{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### [**NICOLAS CACHANOSKY**](http://www.ncachanosky.com) | Department of Economics | Metropolitan State University of Denver | ncachano@msudenver.edu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AD-AS MODEL\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This note illustrates how to code a simple AD-S Model in Python. The purpose of the note is to walk through Python applications, not to offer a detailed discussion of the AD-AS Model or to show best coding practices. The note also assumes familiarity with the AD-AS model and a beginner experience with Python.\n", "\n", "For a more complete and detailed discussion of Python applications see the material in [Quant Econ](https://quantecon.org/).\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TABLE OF CONTENTS\n", "1. [The AD-AS model](#1.-THE-AD-AS-MODEL)\n", "2. [Aggregate Demand (AD)](#2.-AGGREGATE-DEMAND-(AD))\n", "3. [Long-Run Aggregate Supply (LRAS)](#3.-LONG-RUN-AGGREGATE-SUPPLY-(LRAS))\n", "4. [Short-Run Aggregate Supply (SRAS)](#4.-SHORT-RUN-AGGREGATE-SUPPLY-(SRAS))\n", "5. [Putting all the pieces together](#5.-PUTTING-ALL-THE-PIECES-TOGETHER)\n", "6. [Shocks](#6.-SHOCKS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. THE AD-AS MODEL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The AD-AS model shows the relationship between output $(Y)$ and the price level $(P)$. Different to the IS-LM model, in this case $P$ is endogenous and varies with different levels of $Y$. The AD-AS model has three components:\n", "\n", "1. AD: Aggregate demand\n", "2. LRAS: Long-run aggregate supply\n", "3. SRAR: Short-run aggregate supply\n", "\n", "The AD-AS model is more *general* than the IS-LM model in the sense that it allows for the price level to change. Therea are two differences between these models. The first one is taht while the AD-AS model allows for the interest rate $(i)$ to change, it does not show up explicitly in the model as it does in the IS-LM framework. The second one, is that both, monetary and fiscal policy affect the same line (the $AD$), while in the IS-LM framework monetary and fiscal policy shift *different* lines." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. AGGREGATE DEMAND (AD)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The $AD$ line tracks all the output and price level combinations for whith the IS-LM model is in equilibrium. Graphically, it shows how equilibrum moves in the IS-LM graph when $P$ changes. A change in $P$ shifts the LM schedule giving a new equilibrium point on the IS schedule. In simple terms: $AD = Y = C + I + G + (X - Z)$ where $C$ is the household consumption, $I$ is investment, $G$ is govegnment spending, $X$ is exports, and $Z$ is imports.