cRB/+vxPjOg6ggs6X1D6DP/6F5x4IhQOKNCvX7nXKSJS05Spq0QzqwP8Cvhd0OTHgDfN7LfAJuDSig8v/Pr168dxxx0HQFJSEhs2bKBhw4YkJCTQt29fgP0j4MybN4/bbrsNgK5du9KmTRvWrFkDeC3K+vXrU79+fRo2bMgFF3hJKzExkWXLlpGens78+fO59NID1ZSTk1NsTBdccAGNGjUqNgEHO5xrmOf/PJ+Lpl5ERl4Gky+azJXdrzy03+CiLrnE6zQiIaHc6xMRqanKlGCdc5lA0yLTduKdVRzR4gqHSAOio6PJz8/HOVds0iktoQUvJyoqav/zqKgo8vPzCQQCNGrUiOTk5DLFFRUVRVSIno+WLFlS7lF0dmftpmF8Q2ZdO4sTW5xYcsHnnvOuaX3mGWjXDh58sFzrERGp6dR3XTG6du3Kli1bWLRoEeCNfJOfn8/AgQP3j3yzZs0aNm3adMhINyVp0KAB7dq1Y9o07xwx5xxLly497Bjnzp3L+PHjufHGG0OWzSvI4/OfPgfg/M7ns+KmFaUnV4BffoENG7yzhUVEpNxq5Gg6odSqVYupU6dy2223kZWVRe3atfnss8+4+eabGTNmDImJicTExDBx4sSDWq6hTJ48mZtuuom//e1v+88O7tmzZ5nnnzp1KvPmzSMzM5N27drx1ltvhWzBpmSkcNn0y/hi4xesumUVnZp2Ija6hLFa09Jg505o2xb8M5nVf7CIyOFRX8TV2NJtSxn+xnC2pW/j5Qtf5uoeV5c+w1lneYOhL18OMfrvJSISpOL7IpbINP2H6YycMZLG8Y358rov6Xts39AzPfig14pVchUROWL6Ja2m1u9eT9IxSbx12VscU++Ykgt+8AFs2wY33ACnnlp5AYqIVHPaRVyNOOf4Zd8vHN/weJxz5AXyqBVdq7QZYMQIb8zWr75SP8IiIiXTgOs1lXOOu2feTY8XerBhzwbMrOTkGgh4I+GYweTJMGuWkquISAVTgq0GAi7AzR/ezLhvxnFNj2to3bB16TPcfDNccAHk5kK9elC3lBFzRETksOgYbITLD+Rzw3s3MGnpJO497V4eHfpo6J6ZTj0VWrSA2BIu1xERkSOmBBvhnl34LJOWTuKhwQ/xfwP/r+Tk6hxs3Ohd43rttZUao4hITaQEG+Fu6nMTxzU4jku6XVJ6wUcegccfh6VLoU2byglORKQG0zHYCJSZl8ktH95CamYqcTFxoZMreK3We++F1iGOz4qISIVQgo0wuQW5XDjlQl749gXmbZpXemHn4JNPvPvWreG++7wzh0VEJOyUYCNIwAW4/t3rmfXTLCZcOIERXUeUPsO778I558B771VKfCIicoASbAS5f9b9TF4+mUfOeISRSSNDz3DhhfD6694lOSIiUqmUYCNEWk4ab696m9/1/h33nX5f6YWnToWUFG8knCuv1Ig4IiJVQF0lRpDdWbupH1efmKhSTv7evh3at/f6Fn7qqcoLTkSkelNXidXNgl8WcON7N5JbkEvj2o1LT64ALVvCvHnw6KOVE6CIiBRL18EexdbuWsuwKcNoENeAvdl7aV63ecmFd+zwrnH91a+gV6/KC1JERIqlFuxRKiUjhXNeOwfnHB9f9XHpyRXggQfgoosgNbVyAhQRkVIpwR6FMvMyuWDKBWxO28z7V75Pp6adQs80bhx8+CE0axb+AEVEJCQl2KPQ6tTVrNm5hikXT+GU408pvfDMmZCX542IM3Bgude1bds2rrjiCjp06EC3bt0477zzWLNmzWFGXnmSk5P56KOP9j9/7733eOyxxypk2ZFaJyJydNFZxEepPdl7aBTfqPRCq1dDt27w0ENw//3lXodzjlNPPZWRI0cyZswYwEtcaWlpDBgw4DCirjwTJ05k8eLFPPvssxW63EiuExEJK51FHMnmbZrHuK/H4ZwLnVwBunSBd96BO+88rPXNnj2b2NjY/YkEICkpiQEDBuCc45577qF79+4kJiYydepUAObMmcPgwYO55JJL6Nq1K1dddRWFf9LGjh1Lt27d6NGjB3fffTcAo0aNYvr06fuXX69evf3LGTRoEJdddhmdO3dm7NixTJ48mX79+pGYmMi6dev2zz9mzBgGDBhA586d+eCDD8jNzeXPf/4zU6dOJSkpialTpzJx4kRuvfVWADZu3MjQoUPp0aMHQ4cOZdOmTfuXdfvtt3PqqafSvn37g+IqS52IiJSHziI+SqRmpnLF9CuoHVub0b1HU69WvZIL79gBu3ZB165eb02HacWKFfTu3bvY195++22Sk5NZunQpqamp9O3bl4H+LuglS5bw/fff06pVK0477TS++uorunXrxjvvvMOqVaswM/bs2RNy/UuXLmXlypU0adKE9u3bc8MNN7Bw4UKeeuopnnnmGZ588kkANmzYwNy5c1m3bh1Dhgxh7dq1PPTQQwe1YCdOnLh/ubfeeivXXnstI0eOZMKECdx+++3MmDEDgK1btzJv3jxWrVrFhRdeyCWXHDxQQml1IiJSHmVqwZpZIzObbmarzGylmZ1iZk3M7FMz+9G/bxzuYKurgAswcsZIUjJTePOSN0tPrgCjR8MZZ0BWVthimjdvHldeeSXR0dG0bNmSQYMGsWjRIgD69evHcccdR1RUFElJSWzYsIEGDRoQHx/PDTfcwNtvv02dOnVCrqNv374kJCQQFxdHhw4dOOusswBITExkw4YN+8tddtllREVF0alTJ9q3b8+qVatKXe7XX