{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducción a la Programación en MATLAB (C6)\n", "\n", "Mauricio Tejada\n", "\n", "ILADES - Universidad Alberto Hurtado\n", "\n", "Agosto 2017\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contenidos\n", "\n", "- [Aplicaciones I](#6.-Aplicaciones-I)\n", " - [Modelo Insumo-Producto](#6.1-Modelo-Insumo-Producto)\n", " - [Equilibrio de Mercado con Dos Bienes](#6.2-Equilibrio-de-Mercado-con-Dos-Bienes)\n", " - [Mínimos Cuadrados Ordinarios](#6.3-M%C3%ADnimos-Cuadrados-Ordinarios)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Aplicaciones I" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.1 Modelo Insumo-Producto \n", "\n", "*Aplicación tomada del libro \"Matemática para Economistas con Excel y Matlab\" de Alicia Bernardello y coautores*\n", "\n", "Características del modelo:\n", "\n", "- Describe relaciones interindustriales o intersectoriales.\n", "- Usa dichas relaciones para predecir cambios en la demanda para predecir causados por cambios en la demanda autónoma del los productos finales.\n", "\n", "Suponga una economía con tres sectores: \n", "\n", "\n", "| Prod/Ins | I | II | III |\n", "|--------------|--------|-----|------|\n", "| I |0.2 |0.6 |0 |\n", "| II |0.2 |0 |0.2 |\n", "| III |0.4 |0.2 |0.5 |\n", "\n", "- Columna 1: Por cada peso producido del producto I se usan: 0.2 pesos del mismo producto, 0.2 pesos del producto II, y 0.4 pesos del producto III.\n", "- La demanda de cada una de las tres industrias está compuesta de la demanda derivada de las otras industrias y la demanda autónoma:\n", "\n", "\\begin{eqnarray}\n", "x_1 &=& 0.2x_1+0.6x_2+d_1 \\\\ \n", "x_2 &=& 0.2x_1+0.2x_3+d_2 \\\\ \n", "x_3 &=& 0.4x_1+0.2x_2+0.5x_3+d_3 \\\\\n", "\\end{eqnarray}\n", "\n", "- Definamos la matriz de coeficientes técnicos como:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A =\n", "\n", " 0.2000 0.6000 0\n", " 0.2000 0 0.2000\n", " 0.4000 0.2000 0.5000\n" ] } ], "source": [ "clear all;\n", "A = [0.2 0.6 0; 0.2 0 0.2; 0.4 0.2 0.5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Si definimos los vectores $x=(x_1,x_2,x_3)'$ y $d=(d_1,d_2,d_3)'$ tenemos:\n", "\n", "$$x = Ax + d$$\n", "\n", "- Entonces:\n", "\n", "$$x=(I-A)^{-1}d$$\n", "\n", "- La matriz $I-A$ es denominada matriz de Leontief.\n", "- La matriz $A$ tiene todo sus autovalores en el círculo unitario y $I-A$ es no singular. Entonces $(I-A)^{-1}$ es no negativa." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "eigA =\n", "\n", " 0.7445\n", " 0.1432\n", " -0.1877\n", "\n", "\n", "detL =\n", "\n", " 0.2600\n" ] } ], "source": [ "eigA = eig(A)\n", "detL = det(eye(3)-A)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "invL =\n", "\n", " 1.7692 1.1538 0.4615\n", " 0.6923 1.5385 0.6154\n", " 1.6923 1.5385 2.6154\n" ] } ], "source": [ "invL = inv(eye(3)-A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Si $d_1=100$, $d_2=50$, y $d_3=200$, las demandas sectoriales serían:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d =\n", "\n", " 100\n", " 50\n", " 200\n", "\n", "\n", "x =\n", "\n", " 326.9231\n", " 269.2308\n", " 769.2308\n" ] } ], "source": [ "d = [100; 50; 200]\n", "x = invL*d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Nos interesa calcular el efecto de un cambio en la demanda autónoma de un producto sobre la demanda total (dadas las relaciones entre sectores).\n", "\n", "$$\\Delta x = (I-A)^{-1} \\Delta d$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Suponga ahora $d'_1=110$, $d'_2=100$, y $d'_3=180$." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dd =\n", "\n", " 10\n", " 50\n", " -20\n", "\n", "\n", "Dx =\n", "\n", " 66.1538\n", " 71.5385\n", " 41.5385\n" ] } ], "source": [ "d2 = [110; 100; 180];\n", "Dd = d2-d\n", "Dx = invL*Dd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.2 Equilibrio de Mercado con Dos Bienes\n", "\n", "*Aplicación tomada del libro \"Matemática para Economistas con Excel y Matlab\" de Alicia Bernardello y coautores*\n", "\n", "El Modelo:\n", "\n", "- Función de Demanda del producto 1: $$Q_{d1} = 40 - 2 P_1 + P_2$$\n", "- Función de Oferta del producto 1: $$Q_{o1} = -5 + 3 P_1 - P_2$$\n", "- Equilibrio