\n", "\n", "In the [IS-LM model note](https://nbviewer.jupyter.org/github/ncachanosky/Macroeconomics-with-Python/blob/master/IS-LM%20Model.ipynb) there is consumption function, an investment function, and an imports function. The remainig variables $(G = \\bar{G} \\text{ and } X = \\bar{X})$ are treated as exogenous. The consumption, investment, imports, and money demadn functions are:\n", "\n", "\\begin{align}\n", " C &= a + b(Y-T) \\\\[10pt]\n", " I &= \\bar{I} - d \\cdot i \\\\[10pt]\n", " Z & = \\alpha + \\beta(Y-T) \\\\[10pt]\n", " M^d &= c_1 + c_2 Y - c_3 i\n", "\\end{align}\n", "\n", "Where $a>0$ and $b \\in (0, 1)$ are the household level of autonomous consumption and the marginal propensity to consume respectively with $T$ representing the nominal value of taxes; \\bar{I} is the level of investment when $i = 0$ and $d >0$ is the slope of investment with respect to $i$; $\\alpha >0$ and $\\beta \\in (0, 1)$ are the autonomous level and the marginal propensity to import respectively; and $c_1>0, c_2>0, c_3>0$ capture the keynesian precautionary, transation, and especualtion reasons to demand money.\n", "\n", "The $AD$ is the equilibrium level of output $(Y^*)$ from the IS-LM model, which is a function of $(P)$. From the [IS-LM model note](https://nbviewer.jupyter.org/github/ncachanosky/Macroeconomics-with-Python/blob/master/IS-LM%20Model.ipynb) (section 4):\n", "\n", "\\begin{align}\n", " Y^* &= \\frac{\\left[(a-\\alpha)-(b-\\beta)T+\\bar{I}+\\bar{G}+X\\right]/d + (1/c_3) \\left(M^S_0/P - c_1 \\right)}{(1-b+\\beta)/d - (c_2/c_3)} \\\\[10pt]\n", " Y^* &= \\underbrace{\\left[\\frac{(a-\\alpha)-(b-\\beta)T+\\bar{I}+\\bar{G} + X}{d} - \\frac{c_1}{c_3} \\right] \\left[\\frac{1-b+\\beta}{d} - \\frac{c_2}{c_3} \\right]^{-1}}_\\text{vertical level} + \\underbrace{\\frac{M^S_0}{c_3} \\left[\\frac{1-b+\\beta}{d} - \\frac{c_2}{c_3} \\right]^{-1}}_\\text{shape} \\cdot \\frac{1}{P}\n", "\\end{align}\n", "\n", "Even though the function looks complicated, note that the relationship between $Y$ and $P$ is hiperbolic. Note that an increaes in $M^S_0$ increases the level of Y but also changes the *shape* of $AD$.\n", "\n", "## 2.1 MONEY SUPPLY AND VELOCITY\n", "\n", "The AD-AS model has a real variable $(Y)$ and a nominal variable $(P)$. Because $PY = NGDP$ the model can be framed in terms of the equation of exchange.\n", "\n", "\\begin{equation}\n", " MV_{Y} = P_{Y}Y\n", "\\end{equation}\n", "\n", "Where $M$ is money supply (shown as $M^S_0$ above), $V_Y$ is the velocity of money circulation, and $P_{Y}$ is the GDP deflator of real output $Y$. Note this simple form, $Y = \\frac{MV_Y}{P_Y}$ also has the hiperbolic shape discussed above.\n", "\n", "To add a layer of complexity, money supply can be open in base money $(B)$ times the money-multiplier $m$:\n", "\n", "\\begin{align}\n", " m &= \\frac{1 + \\lambda}{\\rho + \\lambda} \\\\[10pt]\n", " M &= B \\cdot m \\\\[10pt]\n", " \\left(Bm\\right) V_Y &= P_Y Y\n", "\\end{align}\n", "\n", "where $\\lambda \\in (0, 1)$ is the currency-drain ratio (cash-to-deposit ratio) and $\\rho \\in (0, 1)$ is the reserve ratio (desired plus required level of reserves).\n", "\n", "If money demand $M^D$ is a $k$ proportion of nominal income $(P_YY)$, then, assuming equilibrium in the money market, money velocity is the inverse of money demand: $V_Y = 1/k$. As less (more) money is demanded to be hold as a cash-balance, the more (less) quickly money moves (in average) in the economy.\n", "\n", "\\begin{align}\n", " M^S_0V_Y &= P_YY \\\\[10pt]\n", " M^D &= k \\cdot \\left(P_YY \\right) \\\\[10pt]\n", " M^S_0 &= M^D \\\\[10pt]\n", " V_Y &= \\frac{1}{k}\n", "\\end{align}\n", "\n", "---\n", "\n", "We can code $AD$ with a `class`. A `class` allows to build our own type of objects. In this case the code constructs a class called `AD` that has (1) the money multiplier, (2) the money supply, and (3) estimates $AD$ for a given $P$.