{ "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": [ "# IS-LM MODEL\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This note illustrates how to code the IS-LM Model in Python. The purpose of the note is to walk through Python applications, not to offer a detailed discussion of the IS-LM Model or to show best coding practices. The note also assumes familiarity with the IS-LM 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 IS-LM model](#1.-THE-IS-LM-MODEL)\n", "2. [The IS schedule (Investment-Saving)](#2.-THE-IS-SCHEDULE-(INVESTMENT-SAVING))\n", "3. [The LM schedule (Liquidity Preference-Money Supply)](#3.-THE-LM-SCHEDULE-(LIQUIDITY-PREFERENCE-MONEY-SUPPLY))\n", "4. [Equilibrium](#4.-EQUILIBRIUM)\n", "5. [Dynamics](#5.-DYNAMICS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. THE IS-LM MODEL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IS-LM, or Hicks-Hansen, model shows the combinations of interest rate (vertical axis) and income (horizontal axis) for which the goods market (IS) and the loan market (LM) are in equilibrium. There is one particular combination of interest rate and income that is consistent with equilibrium in both markets simultaneosuly. \n", "\n", "In the graphic version of the model, all the points in the **IS shedule** represent equilibrium in the goods makret. Similarly, all the points in the **LM schedule** represents equlibrium in the loan market.\n", "\n", "This model treats the price level as exogenous (given and fixed). In this sense, the IS-LM model is applicable in the context of idle resources or when changes in income has no effect on the price level." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. THE IS SCHEDULE (INVESTMENT-SAVING)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The IS schedule is derived from the equilbrium condition where output $(Y)$ equals spending:\n", "\n", "\$$\n", "Y = C + I + G + (X - Z)\n", "\$$\n", "\n", "where $C$ is household consumption, $I$ is private investment, $G$ is the level of government spending, $X$ is exports, and $Z$ is imports. We can treat variables $G = \\bar{G}$ and $X = \\bar{X}$ as exogenous. The former is defined by policy makers, the latter is given by economic conditions in the rest of the world. A more detailed exposition, such as the Mundell-Fleming model, would also take into consideration the exchange rate, and exprots will also be dependent (to some extent) of domestic economic policy.\n", "\n", "Household's consumption follow a keynesian consumption function:\n", "\n", "\$$\n", " C = a + b(Y - T)\n", "\$$\n", "\n", "where $a \\geq 0$ is the level of autonomous consumption (indepenent of the level of income), $b \\in (0, 1)$ is the marginal propensity to consume, and $T$ is the dollar-amount of taxes.\n", "\n", "Assume now a simple linear relationship between investment and the interst rate $i$:\n", "\n", "\$$\n", " I = \\bar{I} - d \\cdot i\n", "\$$\n", "\n", "where $\\bar{I}$ represents the level of investment when $i = 0$ and $d$ is the rate at which $I$ falls when $i$ increases.\n", "\n", "We can also assume that imports follow a similar functional form than household's domestic consumption:\n", "\n", "\$$\n", " Z = \\alpha + \\beta (Y - T)\n", "\$$\n", "\n", "where $\\alpha$ is the autonomous level of imports and $\\beta$ is the marginal propensity to import.\n", "\n", "To derive the IS schedule we need to use the consumption, investment, and import functions and solve for $i$ from the equilibrium condition:\n", "\n", "\\begin{align}\n", " Y &= C + I + G + (X - Z) \\\\\n", " Y &= \\underbrace{\\left[a + b(Y-T) \\right]}_{C} + \\underbrace{\\left[\\bar{I} - d \\cdot i \\right]}_{I} + \\bar{G} + \\left[X - \\underbrace{\\left(\\alpha + \\beta(Y-T) \\right)}_{Z} \\right] \\\\\n", " i_{IS} &= \\underbrace{\\frac{(a-\\alpha)-(b-\\beta)T + \\bar{I} + \\bar{G} + \\bar{X}}{d}}_\\text{intercept} - \\underbrace{\\frac{1-b+\\beta}{d} }_\\text{slope} \\cdot Y\n", "\\end{align}\n", "\n", "Note that the larger $\\alpha$ and $\\beta$, the lower the intercept. This means that at the same level of $i$ income will be lower. Note also that the intercept and the slope of the IS schedule is sensitive to the value of $d$. The more (less) sensitive investment is to $d$, the more (less) horizontal the IS schedule looks. Finally, note that the IS schedule is a straight line.\n", "\n", "The following code plots the IS scheduel using the above information. The first part of the code imports the required Python packages. The second part of the code defines the parameters and arrays. The third part of the code defines and populates the IS schedule. The fourt part of the code plots the IS schedule." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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