3/Nb37zGwCuueYa5s07MCjCiBEjiIqKolu3bmzfvr281SIiUmZl3UX8FPCxc64r0BNYCYwFZjnnOgGz/OdyGP49/9989ONHjDtrHL0SynAN69NPw3/+A7VrH9F6TzzxRL799ttiXyvt2HxcXNz+x9HR0eTn5xMTE8PChQu5+OKLmTFjBueccw4AMTExBAKB/cvMzc0tdjlRUVH7n0dFRZGfn7//taKDyJc4qHwJgssHr7O4bSytTkREyiNkgjWzBsBA4D8Azrlc59weYDgwyS82CRgRnhCrt61pW/m/2f/HJd0u4ea+N5deeO9e7751azj33CNe9xlnnEFOTg4vvfTS/mmLFi1i7ty5DBw4kKlTp1JQUEBKSgpffPEF/fr1K3FZ6enp7N27l/POO48nn3yS5ORkANq2bbs/Yb377rvk5eWVO85p06YRCARYt24d69evp0uXLtSvX5+0tLRiy5966qm88cYbAEyePJnTTz+9zOsqrU5ERMqjLC3Y9kAK8IqZLTGzl82sLtDSObcVwL9vEcY4q62E+gnMvHomL1/wcukts8xMOPlk+OMfK2zdZsY777zDp59+SocOHTjxxBN58MEHadWqFb/+9a/p0aMHPXv25IwzzuCf//wnxxxzTInLSktLY9iwYfTo0YNBgwbxxBNPAHDjjTcyd+5c+vXrx4IFC6hbt2654+zSpQuDBg3i3HPP5YUXXiA+Pp4hQ4bwww8/7D/JKdjTTz/NK6+8Qo8ePXj11Vd5qhx9MpdWJyIi5RHyMh0z6wN8A5zmnFtgZk8B+4DbnHONgsrtds4dchzWzEYDowFat27de+PGjRUYfuRyzrFk2xJOSjipbDPk5cHf/gYDBsCZZ4Y3uKPIqFGjGDZs2CEnI4mIVCYz+x1+LvONd86NL22esrRgfwF+cc4t8J9PB04CtptZgr/iBGBHcTM758Y75/o45/o0bx6iu78a5MlvnqTP+D5888s3ZZshNhb++tcalVxFRI4WwbnMv5WaXKEMCdY5tw342cy6+JOGAj8A7wGFo36PBN49zLhrnIWbF3LvZ/cyvOtw+h/bv/TCO3bA4MFeR/410MSJE9V6FZGIVNbrYG8DJptZLWA9cB1ecn7TzH4LbAIuDU+I1cue7D1cPv1yWtVvxYQLJ4Q+I/bnn+GXXyA6unICFBGRClGmBOucSwb6FPPS0AqNpga4Z+Y9/Lz3Z+ZdP4/Gtctw6XDv3rBqFcSoTxARkUiirhIr2YktTuSBgQ9w8nEnl15w9Wp49llwTslVRCQCqbP/o9Uf/+h1JrFyJbTQFVAiIlVMnf0frZ74+gleX/562Wf4xz9g0SIlVxGRCKUEWwlWpqzk3s/u5aMfPwpdeP162L0bzKB9+/AHJyIiYaEEG2YBF2D0B6OpH1efcWePCz3DqFFeZxJ+/70iIhKZdPZMmL383cvM2zSPCRdOoEXdMuzuffpp2LIFovTfR0QkkukkpzDalbWL9k+1p1dCLz6/9vNyjwIjIiJHjXL/gKsFG0aN4xszYfgEurfoHjq53nOP12p97DHv+KuIiEQ0JdgwySvIIzY6lotOuCh0YecgPd3rrUnJVUSkWtCBvjBIz00n8flEJiyZULYZzOD55+GZZ8IbmIiIVBol2DD48+w/s3rnaro07RK68OzZsHat91itVxGRakO7iCvYt1u+5akFTzGm9xhOa31a6YUDAbjpJmjeHL78snICFBGRSqEEW4Gcc9z2v9toUbcFj575aOgZoqK8FuzeveEPTkREKpUSbAX6but3fPPLN7w47EUaxTcqvXBWFtSuDQkJ3k1ERKoVJdgK1LtVb5bdtIyuzbqWXtA5OP986NABXnqpcoITEZFKpQRbQdJy0qgfV5/uLbqHLhwIwJlnwjHHhD8wERGpEjqLuAJk52eT+HwiD899uGwzREfDn/4E118f3sBERKTKKMFWgOcWPsfGvRs5vfXpoQu/+irMmhX+oEREpEppF/ER2pu9l7/P+ztndTiLIe2GlF44EIB//hM6doShQysnQBERqRJKsEfoX/P/xa6sXTw29LHQhaOivEHUdVmOiEi1pwR7BLLzs3npu5e4ovsV9EroVXrhnByIjYX4eO8mIiLVmhLsEYiPiWfZmGXkB/JDF/7b3+CDD2D+fO/6VxERqdaUYA9TRm4GdWLr0LJey7LN0LMnZGcruYqI1BAacP0wXTH9CnZn7+bjqz7WQOoiItVfuX/odZnOYfh2y7dM/X4qfVv1DZ1cU1Nh4kTIL8NuZBERqTbKlGDNbIOZLTezZDNb7E9rYmafmtmP/n3j8IZ69Lhv1n00rd2Ue069J3ThV1/1OpQoHJJORERqhPK0YIc455Kcc33852OBWc65TsAs/3m1N2v9LD5d/yl/GvAnGsY3DD3DHXfAwoXQNUT/xCIiUq0cyS7i4cAk//EkYMQRRxMBxn0zjuMbHM/NfW8OXTgQ8AZR79MndFkREalWyppgHTDTzL41s9H+tJbOua0A/n2LcAR4tJl+6XRmXjOT+JgQ17Ju2wadO8PMmZUTmIiIHFXKepnOac65LWbWAvjUzFaVdQV+Qh4N0Lp168MI8egRcAFqx9YOPRwdeL01tWkDbduGPS4REQmv4FzmG++cG1/qPOW9TMfMHgTSgRuBwc65rWaWAMxxznUpbd5Ivkzn+x3fc+EbFzLl4in0O7ZfVYcjIiKVq+Iv0zGzumZWv/AxcBawAngPGOkXGwm8W96VR5JxX49ja9pWOjTuELrwJ5/Avn3hD0pERI5aZTkG2xKYZ2ZLgYXAh865j4HHgF+Z2Y/Ar/zn1dLWtK28tvw1rku6jqZ1mpZeePduGDEC7ruvUmITEZGjU8hjsM659UDPYqbvBGrEmGvPLnyWvII87jzlztCFGzeGr76CZs3CH5iIiBy11BdxCBm5GTy/+Hl+fcKv6dikY9lmOumk8AYlIiJHPSXYEGrH1uaV4a/QplGb0IUnTIDvv4fHHvOGphMRkRpLCTaEKItieNfhZSu8erU3oLqSq4hIjafO/kvx4ZoPeeDzB8jKyyrbDP/4B3z+eXiDEhGRiKAEWwLnHA9/8TBTVkyhVnSt0DPs2uXdx2ingIiIKMGWaP7P81mweQF3nXIX0VHRpRdeswaOOQamT6+c4ERE5KinBFuCx79+nCa1mzAqaVTowvXrw+23w+mnhz0uERGJDNqfWYw1O9fw7qp3uX/A/dSJrRN6hoQEePzx8AcmIiIRQy3YYjjnuKTbJdzS75bQhWfNguTksMckIiKRpdyd/R+JSO7sv0S9e3snNi1YUNWRiIhI+JS7s3/tIi7iwzUf0rFJR7o0K3VgoAM++wy2bg1vUCIiEnG0izhIVl4W1717HX+Y+Yeyz9S4MXTrFr6gREQkIinBBpn2wzRSMlO465S7QhdeuxbOPRdWlXnseRERqUGUYINMWDKBjk06MqTtkNCF16+HlSuhYcPwByYiIhFHCda3dtda5m6cy3VJ12FWhmPZZ53lJdmEhPAHJyIiEUcJ1pe8LZl6tepxbc9rQxfetg2cgyhVn4iIFE+X6QTJysuidmzt0gs5553U1Ls3vPZa5QQmIiJVTZfpHI7CxBoyuQIUFMA993h9D4uIiJRACRYYOWMke3P28snVn4QuHBMD118f/qBERCSi1fiDiKmZqcxYNYNuzcpwLevWrTBlCmRmhj8wERGJaDU+wb6+/HXyAnlc36sMrdLp0+E3v4Fffgl/YCIiEtFqfIJ9JfkV+rTqQ2LLxNCFb7nF63O4c+fwByYiIhGtRifYJVuXkLwtmeuTynhMNSoK+vULb1AiIlIt1OiTnDo37cwrw19heJfhoQs/9RRkZ8O994Y/MBERiXg1OsHWrVWXUUmjylb4m28gKyus8YiISPVR5l3EZhZtZkvM7AP/eRMz+9TMfvTvG4cvzIo3c91MnvzmSXLyc8o2w5QpMG1aeIMSEZFqozzHYH8PrAx6PhaY5ZzrBMzyn0eMf3/9b5745glio2NDFy4o8O5jy1BWRESEMiZYMzsOOB94OWjycGCS/3gSMKJCIwujTXs38em6T7ku6TqiLEQVZGZCmzbwyiuVE5yIiFQLZW3BPgn8EQgETWvpnNsK4N+3KG5GMxttZovNbHFKSsqRxFphJiVPwuHKdvx13z5v5JxOncIel4iIHJ2Cc5l/Gx1ynlCd/ZvZMOA859zNZjYYuNs5N8zM9jjnGgWV2+2cK/U47NHQ2X/ABej4dEfaN27PZ9d+VqWxiIhIxAhLZ/+nARea2XlAPNDAzF4DtptZgnNuq5klADvKu/KqsCtrF+0at+O3vX4buvCePbB3r7eLWEREpBxC7iJ2zt3nnDvOOdcWuAL43Dl3NfAeMNIvNhJ4N2xRVqBmdZox69pZXJl4ZejCkyZB27bw009hj0tERKqXI7kO9jHgTTP7LbAJuLRiQgqf9Nx0MnIzaFmvZdlmGDEC4uKgXbuwxiUiItVPubpKdM7Ncc4N8x/vdM4Ndc518u93hSfEijN52WSOHXcsP+78sWwztGkDY8aENygREamWalRfxG98/wadm3amY5OOoQvPmAFffBH2mEREpHqqMQk2JSOFLzZ+wSXdLsGsDCeDPfAA/P3v4Q9MRESqpRrTF/GMVTMIuAAXn3Bx2Wb45hs4Sq7bFRGRyFNjEuzbq96mfeP29GjZo2wz1K3r3URERA5DjdlF/Pz5z/PK8FdC7x7OzoZhw2Du3MoJTEREqqUa04Jt26gtbRu1DV1wwwZYswZyyjjKjoiISDFqRIL9x7x/cELzE7iwy4WhC3ftCqtXhz8oERGp1qr9LuL03HQenPsgs9bPCl04NxcCATDzbiIiIoep2ifYj378iOz8bC7uVoazh197Ddq3h+3bwx+YiIhUa9U+wb618i1a1G3BacefFrpw69YwZAi0KHbkPRERkTKr1sdgs/Ky+HDNh1zd42qio6JDz3Dmmd5NRETkCFXrFuzP+36mbaO2ZetcYssWb3B1ERGRChBywPWKVFUDrjvnQl//euON8O67sHUrRJehtSsiIjVJWAZcj0j5gXwKAgXExcSVre/hG26AgQOVXEVEpEJU213Es9bPovm/mvPd1u/KNkP//nDNNeENSkREaoxqm2DfWvkWDke35t1CF549G1asCH9QIiJSY1TLXcQFgQJmrJrB+Z3OJz4mPvQMt93mXZrz+efhD05ERGqEaplgv9z0JSmZKWUfmu7TT2HnzvAGJSIiNUq1TLBv/fAW8THxnNvp3LLNkJDg3URERCpItUyw1/e6nt6telOvVr3Qhf/+d+8Ep6FDwx+YiIjUGNXyJKdeCb0YlTQqdMHsbHjqKe8kJxERkQpU7Vqw76x8h0bxjRjSbkjowvHxsHkzZGWFPzAREalRqlWCdc7xh5l/oGuzrmVLsAAxMVC/fngDExGRGqda7SJO3pbMT3t+KtvZwzk5cMYZ8PHH4Q9MRERqnGqVYN9a+RZRFsXwrsNDF96yBdLTvQHWRUREKljIXcRmFg98AcT55ac75/5iZk2AqUBbYANwmXNud/hCDe39Ne8zqM0gmtVpFrpwu3awcGH4gxIRkRqpLC3YHOAM51xPIAk4x8xOBsYCs5xznYBZ/vMqlZKRQo+WPUIXdA4KCsIfkIiI1FghE6zzpPtPY/2bA4YDk/zpk4AR4QiwPLb8YQv/PuvfoQsuWQLHHAPz5oU/KBERqZHKdAzWzKLNLBnYAXzqnFsAtHTObQXw71uUMO9oM1tsZotTUlIqKOySRUeVYbi5WrXg7LOhS5ewxyMiIpEvOJf5t9Eh5ynPgOtm1gh4B7gNmOecaxT02m7nXOPS5g/ngOvrd6/nz7P/zD2n3kPPY3qGZR0iIlJjlXvA9XKdReyc2wPMAc4BtptZAoB/v6O8K69IP+3+icnLJ7Mne0/pBffsge3bKyMkERGpwUImWDNr7rdcMbPawJnAKuA9YKRfbCTwbphiLJPUzFQAmtdtXnrB117zOvbftKkSohIRkZqqLD05JQCTzCwaLyG/6Zz7wMy+Bt40s98Cm4BLwxhnSCmZ3vHdkJfo/OpX8MQT0Lp1JUQlIiI1VcgE65xbBvQqZvpO4KgZgqawBdukdpPSC3bpopObREQk7KpVT07tG7cnJqqU/wzJyTB/vnpvEhGRsCvXWcRHKpxnEZfJyJHw4YfeSU7RZbicR0RExBPes4gj3hNPwEcfKbmKiEjYVZsEe8N7N/Cvr/5VeqEmTaBfv8oJSEREarRqMx7sRz9+hJXWgp81CzZsgFGj1IIVEZGwqxYtWOccqZmppV+i8/rr8Ne/QlS12GQRETnKVYsWbFpuGnmBvNI7mXj5Zdi2Dazcx6lFRETKrVo051IyytDJhJnXg5OIiEglqBYJNrcgl+4tunNcg+OKL/DGG/DHP0J+fuUGJiIiNVa1SLAnND+B5Tct54x2ZxRfYOlS+N//IKZa7BEXEZEIUHM6migo0NnDIiJyuGpmRxMTlkzgtAmnkZ2fXXIhJVcREalE1SLBrk5dzeIti4mLjjv0xaefhhEjdPxVREQqVbVIsCmZKTSv0xwr7hKcwo79dfxVREQqUbVIsKV2MnHHHTBjRmWGIyIiUs0TbEFB5QcjIiJCNUmwJzQ7gf7H9j/0hb/8Bbp31/FXERGpdNXiwOR/hv+n+BcSEyErS8dfRUSk0tWc62BFREQOX827DnZ7+nbaPdWOad9PO/iFXbsgu5TrYkVERMIo4hPsjowdbNiz4dAXHnnE69w/L6/SYxIREYn4g5OpmalAMSPpjBgB7dtDbGzlByUiIjVetUmwh4wFO2CAdxMREakCEb+LOCWzmLFg162D1auhEk/gEhERCRYywZrZ8WY228xWmtn3ZvZ7f3oTM/vUzH707xuHP9xDHdfgOIZ1HkbT2k0PTPz3v6FPH13/KiIiVSbkZTpmlgAkOOe+M7P6wLfACGAUsMs595iZjQUaO+fuLW1ZlXaZzk8/wcqVcN554V+XiIjUBOW+TCfkMVjn3FZgq/84zcxWAscCw4HBfrFJwByg1ARbadq1824iIiJVpFzHYM2sLdALWAC09JNvYRJuUeHRlcH5r5/PBVMuODBh0SJ47z3tHhYRkSpV5gRrZvWAt4A7nHP7yjHfaDNbbGaLU1JSDifGUm1J24IFt9xfeAGuvx6iIv78LREROUoE5zL/NjrUPGXKQmYWi5dcJzvn3vYnb/ePzxYep91R3LzOufHOuT7OuT7NmzcvrsgRScnwxoLd7//9P/jiCyVYERGpMMG5zL+NDzVPWc4iNuA/wErn3Ligl94DRvqPRwLvHk7QR8I5d+hQdXFx0K1bZYciIiJykLI0804DrgHOMLNk/3Ye8BjwKzP7EfiV/7xSZeRlkFOQc6CTiY8/hn/9C3JzKzsUERGRg5TlLOJ5lHx68tCKDad88gP5jOk9ht4Jvb0Jn3wC06bB3XdXZVgiIiLVcLi6tDSoXz+86xARkZqmZg1Xl1eQR0Gg4OCJSq4iInIUiOgEO2XFFGIfjmX97vWwcSPccAOsWFHVYYmIiER2gk3JSMHhvH6IN2+GDz7wdhGLiIhUsYgeri41M5XYqFgaxDWAU0+FbduqOiQREREg0luwmSk0q9MM71JdERGRo0dEJ9iDOpn4y1/g4YerNiARERFfRO8i/nXXX5OW6x9zXbcOYiJ6c0REpBqpftfBioiIVLyadR3slrQt5OTnVHUYIiIih4jYBJsfyOfYccfy6LxHITkZzj5b18CKiMhRI2IT7K6sXQDeSU7p6bBrF9SqVcVRiYiIeCL2rKCUDG/w9uZ1mkO/02HRoiqOSERE5ICIbcGmZqYCHDwWrIiIyFEiYhNsSqbfgq3bHEaOhD/+sYojEhEROSBiE2z3Ft35x5n/oHXD1lCvHtSpU9UhiYiI7KfrYEVEREKrOdfBbtq7iQ17NlR1GCIiIsWK2AT7p1l/Yuh/h8Jnn0G3bvDDD1UdkoiIyH4Rm2BTMlO8S3Tq1IGuXaFp06oOSUREZL+IvQ42NTOVhHoJ3jiwb79d1eGIiIgcJHJbsBkp3iU6IiIiR6GITbCpmak0q90MTjsNbrmlqsMRERE5SETuInbO8dIFL9GlWRc4Ix7atKnqkERERA6i62BFRERCq/jrYM1sgpntMLMVQdOamNmnZvajf9+4vCs+EruzdvPlxi9Jy9wDlfgHQUREpKzKcgx2InBOkWljgVnOuU7ALP95pVmweQEDJw5k+RtPQYMGsHZtZa5eREQkpJAJ1jn3BbCryOThwCT/8SRgRMWGVbr9I+m0OxGuvx5atarM1YuIiIR0uCc5tXTObQVwzm01sxYVGFNI+8eCPXkoDLmkMlctIiJSJmG/TMfMRpvZYjNbnJKSUiHLTM1MJdqiaZgfrWOwIiISdsG5zL+NDjXP4SbY7WaW4K80AdhRUkHn3HjnXB/nXJ/mzSumY4iUzBSa1WlGVPdEuPHGClmmiIhISYJzmX8bH2qew91F/B4wEnjMv3/3MJdzWG7rdxsXnXARxK6Gjh0rc9UiIiJlEvI6WDObAgwGmgHbgb8AM4A3gdbAJuBS51zRE6EOoetgRUQkQpX7OtiQLVjn3JUlvDS0vCurKB/9+BHHRzchsUlXaNSoqsIQEREpUUT2RTxyxkien/5HaNwYNm+u6nBEREQOEXF9ERcECtiZuZNmXU6Af10ICQlVHZKIiMghIi7B7s7ejcN5nUxccXtVhyMiIlKsiNtFvL+Tib35kJZWxdGIiIgUL+IS7P5uEv/wAPzpT1UcjYiISPEibhdx0jFJfDXqS7p1XQftu1V1OCIiIsWKuARbP64+p7Y5HdqcXtWhiIiIlCjidhEv3LyQ/855CrdsGRQUVHU4IiIixYq4BPvGije4Ze4fsZ49ISurqsMREREpVsQl2NTMVJrXawnTp0O9elUdjoiISLEiLsGmZKbQrMExcPHFVR2KiIhIiSIuwaZmptIsC/j556oORUREpEQRl2BTMlJoPmcxjA85FJ+IiEiVibjLdL64djZRvVbA8SdWdSgiIiIlirgE27pJOxjUrqrDEBERKVVE7SLelbWLx966k1VvvQj5+VUdjoiISIkiKsFu3LOR+1Y8yaoHbwUr9+DyIiIilSaiEmxKpjeSTrNxL0B0dBVHIyIiUrKISrD7R9LpdVoVRyIiIlK6iEqw+8eC3bSziiMREREpXUQl2NRdPxMVgMafz6/qUEREREplzrlKW1mfPn3c4sWLD3v+3Pwcdm1YyTGNj4emTSswMhERkVKV+8zaiLoOtlZMHMd0TKrqMEREREKKqF3ET752K5OeHAWV2OoWERE5HBGVYF9a9TrvfTdF18CKiMhR74gSrJmdY2arzWytmY2tqKBKktooluYXXBHu1YiIiByxw06wZhYNPAecC3QDrjSzbhUVWFEBF2Bn5k6aNT0+XKsQERGpMEfSgu0HrHXOrXfO5QJvAMMrJqxD7UlPpcAV0Dw1K1yrEBERqTBHkmCPBYJHPf/FnxYWuzatJioAzbbvC9cqREREKsyRXKZT3JlGh5zea2ajgdH+03QzW30E6+RqXm529e0vpx7JMmS/ZoDqsmKoLiuO6rLiqC4rzmZga9Dz8c658aXNcCQJ9hcg+IDoccCWooX8AEoNojzMbLFzrk9FLa8mU11WHNVlxVFdVhzVZcU5nLo8kl3Ei4BOZtbOzGoBVwDvHcHyREREqo3DbsE65/LN7FbgEyAamOCc+77CIhMREYlgR9RVonPuI+CjCoqlrCpsd7OoLiuQ6rLiqC4rjuqy4pS7Liu1s38REZGaIqK6ShQREYkUEZNgK7tbxurEzCaY2Q4zWxE0rYmZfWpmP/r3jasyxkhhZseb2WwzW2lm35vZ7/3pqs9yMrN4M1toZkv9uvyrP111eZjMLNrMlpjZB/5z1eVhMLMNZrbczJLNbLE/rdx1GREJtrK7ZayGJgLnFJk2FpjlnOsEzPKfS2j5wB+ccycAJwO3+J9F1Wf55QBnOOd6AknAOWZ2MqrLI/F7YGXQc9Xl4RvinEsKujSn3HUZEQmWSu6Wsbpxzn0B7CoyeTgwyX88CRhRmTFFKufcVufcd/7jNLwfs2NRfZab86T7T2P9m0N1eVjM7DjgfODloMmqy4pT7rqMlARbqd0y1hAtnXNbwUsaQIsqjifimFlboBewANXnYfF3aSYDO4BPnXOqy8P3JPBHIBA0TXV5eBww08y+9XsjhMOoyyO6TKcSlalbRpHKYmb1gLeAO5xz+0xjFB8W51wBkGRmjYB3zKx7FYcUkcxsGLDDOfetmQ2u4nCqg9Occ1vMrAXwqZmtOpyFREoLtkzdMkq5bDezBAD/fkcVxxMxzCwWL7lOds697U9WfR4B59weYA7euQKqy/I7DbjQzDbgHUI7w8xeQ3V5WJxzW/z7HcA7eIcpy12XkZJg1S1jxXsPGOk/Hgm8W4WxRAzzmqr/AVY658YFvaT6LCcza+63XDGz2sCZwCpUl+XmnLvPOXecc64t3u/j5865q1FdlpuZ1TWz+oWPgbOAFRxGXUZMRxNmdh7eMYbCbhkfqdqIIoeZTQEG442ssR34CzADeBNoDWwCLnXOFT0RSoows9OBL4HlHDjW9Se847Cqz3Iwsx54J4tE4/3Zf9M595CZNUV1edj8XcR3O+eGqS7Lz8za47VawTuM+rpz7pHDqcuISbAiIiKRJFJ2EYuIiEQUJVgREZEwUIIVEREJAyVYERGRMFCCFRERCYOjPsGa2YNmttkf1SDZzB4zszFmdm0lx/Eff9SPZWY23e/Jp2iZJDP72h8ZZJmZXV6G5QZv349m9nbwQAZm9nJVDWxgZm2DR+Ap8tocM+tT3GsllH/IzM6suOiOLmaWHrpUZDCzUWbWKuh52D6D/mfsN4cxXyMzuznoeSszm15BMcWZ2Wf+dzLkd/hoUtb69EeLaVaGcr389/+6oN/g3KCRZh4rYb5ivw9mNtHMLgm9JfvLjwla17xQn0Mze88OHjXsVjO7rqzrq3DOuaP6BjyId01XuJYfXcZyDYIejwPGFlOmM9DJf9wK2Ao0Ks/2AZcD24DmR0HdtwVWlPDaHKBPVcd4tNyA9KqOoQK3pdLeW7zrsz8o5zzRpX02KyCmk4G5Ja37KHh/Yo60PoENQLMylJsG9CzvvCV9H/BG9rqkHNsa/Lt7IfBxKWUvAl4P/lwAdYAlVfVeHfUt2OL4rb67/cd9/dbi12b2r8J/L/6/8GeD5vmgsI9OM0v3W1QLgFPM7GrzxqVMNrMXzRse7yDOuX3+vAbUppi+kJ1za5xzP/qPt+B1pdW8PNvmnJsKzAR+469vjpn1Ma9T9IlmtsL/R3dn0OtPmtl8/7V+/vQmZjbDr5tv/Iv6MbNBQf9El5hZfTOrZ2azzOw7f9nBIxXFmNmkoJZ7nWLej7P8+v/OzKaV0Lrf/8/V//f816D1dfWn1zOzV/xpy8zsYn/6lf60FWb2j6BlppvZP8zrkPszM+vn18d6M7vQLxPtfy4W+cv8XXH17tfVt+btfRhdZB2PmLf34hsza+lPb+dv8yIze7ik99PMrvXXu9TMXvWntfHre5l/3zqojp7238v1QfWVYGZf+O/ZCjMbUBhb0HouMbOJQct53rxxa9f77/kE88awnVhk2/7tvw+zzOtZ6RKgDzDZX19tC9pbEeK9OKSeitTFIZ894DFggD/tTvNaYF/6MX1nZqf68w72t+d1vE4+HgM6+PP9y4L2tpj33X/bzD42b6/QP4Ni+K2ZrfG36SUL+o3wX28BvIbXP3KymXXwP69/NrN5wKVH8nkssq6S3tdz/G1famaz/GkPmtl4M5sJ/LekeiqmPqPN7HE78J26LSiE26zId7BIfPWBHs65pUVfCypT7PfGf+2gz1Yx8/Y2s7n+/J+Y3w1hsMLfXV9dSuiD3rzfnLuAvxWZPxPYYP7vYqWryn9iZfwH8yCwGUj2b2cT1OrD68LqVP/xY/j/XoBRwLNBy/kAGOw/dsBl/uMTgPeBWP/5/wOuLSGWV/B6QpoN1AkRdz+8ocyiyrB9dxeZdgfwvP94Dt4PXm+80UYKyzQKev0l//HAoO1/BviL//gMINl//D5eR9YA9fB6KonB/6eI19vTWrwBFtr6dVVYfkJQvRfG1Qz4AqjrT78X+HMx2zkR/58r3j/g2/zHNwMv+4//ATwZNE9jvD0Bm/D+qMQAnwMjgt7Hc/3H7+D9MYkFegZt72jg//zHccBioF0x8TXx72vjfaaaBq3jAv/xP4OW9V7h5wS4hWL+sQMnAqvx/+0HreN9YKT/+HpgRlAdTcM7dNMNb4hGgD8A9/uPo4H6/uP0oHVdAkwMWs4b/ns4HNgHJPrL/RZICtq2q/zHf8b/vlCkBRv0Xod6Lw6ppyL1UdxnbzBBLS68Fke8/7gTsNh/PBjIKHzvKNKCDX6O991fDzQE4oGNeH2Zt8L77DXB+5x8SdBvRNCyisa0Afij//iIPo9F1nPI++ov9+eg7Sz8zDzov3e1y1BPwbHfhNdvdkyR5W2gmO9gkfiGAG8VM30Dh36mi/veFPfZmoj3WY0F5uPvqcPbczehhN/IW4B1fr10KqHME8Cvi34u/NfuxxvDudLzV6S0YJ9w3sC3Sc65TwonmtePaX3n3Hx/0utlXF4B3ocOYChe8lpk3rBZQ4H2xc3knLsO7wu2Eu8DUSz/n9irwHXOuUBJ5UpR3NAs64H2ZvaMmZ2D96NZaIof3xdAA79eTvdjwDn3OdDUzBoCXwHjzOx2vCSd76/v72a2DPgMbyjAwhbIz865r/zHr/nLDXYyXjL4yq+/kUCbMmxjYSf53+J9KcDri/a5wgLOud1AX2COcy7Fj3Uy3h8JgFzgY//xcrzdenn+48JlngVc68e2AGiK94NU1O1mthT4Bu/HuLBMLt6fs6KxnoZf7/j1XIwzgOnOuVR/ewq7VTuFA5/VVzm4Tmc45wLOuR848B4sAq4zsweBROeNQxvK+877dVkObHfOLfc/i98HbUMAmOo/Lu69LSrUe1FcPQUr7rNXVCzwkpktx/uzEXzMbaFz7qcQMRaa5Zzb65zLBn7A+0z2w/uM7PI/J9PKuCw4UE9H+nkMVtz7ejLwReF2uoO74nvPOZflPy6tnoKdCbxQWNdFllfcdzBYApBSwnILlfS9CfXZ6gJ0xxupJhn4P7xBXA7hnHvOOdcB78/7/xV93cySgI7OuXdKiHEH3u92pYuU4epKUtoYYfkcfBJXfNDjbOcNk1W4jEnOufvKskLnXIGZTQXuwWvRHhyQWQPgQ7x/8N+UZZnF6IXX0gpe724z64nXgr8FuAyv9QOH7jZxlDDEn3PuMTP7EDgP+Ma8E49Oxvvn3Ns5l2feiBzxQcsquuxghteyvrKsG+fL8e8LOPA5tBKWX5I8P4mA94XOAXDOBcwseJm3Bf8xK8q8QwdnAqc45zLNbA4Htj94HcGxUkyshyy6DGWKLicn6LGB98fJzAbiDab9qpn9yzn33yLzBX++g5cT4OBlBij5e1+W7SlJafXkLbz4z15Rd+LtJeqJ9/3NDnotI0R8wYK3uTCeIxlTsHDdR/p53K+49xXYQ8nvQ/D2l1ZPwUr7DBb3HQyWxaGfqwMLLv17U1Rx3+vvnXOnlLT8YrwBPF/M9FOA3v7vVgzQwszmOOcG+6/H421LpYuUFmyx/BZOmpmd7E+6IujlDXjHUaLM7Hi8f6/FmQVc4h97KTx2eVALzDwdCx8DF+CN+kGRcrXwdg391zlXnn/Hwcu4