\n", "\n", "The code follows the following structure. The first section imports the required packages. The second section builds the $AD$ `class`. The third section show the values of the money multiplier and total money supply. The fourth section plots the $AD$.\n", "\n", "The `class` is build the following way. The first element, `__init__` collects the model (or `class`) parameters. Note that the values of these parameters are defined **inside** the `class` (this does not need to be the case) and that these parameters exist **inside** the class (they are not global values). After the parameters are defined, the `class` continues to build the three components. Note that the first two (money multiplier and money supply) can be defined with the paramters already included in the `class`. The third component, the value of $AD$, requires an exogenous value, $P$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Money multiplier = 7.0\n", "Money supply = 1750.0\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"1|IMPORT PACKAGES\"\n", "import numpy as np # Package for scientific computing with Python\n", "import matplotlib.pyplot as plt # Matplotlib is a 2D plotting library\n", "\n", "\"2|BUILD AD CLASS\"\n", "class class_AD:\n", " \"Define the parameters of the model\"\n", " def __init__(self, a = 20 , # AD: autonomous consumption \n", " b = 0.2 , # AD: marginal propensity to consume\n", " alpha = 5 , # AD: autonomous imports\n", " beta = 0.1 , # AD: marginal propensity to import\n", " T = 1 , # AD: Taxes\n", " I = 10 , # AD: Investment\n", " G = 8 , # AD: Government spending\n", " X = 2 , # AD: Exports\n", " d = 5 , # AD: Investment slope\n", " c1 = 175 , # AD: Precautionary money demand\n", " c2 = 2 , # AD: Transactions money demand\n", " c3 = 50 , # AD: Speculatio money demand\n", " B = 250 , # AD: Base money\n", " lmbda = 0.05 , # AD: Currency drain ratio\n", " rho = 0.10 ): # AD: Reserve requirement)\n", "\n", " \"Assign the parameter values\"\n", " self.a = a\n", " self.b = b\n", " self.alpha = alpha\n", " self.beta = beta\n", " self.T = T\n", " self.I = I\n", " self.G = G\n", " self.X = X\n", " self.d = d\n", " self.c1 = c1\n", " self.c2 = c2\n", " self.c3 = c3\n", " self.B = B\n", " self.lmbda = lmbda\n", " self.rho = rho\n", "\n", " \"Money multiplier\"\n", " def m(self):\n", " #Unpack the parameters (simplify notation)\n", " lmbda = self.lmbda\n", " rho = self.rho\n", " #Calculate m\n", " return ((1 + lmbda)/(rho + lmbda))\n", " \n", " \"Money supply\" \n", " def M(self):\n", " #Unpack the parameters (simplify notation)\n", " B = self.B\n", " #Calculate M\n", " return (B*self.m())\n", " \n", " \"AD: Aggregate demand\"\n", " def AD(self, P):\n", " #Unpack the parameters (simplify notation)\n", " a = self.a\n", " alpha = self.alpha\n", " b = self.b\n", " beta = self.beta\n", " T = self.T\n", " I = self.I\n", " G = self.G\n", " X = self.X\n", " d = self.d\n", " c1 = self.c1\n", " c2 = self.c2\n", " c3 = self.c3\n", " #Calculate AD\n", " AD_level1 = (((a-alpha)-(b-beta)*T+I+G+X)/d-c1/c3)\n", " AD_level2 = ((1-b+beta)/d-c2/c3)**(-1)\n", " AD_shape = self.M()/c3 * ((1-b+beta)/d - c2/c3)**(-1)\n", " return (AD_level1 * AD_level2 + AD_shape/P)\n", "\n", "\"3|SHOW RESULTS\"\n", "out = class_AD()\n", "\n", "print(\"Money multiplier =\", round(out.m(), 2))\n", "print(\"Money supply =\" , round(out.M(), 2))\n", "\n", "size = 50\n", "P = np.arange(1, size)\n", "Y = out.AD(P)\n", "\n", "\"4|PLOT AD\"\n", "y_max = np.max(P)\n", "x_max = np.max(Y)\n", "v = [0, x_max, 0, y_max]\n", "fig, ax = plt.subplots(figsize=(10, 8))\n", "ax.set(title=\"AGREGGATE DEMAND\", xlabel=\"Y\", ylabel=\"P\")\n", "ax.plot(Y, P, \"k-\")\n", "ax.yaxis.set_major_locator(plt.NullLocator()) # Hide ticks\n", "ax.xaxis.set_major_locator(plt.NullLocator()) # Hide ticks\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. LONG-RUN AGGREGATE SUPPLY (LRAS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prices are flexible in the long-run, therefore money is neutral and the $LRAS$ is a vertical line (a given value of $Y$ rather than a function). The valor of $Y$ in the long-run can be derived from the Solow model's steady-state. From the [Solow model notes](https://nbviewer.jupyter.org/github/ncachanosky/Macroeconomics-with-Python/blob/master/Solow%20Model.ipynb) (section 2) we know that there is an equilibrium level of capital $K^*$. Therefore, assuming a typical Cobb-Douglass production function with constant returns to scale, for any period of time $t$, output in the long run $(Y_{LR})$ equals:\n", "\n", "\\begin{align}\n", " K_{LR} &= N \\cdot \\left(\\frac{sA}{\\delta} \\right)^{\\frac{1}{1-\\theta}} \\\\\n", " Y_{LR} &= A \\cdot \\left(K_{LR}^{\\theta} N^{1-\\theta} \\right)\n", "\\end{align}\n", "\n", "Where $A$ is techonology or total factor productivity (TFP), $N$ is labor, and $\\theta$ is the output elasticity of capital. In the long-run, the leve of output will grow at the rate of growth of population $(n)$ and technology $(\\gamma)$. This is shown as a right-ward movemet in the common pictorial depiction of the AD-AS model. Also note that since th $LRAS$ is a value (a vertical line), it can only shift **left** or **right**, but not up or down." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. SHORT-RUN AGGREGATE SUPPLY (SRAS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As long as wages are sticky, then changes in $P$ do have short-run effects on the level of $Y$ thourgh changes in the real wage $(W/P)$. In the long-run $N$ is at full employment and the representative firm can change the size of $K$. But in the short-run, the firm can change $N$ but $K$ is fixed. Therefore, to construct the $SRAS$ we need the bheavior of labor supply in the short-run. For simplicity, this note takes a simple approach. It assumes a closed economy (you can try to add a foreign sector yourself) and lets the level of employment be defined by the following function with respect to the price level (you cantry to replace with a labor market as discussed in the [labor market notes](https://nbviewer.jupyter.org/github/ncachanosky/Macroeconomics-with-Python/blob/master/Labor%20Market.ipynb)):\n", "\n", "\\begin{equation}\n", " N = \\eta \\cdot P^{\\nu}; \\eta, \\nu > 0\n", "\\end{equation}\n", "\n", "The function is (implicitly) assuming constant expectations by labor. Therefore, an increase in the price level reduces the real wage paid by the firms, but labor does not realize that the real wage has decreased. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. PUTTING ALL THE PIECES TOGETHER" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now put all the pieces together. In this case, instead of using a `class` the sample code uses `functions`. The model assumes a given stock of capital $K = \\bar{K}$ (you can try to add a *steady-state* level of capital as discussed in the [Solow Model notes](https://nbviewer.jupyter.org/github/ncachanosky/Macroeconomics-with-Python/blob/master/Solow%20Model.ipynb). The code uses the `root` function to find the price level of equilibrium (section 4)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"1|IMPORT PACKAGES\"\n", "import numpy as np # Package for scientific computing with Python\n", "import matplotlib.pyplot as plt # Matplotlib is a 2D plotting library\n", "from scipy.optimize import root # Package to find the roots of a function\n", "\n", "\"2|DEFINE PARAMETERS\"\n", "# PRODUCTION FUNCTION\n", "A = 1 # Total Factor Productivity\n", "varphi = 0.70 # Output elasticity of capital\n", "# CAPITAL STOCK\n", "s = 0.25 # Savings rate\n", "delta = 0.20 # Depreciation rate\n", "K = 100 # Capital stock\n", "# LABOR SUPPLY\n", "v = 0.75 # Labor supply \"slope\" with respect to P\n", "# MONEY SUPPLY\n", "B = 100 # Base Money\n", "lmbda = 0.05 # Currency drain\n", "rho = 0.20 # Reserve ratio\n", "# AGGREGATE DEMAND\n", "a = 40 # Autonomous household domestic consumption\n", "b = 0.3 # Marginal propensity to consume\n", "T = 1 # Taxes\n", "I = 4 # Investment with i = 0\n", "G = 2 # Government Spengin\n", "d = 2 # Slope of investment with respect to i\n", "c1 = 200 # Money demand: Precuationary\n", "c2 = 0.6 # Money demand: Transactions\n", "c3 = 10 # Money demand: Speculation\n", "\n", "\"3|FUNCTIONS\"\n", "# LABOR SUPPLY\n", "def N(P):\n", " N = v * P\n", " return N\n", "\n", "# OUTPUT\n", "def output(P):\n", " output = A * (K**(varphi)) * (N(P)**(1-varphi))\n", " return output\n", "\n", "# MONEY SUPPLY\n", "m = (1+lmbda)/(rho+lmbda) # Money Multiplier\n", "M = B*m # Money Supply\n", "\n", "# AGGREGATE DEMAND\n", "def AD(P):\n", " AD_level1 = (a-b*T + I + G)/d - (c1/c3)\n", " AD_level2 = ((1-b)/d - (c2/c3))**(-1)\n", " AD_shape = M/c3 * AD_level2\n", " AD = AD_level1 * AD_level2 + AD_shape/P\n", " return AD\n", "\n", "\"4|EQUILIBRIUM: PRICE LEVEL\"\n", "def equation(P):\n", " Eq1 = AD(P) - output(N(P))\n", " equation = Eq1\n", " return equation\n", "\n", "sol = root(equation, 10)\n", "Pstar = sol.x\n", "Nstar = N(Pstar)\n", "\n", "# #Define domain of the model\n", "size = np.round(Pstar, 0)*2\n", "P_vector = np.linspace(1, size, 500) # 500 dots between 1 and size\n", "\n", "# Agregate Supply and Aggregate Demand\n", "LRAS = output(N(Pstar))\n", "SRAS = output(N(P_vector))\n", "AD_vector = AD(P_vector)\n", "\n", "\"5|PLOT AD-AS MODEL\"\n", "v = [0, 80, 0, size] # Axis range\n", "fig, ax = plt.subplots(figsize=(10, 8))\n", "ax.set(title=\"AD-AS MODEL\", ylabel=r'$P$', xlabel=\"Output\")\n", "ax.plot(AD_vector, P_vector, \"k-\", alpha = 0.7)\n", "ax.plot(SRAS , P_vector, \"b-\", alpha = 0.7)\n", "plt.axvline(x = LRAS, color = 'r', alpha = 0.7)\n", "plt.axhline(y = Pstar, xmax = LRAS/80, ls=':', color='k')\n", "ax.yaxis.set_major_locator(plt.NullLocator()) # Hide ticks\n", "ax.xaxis.set_major_locator(plt.NullLocator()) # Hide ticks\n", "plt.text(75, 2.5, \"AD\")\n", "plt.text(45, 11.0, \"SRAS\", color='b')\n", "plt.text(30, 11.0, \"LRAS\", color='r')\n", "plt.axis(v)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6. SHOCKS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now add a real and a nominal positive shocks. In the case of the real shock, technology increases by 10-percent. In the case of the nominal shock, base money increases by 10-percent. Even though the output is not shown, the code inclides the inflation rate and the percent change in output calculations for each one of the two shocks. Note that the $SRAS$ shifts horizontally with shocks to the $LRAS$. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\"1|IMPORT PACKAGES\"\n", "import numpy as np # Package for scientific computing with Python\n", "import matplotlib.pyplot as plt # Matplotlib is a 2D plotting library\n", "from scipy.optimize import root # Package to find the roots of a function\n", "\n", "\"2|DEFINE PARAMETERS\"\n", "# PRODUCTION FUNCTION\n", "A = 1 # Total Factor Productivity\n", "varphi = 0.70 # Output elasticity of capital\n", "# CAPITAL STOCK\n", "s = 0.25 # Savings rate\n", "delta = 0.20 # Depreciation rate\n", "K = 100 # Capital stock\n", "# LABOR SUPPLY\n", "v = 0.75 # Labor supply \"slope\" with respect to P\n", "# MONEY SUPPLY\n", "B = 100 # Base Money\n", "lmbda = 0.05 # Currency drain\n", "rho = 0.20 # Reserve ratio\n", "# AGGREGATE DEMAND\n", "a = 40 # Autonomous household domestic consumption\n", "b = 0.3 # Marginal propensity to consume\n", "T = 1 # Taxes\n", "I = 4 # Investment with i = 0\n", "G = 2 # Government Spengin\n", "d = 2 # Slope of investment with respect to i\n", "c1 = 200 # Money demand: Precuationary\n", "c2 = 0.6 # Money demand: Transactions\n", "c3 = 10 # Money demand: Speculation\n", "\n", "\"3|FUNCTIONS\"\n", "# LABOR SUPPLY\n", "def N(P):\n", " N = v * P\n", " return N\n", "\n", "# OUTPUT\n", "def output(P):\n", " output = A * (K**(varphi)) * (N(P)**(1-varphi))\n", " return output\n", "\n", "# MONEY SUPPLY\n", "m = (1+lmbda)/(rho+lmbda) # Money Multiplier\n", "M = B*m # Money Supply\n", "\n", "# AGGREGATE DEMAND\n", "def AD(P):\n", " AD_level1 = (a-b*T + I + G)/d - (c1/c3)\n", " AD_level2 = ((1-b)/d - (c2/c3))**(-1)\n", " AD_shape = M/c3 * AD_level2\n", " AD = AD_level1 * AD_level2 + AD_shape/P\n", " return AD\n", "\n", "\"4|EQUILIBRIUM: PRICE LEVEL\"\n", "def equation(P):\n", " Eq1 = AD(P) - output(N(P))\n", " equation = Eq1\n", " return equation\n", "\n", "sol = root(equation, 10)\n", "Pstar = sol.x\n", "Nstar = N(Pstar)\n", "\n", "# #Define domain of the model\n", "size = np.round(Pstar, 0)*2\n", "P_vector = np.linspace(1, size, 500) # 500 dots between 1 and size\n", "\n", "# Agregate Supply and Aggregate Demand\n", "LRAS = output(N(Pstar))\n", "SRAS = output(N(P_vector))\n", "AD_vector = AD(P_vector)\n", "\n", "\"5|CALCULATE SHOCK EFFECTS\"\n", "real_shock = 1.10\n", "nominal_shock = 1.10\n", "\n", "# Real shock\n", "def