en el mercado 1: $$Q_{d1}=Q_{o1}=Q_1$$\n", "- Función de Demanda del producto 2: $$Q_{d2} = 90 + P_1 - P_2$$\n", "- Función de Oferta del producto 2: $$Q_{o2} = -2 + 2 P_2 $$\n", "- Equilibrio en el mercado 2: $$Q_{d2}=Q_{o2}=Q_2$$\n", "\n", "El modelo puede ser resuelto usando el método de reemplazo para encontrar la solución al sistema de ecuaciones. Bastante tedioso. Definamos mas bien el siguiente vector: $$x = (Q_1,Q_2,P_1,P_2)'$$\n", "\n", "Definamos entonces: $$ Ax = b$$\n", "\n", "La solución del sistema de ecuaciones es entonces (si $A$ es invertible): $$x = A^{-1} \\times b$$\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A =\n", "\n", " 1 0 2 -1\n", " 1 0 -3 1\n", " 0 1 -1 1\n", " 0 1 0 -2\n", "\n", "\n", "b =\n", "\n", " 40\n", " -5\n", " 90\n", " -2\n", "\n", "\n", "dA =\n", "\n", " -13\n" ] } ], "source": [ "A = [1 0 2 -1;\n", " 1 0 -3 1;\n", " 0 1 -1 1;\n", " 0 1 0 -2]\n", "b = [40; -5; 90; -2]\n", "dA = det(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Solución:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x =\n", "\n", " 29.7692\n", " 75.6923\n", " 24.5385\n", " 38.8462\n" ] } ], "source": [ "x = A \\ b % alternativamente x =inv(A)*b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Entonces: $Q_1=29.7$ ,$P_1=24.5$, $Q_2=75.7$, $P_2=38.8$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 6.3 Mínimos Cuadrados Ordinarios\n", "\n", "Construiremos dos vectores $X_1$ y $X_2$ de $N \\times 1$ cada uno y con ello un vector $y$ de acuerdo a: \n", "\n", "$$y_i = 3 + 0.5X_{1i} + 0.9X_{2i} + \\epsilon_i$$\n", "\n", "donde $X_{1i} \\sim N(1,4)$, $X_{2i} \\sim N(2,4)$ y $\\epsilon \\sim N(0,1)$.\n", "\n", "A partir de estos datos usaremos la fórmula estándar de MCO para encontrar los coeficientes estimados. $$\\hat{\\beta} = (X'X)^{-1} X'y$$\n", "\n", "- Generando Datos:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "N = 100;\n", "X1 = 1 + 2*randn(N,1);\n", "X2 = 2 + 2*randn(N,1);\n", "eps = randn(N,1);\n", "y = 3 + 0.5*X1 +0.9*X2 + eps;\n", "\n", "data = [y X1 X2];\n", "\n", "dlmwrite('DatosMCO.txt',data);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Cargando Datos y estimado los parámetros:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clear all;\n", "whos" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Name Size Bytes Class Attributes\n", "\n", " impdata 100x3 2400 double\n" ] } ], "source": [ "impdata = importdata('DatosMCO.txt');\n", "whos;" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "beta =\n", "\n", " 2.9913\n", " 0.5640\n", " 0.8020\n" ] } ], "source": [ "[N, K] = size(impdata);\n", "y = impdata(:,1);\n", "X1 = impdata(:,2);\n", "X2 = impdata(:,3);\n", "\n", "X = [ones(N,1) X1 X2];\n", "\n", "beta = (X'*X)\\(X'*y) % alternativa beta = inv(X'*X)*(X'*y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Graficando la predicción y los errores del modelo. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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Od8o0xqEJKZAMQzoWAr6mco9CQsIYZKKhGgCSga0trVNgakNLo0P864suIff0Ssftt9/+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\nqbv25REXaLq+medchtULDq5DjtWYTWpw2FG5EtNQ2G5rau8bu3zHvkU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frwBSwnogXbp0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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "err = y - X*beta;\n", "yhat = X*beta;\n", "\n", "plot([y yhat]);\n", "title('Ajuste')\n", "legend('y','yhat');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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WlbDCtrVCjUDogCDFFKW3ohtZ4NcQUiz96AzzyQIgJxAkQNLJBDy/IwkST4BwAUECJJ0q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\nAEAKIEgAAACk4P8HybnYZcpbjGIAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(err);\n", "title('Errores');" ] } ], "metadata": { "kernelspec": { "display_name": "Matlab", "language": "matlab", "name": "matlab" }, "language_info": { "file_extension": ".m", "help_links": [ { "text": "MetaKernel Magics", "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" } ], "mimetype": "text/x-octave", "name": "matlab", "version": "0.9.4" } }, "nbformat": 4, "nbformat_minor": 1 }