GK/VNaXI9GZ4u5vfAh4ATgp6+XK/zOnAXufcXrzdtlf50wcDqc4bt7SD35r5B14S74q3K22Hn1yHcHALtLWZFX4JrgTmFQn5G+C0oPqpY2adD2fb8Xap3Rq0zY3xWp2DzKyZecfGrwTmlmOZnwA3mTfEHGbW2bwRMoI1BHb7PxJd8f5whPIVBz5vV5VQZhZwmXmdhGNmTfzp84vMW7ROD+J/Hnc4517CG8mn8L3fbmYnmFkU3u6x8orC210H3jH/wjjS8HZXFnVE70UJn72i62oIbPVb29fg7TotTkkxlmahH39jP+FdXM754cg/j/uV8L5+7S+/nV+mSQmzl1RPRetlJjCmMMGXsrzirAQ6lvJ6ad+bkj5bhVYDzQt/W8ws1sxOLLoCMwve23Q+8GPRMs65551zrZw3ktDpwJqg5AreyafFXg0RbpHeggX4Ld6ukgy8Y0V7/elfAT/h7Z5ZAXxX3MzOuR/M7P/wRq+PAvLwWogbg4oZMMlvnRqwFO/YBuad/DHGOXcDXqtyIN7u2FH+vKOcc8khtuFOM7sa7yD+CuAM51zRXTPHAq/4MQIEt7h3m9l8oAEHWrUP+uWXAZkcGGbpDj+JFuDtOvsf3hfyfTNbjHecO/jPw0pgpJm9iPfhPugfpHMuxd/WKWYW50/+P2BNiG0uzt+A58w7WaUA+Ktz7m0zuw/vuLcBHznnyjPk1st4u7++8/8cpQAjipT5GO9HaBneF78sex5+D7xuZr/nwOGGgzjnvjezR4C5ZlYALME7Png7MMHM7vHjCXUZwWDgHjPLA9KBwkvUxuLtlv0Z73NzyMllIWQAJ5rZt3jfm8LDHhOBF8wsC691ULg9W4/wvSjusxcA8v3djBPxzoF4y8wu9ddTbKvVObfTzL7yPyv/I+jQQkmcc5vN7O94SXKLH8Pe0uc6ZBlHWgfBBlPkffW/T6OBt/3v+g7gV8XMW1I9LePg+nwGL8Es89fzEvDsoYs7lHNulZk1NLP6JRyWKO17U9Jnq3DZueadUPe0eYeuYvBGS/u+yDpu9fd05AG7OfA7hpklO+eSyrAppwF/LUO5Chfxo+mYWT3nXLr/eCyQ4Jz7fRWHVWn83TJ3O+cWhyorEszM0p1z5U3KEa3w98Jv0b2Dd2JNScfuajzzrlZIc869XNWxHA4z6wXc5Zy7pirWH9G7iH3nm3+aOzCAIqdpi4gEedC8k2pW4O3hmlGl0Rz9nufg49mRphneIbUqEfEtWBERkaNRdWjBioiIHHWUYEVERMJACVZERCQMlGBFRETCQAlWREQkDJRgRUREwuD/A/uOAwyrm929AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "caption = '''\n", " Figure 3.2 Disposable income and consumption starting from scratch (Table 3.4)'''\n", "cdata = [s['Cd'] for s in steady_state.solutions]\n", "yddata = [s['YD'] 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(cdata, linestyle=':', color='r') # plot C\n", "axes.plot(yddata, linestyle='--', color='g') # plot YD\n", "plt.axhline(y=80, color='k')\n", "\n", "# add labels\n", "plt.text(2, 72, 'Disposable')\n", "plt.text(2, 68, 'Income YD')\n", "plt.text(10, 60, 'Consumption C')\n", "fig.text(0.1, 0, caption);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Figure 3.3" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "caption = '''\n", " Figure 3.3 Wealth change and wealth level starting from scratch (Table 3.4)'''\n", "\n", "hdata = [s['Hh'] for s in steady_state.solutions]\n", "deltahdata = [s['Hh'] for s in steady_state.solutions]\n", "for i in range(1, len(steady_state.solutions)):\n", " deltahdata[i] -= hdata[i-1]\n", "\n", "fig = plt.figure()\n", "axes = fig.add_axes([0.1, 0.1, 1.0, 1.0])\n", "axes.tick_params(top='off')\n", "axes.set_ylim(0, 13)\n", "axes.set_xlim(-2, 50)\n", "axes.plot(deltahdata, linestyle='--', color='g')\n", "\n", "axes2 = axes.twinx()\n", "axes2.set_ylim(0, 85)\n", "axes2.set_xlim(-2, 50)\n", "axes2.plot(hdata, 'r')\n", "\n", "# add labels\n", "plt.text(20, 16, 'Household saving')\n", "plt.text(20, 12, '(the change in the money stock)')\n", "plt.text(20, 70, 'Wealth level H (money stock)')\n", "\n", "fig.text(0.1, -0.05, caption);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Figure 3.4" ] }, { "cell_type": "code", "execution_count": 25, "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 3.4 Evolution of wealth, target wealth, consumption and disposable income\n", " following an increase in government expenditures ($\\\\bigtriangleup$G = 5) Model SIM '''\n", "hdata = [s['Hh'] for s in step_model.solutions]\n", "cdata = [s['Cs'] for s in step_model.solutions]\n", "vtdata = [s['YD']*(1.-s['alpha1'])/s['alpha2'] for s in step_model.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(78, 102)\n", "axes.set_xlim(-2, 50)\n", "axes.plot(hdata, linestyle='-', color='g', label='Wealth')\n", "axes.plot(cdata, linestyle=':', color='r', linewidth=2, label='Consumption')\n", "axes.plot(vtdata, linestyle='--', color='b', label='Target wealth (and disposable income)')\n", "\n", "plt.legend(loc=(0.35,0.2), frameon=False)\n", "\n", "fig.text(0.1, -0.05, caption);\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scenario: Model SIM, increase propensity to consume" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "alpha_model = create_sim_model()\n", "alpha_model.set_values({'alpha1': 0.6,\n", " 'alpha2': 0.4,\n", " 'theta': 0.2,\n", " 'Gd': 20,\n", " 'W': 1})\n", "\n", "# Use the steady state solution as a starting point\n", "alpha_model.solutions = steady_state.solutions[-2:]\n", "for i in range(50):\n", " alpha_model.solve(iterations=100, threshold=1e-4)\n", " if i == 2:\n", " alpha_model.parameters['alpha1'].value = 0.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Figure 3.8" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will need to generate solutions that involve a change in alpha1 (from 0.6 to 0.7)." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "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 3.8 Evolution of consumption, disposable income and wealth following\n", " an increase in the propensity to consume out of current income \n", " ($\\\\alpha_1$ moves from 0.6 to 0.7)'''\n", "hdata = [s['Hh'] for s in alpha_model.solutions]\n", "cdata = [s['Cs'] for s in alpha_model.solutions]\n", "vtdata = [s['YD'] for s in alpha_model.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(58, 100)\n", "axes.set_xlim(-2, 50)\n", "axes.plot(hdata, linestyle='-', color='g')\n", "axes.plot(cdata, linestyle=':', color='r', linewidth=2)\n", "axes.plot(vtdata, linestyle='--', color='b')\n", "\n", "plt.text(6, 97, 'Consumption')\n", "plt.text(8, 79, 'Disposable income')\n", "plt.text(20, 62, 'Wealth')\n", "\n", "fig.text(0.1, -0.1, 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 }