output2(P):\n", " A2 = A * real_shock\n", " output2 = A2 * (K**(varphi)) * (N(P)**(1-varphi))\n", " return output2\n", "\n", "def equation_real(P):\n", " Eq1 = AD(P) - output2(N(P))\n", " equation_real = Eq1\n", " return equation_real\n", "\n", "sol_real = root(equation_real, 10)\n", "Pstar2 = sol_real.x\n", "Nstar2 = N(Pstar2)\n", "LRAS2 = output2(N(Pstar2))\n", "SRAS2 = output2(N(P_vector))\n", "\n", "gP2 = Pstar2/Pstar - 1 # Percent change in P\n", "gY2 = LRAS2/LRAS - 1 # Percent change in Y \n", "\n", "# Nominal shock\n", "M3 = (B * nominal_shock) * m\n", "\n", "def AD3(P):\n", " AD_level1 = (a-b*T + I + G)/d - (c1/c3)\n", " AD_level2 = ((1-b)/d - (c2/c3))**(-1)\n", " AD_shape = M3/c3 * AD_level2\n", " AD3 = AD_level1 * AD_level2 + AD_shape/P\n", " return AD3\n", "\n", "def equation_nominal(P):\n", " Eq1 = AD3(P) - output(N(P))\n", " equation_nominal = Eq1\n", " return equation_nominal\n", "\n", "sol_nominal = root(equation_nominal, 10)\n", "Pstar3 = sol_nominal.x\n", "Nstar3 = N(Pstar3)\n", "AD_vector3 = AD3(P_vector)\n", "\n", "gP3 = Pstar3/Pstar - 1 # Percent change in P\n", "gY3 = output(Pstar3)/output(Pstar) - 1 # Percent change in Y \n", "\n", "\"6|PLOT AD-AS MODEL WITH SHOCKS\"\n", "P3_stop = output(N(Pstar3))\n", "\n", "v = [0, 80, 0, size] # Axis range\n", "fig, ax = plt.subplots(nrows=2, figsize=(10, 20))\n", "ax[0].set(title=\"AD-AS MODEL: REAL SHOCK\", ylabel=r'$P$', xlabel=\"Output\")\n", "ax[0].plot(AD_vector , P_vector, \"k-\", alpha = 0.7)\n", "ax[0].plot(SRAS , P_vector, \"b-\", alpha = 0.7)\n", "ax[0].plot(SRAS2 , P_vector, \"b:\", alpha = 0.7)\n", "ax[0].axvline(x = LRAS , ls=\"-\", color='r', alpha = 0.7)\n", "ax[0].axvline(x = LRAS2 , ls=\":\", color=\"r\", alpha = 0.7)\n", "ax[0].axhline(y = Pstar , xmax = LRAS/80 , ls=\":\", color=\"k\", alpha = 0.7)\n", "ax[0].axhline(y = Pstar2, xmax = LRAS2/80, ls=\":\", color=\"k\", alpha = 0.7)\n", "ax[0].text(75.0, 2.5, \"AD\")\n", "ax[0].text(44.5, 11.5, \"SRAS\" , color = \"b\")\n", "ax[0].text(48.0, 11.0 , \"SRAS'\", color = \"b\")\n", "ax[0].text(30.0, 11.0, \"LRAS\" , color = \"r\")\n", "ax[0].text(39.0, 11.0, \"LRAS'\", color = \"r\")\n", "ax[0].yaxis.set_major_locator(plt.NullLocator()) # Hide ticks\n", "ax[0].xaxis.set_major_locator(plt.NullLocator()) # Hide ticks\n", "ax[0].axis(v)\n", "ax[1].set(title=\"AD-AS MODEL: NOMINAL SHOCK\", ylabel=r'$P$', xlabel=\"Output\")\n", "ax[1].plot(AD_vector , P_vector, \"k-\", alpha = 0.7)\n", "ax[1].plot(AD_vector3 , P_vector, \"k:\", alpha = 0.7)\n", "ax[1].plot(SRAS , P_vector, \"b-\", alpha = 0.7)\n", "ax[1].axvline(x = LRAS , ls=\"-\", color=\"r\", alpha = 0.7)\n", "ax[1].axhline(y = Pstar , xmax = LRAS/80, ls=\":\", color=\"k\", alpha = 0.7)\n", "ax[1].axhline(y = Pstar3, xmax = P3_stop/80, ls=\":\", color=\"k\", alpha = 0.7)\n", "ax[1].text(75, 1.7, \"AD\")\n", "ax[1].text(75, 2.7, \"AD'\")\n", "ax[1].text(45, 11.0, \"SRAS\" , color = \"b\")\n", "ax[1].text(30, 11.0, \"LRAS\" , color = \"r\")\n", "ax[1].yaxis.set_major_locator(plt.NullLocator()) # Hide ticks\n", "ax[1].xaxis.set_major_locator(plt.NullLocator()) # Hide ticks\n", "ax[1].axis(v)\n", "plt.show()" ] } ], "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": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }