{"id":64,"date":"2018-07-23T22:25:42","date_gmt":"2018-07-24T03:25:42","guid":{"rendered":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/?page_id=64"},"modified":"2023-09-13T22:13:07","modified_gmt":"2023-09-14T04:13:07","slug":"datasets-para-r","status":"publish","type":"page","link":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/?page_id=64","title":{"rendered":"Datasets para R"},"content":{"rendered":"\n
Table I.1<\/a>Descarga<\/a><\/div>\n\n\n\n

tI.1 <- read.table(“https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-I.1.txt”, skip = 6, header = T)<\/p>\n\n\n\n

View(t1.2)<\/p>\n\n\n\n

y <- t1.2[,1]<\/p>\n\n\n\n

x <- t1.2[,2]<\/p>\n\n\n\n

modelo1.2 <- lm(y~x)<\/p>\n\n\n\n

Table2.1<\/a>Descarga<\/a><\/div>\n\n\n\n
Table 3.2<\/a>Descarga<\/a><\/div>\n\n\n\n

t3.2 <- read.table(“https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-3.2.txt”, skip = 6, header = T)<\/p>\n\n\n\n

View(t3.2)<\/p>\n\n\n\n

y <- t3.2[,1]<\/p>\n\n\n\n

x <- t3.2[,2]<\/p>\n\n\n\n

Table 2.7<\/a>Descarga<\/a><\/div>\n\n\n\n

t2.7 <- read.table(“https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-2.7.txt”, skip = 9, header = T)<\/p>\n\n\n\n

View(t2.7)<\/p>\n\n\n\n

y <- t2.7[,1]<\/p>\n\n\n\n

x1 <- t2.7[,2]<\/p>\n\n\n\n

x2 <- t2.7[,7]<\/p>\n\n\n\n

modelo2.7 <- lm(y~x1 + x2)<\/p>\n\n\n\n

Table 2.8<\/a>Descarga<\/a><\/div>\n\n\n\n

t3.5.5 <- read.table(\u201chttps:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-3.5.5.txt\u201d, skip = 6, header = T)<\/p>\n\n\n\n

Table 3.5.5<\/a>Descarga<\/a><\/div>\n\n\n\n

t3.5.5 <- read.table(\u201chttps:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-3.5.5.txt\u201d, skip = 6, header = T)<\/p>\n\n\n\n

Table 3.6<\/a>Descarga<\/a><\/div>\n\n\n\n

t3.6 <- read.table(\u201chttps:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-3.6.txt\u201d, skip = 8, header = T)<\/p>\n\n\n

<\/h1>\n

Introducci\u00f3n a la econometr\u00eda, Wooldridge<\/h1>\n

\u00a0Una\u00a0 explicaci\u00f3n antes de comenzar.<\/h4>\n

 <\/p>\n

\n

Esta p\u00e1gina contiene ejemplos de cada cap\u00edtulo de\u00a0 Introducci\u00f3n a<\/em> la\u00a0Econometr\u00eda :\u00a0 Un Enfoque Moderno,\u00a0 4a\u00a0 edici\u00f3n<\/em>\u00a0de Jeffrey M. Wooldridge.\u00a0 Cada ejemplo ilustra c\u00f3mo cargar los datos, construir modelos econom\u00e9tricos\u00a0 y calcular estimaciones con el programa\u00a0R<\/strong>\u00a0.<\/p>\n

El sitio de recursos complementario al libro proporciona conjuntos de datos disponibles p\u00fablicamente para Eviews, Excel, y el software comercial Stata,\u00a0R<\/strong>\u00a0es la opci\u00f3n de software<\/em> libre.<\/p>\n

Adem\u00e1s, el uso de\u00a0R<\/strong>\u00a0al construir una base en el modelado econom\u00e9trico introduce uno a las herramientas de software capaces de escalar con las demandas de los m\u00e9todos modernos en computaci\u00f3n estad\u00edstica. En el\u00a0Ap\u00e9ndice <\/strong>\u00a0se citan fuentes adicionales sobre el uso de R<\/strong> para la econometr\u00eda.<\/p>\n

Los estudiantes nuevos tanto en econometr\u00eda como en paquete\u00a0R<\/strong>\u00a0pueden considerar que la introducci\u00f3n a ambos es compleja.\u00a0En particular, el proceso de carga y preparaci\u00f3n de datos antes de construir el primer modelo econom\u00e9trico resulta frustrante debido a que no es amigable el programa y cualquier error se constituye en una verdadera complicaci\u00f3n.\u00a0El\u00a0\u00a0<\/code>paquete de datos \u00a0wooldridge\u00a0<\/code>tiene como objetivo simplificar esta tarea.\u00a0Contiene el conjunto de datos de Introducci\u00f3n a la<\/em>\u00a0Econometr\u00eda :\u00a0 Un Enfoque Moderno<\/em>, que se puede cargar con una simple llamada a la\u00a0funci\u00f3n data()<\/code>.<\/p>\n

Lo primero es\u00a0 instalar y carga el paquete\u00a0\u00a0wooldridge con el comando install<\/code>. Para aquellos familiarizados con Stata saben que contiene bases de datos precargadas y con el paquete. Pero con el uso regular de libros de texto, los autores ponen disponibles los datos no s\u00f3lo en la forma regular a trav\u00e9s de archivos indivuales. Ahora tambi\u00e9n se tiene la costrumbre de instalar directamente la biblioteca completa de bases de datos, en este caso los 111 archivos indivuales es cargan directamente con un s\u00f3lo proceso.<\/p>\n

\n
install.packages<\/span>(\"wooldridge\"<\/span>)<\/code><\/pre>\n<\/div>\n
\n
library<\/span>(wooldridge)<\/code><\/pre>\n<\/div>\n<\/div>\n
\n
Cap\u00edtulo 2: El modelo de regresi\u00f3n simple<\/h2>\n
Example 2.10:<\/code>\u00a0Una ecuaci\u00f3n de salario de registro<\/strong><\/p>\n
l\u00a0<\/span>o\u00a0<\/span>g<\/span>(\u00a0<\/span>w\u00a0<\/span>a\u00a0<\/span>g<\/span>e\u00a0<\/span>)<\/span><\/span>1.<\/span><\/span><\/span><\/span>=\u00a0<\/span>\u03b2<\/span>0<\/span><\/span>+\u00a0<\/span>\u03b2<\/span>1<\/span><\/span>e\u00a0<\/span>d<\/span>u\u00a0<\/span>c<\/span><\/span><\/span>logramo(wungramomi)^=\u03b20+\u03b21miretudo<\/span><\/span><\/div>\n
Cargue los\u00a0wage1<\/code>datos y revise la documentaci\u00f3n.<\/p>\n
\n
data<\/span>(\"wage1\"<\/span>)\r\n?wage1<\/code><\/pre>\n<\/div>\n
Estos son datos de la Encuesta de Poblaci\u00f3n Actual de 1976, recopilada por Henry Farber cuando \u00e9l y Wooldridge fueron colegas en el MIT en 1988.<\/p>\n
Estime una relaci\u00f3n lineal entre el\u00a0registro de salario<\/em>\u00a0y\u00a0educaci\u00f3n<\/em>\u00a0.<\/p>\n
\n
log_wage_model <- lm<\/span>(lwage ~<\/span> educ, data =<\/span> wage1)<\/code><\/pre>\n<\/div>\n
Imprimir los resultados.\u00a0Estoy usando el\u00a0stargazer<\/code>paquete para imprimir los resultados del modelo en un formato limpio y f\u00e1cil de leer.\u00a0Ver la bibliograf\u00eda para m\u00e1s informaci\u00f3n.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/tr>\n
<\/td>\nVariable dependiente:<\/em><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nlwage<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
educ<\/td>\n0.083\u00a0***<\/sup>\u00a0(0.008)<\/td>\n<\/tr>\n
Constante<\/td>\n0.584\u00a0***<\/sup>\u00a0(0.097)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Observaciones<\/td>\n526<\/td>\n<\/tr>\n
R\u00a02<\/sup><\/td>\n0.186<\/td>\n<\/tr>\n
R\u00a02<\/sup>\u00a0ajustado<\/td>\n0.184<\/td>\n<\/tr>\n
Residual Std.\u00a0Error<\/td>\n0.480 (df = 524)<\/td>\n<\/tr>\n
Estad\u00edstica F<\/td>\n119.582\u00a0***<\/sup>\u00a0(df \u200b\u200b= 1; 524)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Nota:<\/em><\/td>\np <0.1;\u00a0p <0.05;\u00a0<\/strong><\/em>p <0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
Cap\u00edtulo 3: An\u00e1lisis de regresi\u00f3n m\u00faltiple: estimaci\u00f3n<\/h2>\n
Example 3.2:<\/code>\u00a0Ecuaci\u00f3n de salario por hora<\/strong><\/p>\n
l\u00a0<\/span>o\u00a0<\/span>g<\/span>(\u00a0<\/span>w\u00a0<\/span>a\u00a0<\/span>g<\/span>e\u00a0<\/span>)<\/span><\/span>1.<\/span><\/span><\/span><\/span>=\u00a0<\/span>\u03b2<\/span>0<\/span><\/span>+\u00a0<\/span>\u03b2<\/span>1<\/span><\/span>e\u00a0<\/span>d<\/span>u\u00a0<\/span>c\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>3<\/span><\/span>e\u00a0<\/span>x\u00a0<\/span>p\u00a0<\/span>e\u00a0<\/span>r\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>4<\/span><\/span>t\u00a0<\/span>e\u00a0<\/span>n\u00a0<\/span>u\u00a0<\/span>r\u00a0<\/span>e<\/span><\/span><\/span>logramo(wungramomi)^=\u03b20+\u03b21miretudo+\u03b23miXpagmir+\u03b24tminorteturmi<\/span><\/span><\/div>\n
Estime el modelo de regresi\u00f3n de la\u00a0educaci\u00f3n<\/em>\u00a0, la\u00a0experiencia<\/em>\u00a0y la\u00a0tenencia<\/em>\u00a0contra el\u00a0registro (salario)<\/em>\u00a0.\u00a0Los\u00a0wage1<\/code>datos a\u00fan deber\u00edan estar en su entorno de trabajo.<\/p>\n
\n
hourly_wage_model <- lm<\/span>(lwage ~<\/span> educ +<\/span> exper +<\/span> tenure, data =<\/span> wage1)<\/code><\/pre>\n<\/div>\n
Imprima los coeficientes estimados del modelo:<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/tr>\n
<\/td>\nVariable dependiente:<\/em><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nlwage<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
educ<\/td>\n0.092\u00a0***<\/sup>\u00a0(0.007)<\/td>\n<\/tr>\n
exper<\/td>\n0.004\u00a0**<\/sup>\u00a0(0.002)<\/td>\n<\/tr>\n
tenencia<\/td>\n0.022\u00a0***<\/sup>\u00a0(0.003)<\/td>\n<\/tr>\n
Constante<\/td>\n0.284\u00a0***<\/sup>\u00a0(0.104)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Observaciones<\/td>\n526<\/td>\n<\/tr>\n
R\u00a02<\/sup><\/td>\n0.316<\/td>\n<\/tr>\n
R\u00a02<\/sup>\u00a0ajustado<\/td>\n0.312<\/td>\n<\/tr>\n
Residual Std.\u00a0Error<\/td>\n0.441 (df = 522)<\/td>\n<\/tr>\n
Estad\u00edstica F<\/td>\n80.391\u00a0***<\/sup>\u00a0(df \u200b\u200b= 3; 522)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Nota:<\/em><\/td>\np <0.1;\u00a0p <0.05;\u00a0<\/strong><\/em>p <0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
Cap\u00edtulo 4: An\u00e1lisis de regresi\u00f3n m\u00faltiple: inferencia<\/h2>\n
Example 4.7<\/code>\u00a0Efecto de la capacitaci\u00f3n laboral sobre las tasas de desecho de la empresa<\/strong><\/p>\n
Cargue el\u00a0jtrain<\/code>conjunto de datos y si est\u00e1 utilizando R Studio,\u00a0View<\/code>el conjunto de datos.<\/p>\n
\n
data<\/span>(\"jtrain\"<\/span>)<\/code><\/pre>\n<\/div>\n
De H. Holzer, R. Block, M. Cheatham y J. Knott (1993),\u00a0\u00bfLos subsidios de capacitaci\u00f3n son efectivos?\u00a0The Michigan Experience<\/em>\u00a0, Industrial and Labour Relations Review 46, 625-636.\u00a0Los autores amablemente proporcionaron los datos.<\/p>\n
\n
?jtrain\r\nView<\/span>(jtrain)<\/code><\/pre>\n<\/div>\n
Cree un \u00edndice l\u00f3gico, identificando qu\u00e9 observaciones ocurren en 1987 y son no-uni\u00f3n.<\/p>\n
\n
index <- jtrain$<\/span>year ==<\/span> 1987<\/span> &<\/span> jtrain$<\/span>union ==<\/span> 0<\/span><\/code><\/pre>\n<\/div>\n
A continuaci\u00f3n, subconjunta los datos de jtrain por el nuevo \u00edndice.\u00a0Esto devuelve un data.frame de\u00a0jtrain<\/code>datos de empresas no sindicalizadas para el a\u00f1o 1987.<\/p>\n
\n
jtrain_1987_nonunion <- jtrain[index, ]<\/code><\/pre>\n<\/div>\n
Ahora cree el modelo lineal regresivo\u00a0hrsemp<\/code>(horas totales de capacitaci\u00f3n \/ total de empleados capacitados), el\u00a0lsales<\/code>(registro de ventas anuales) y\u00a0lemploy<\/code>(el registro del n\u00famero de empleados), en contra\u00a0lscrap<\/code>(el registro de la tasa de raspado).<\/p>\n
l\u00a0<\/span>s\u00a0<\/span>c\u00a0<\/span>r\u00a0<\/span>a\u00a0<\/span>p\u00a0<\/span>=\u00a0<\/span>\u03b1\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>1<\/span><\/span>h\u00a0<\/span>r\u00a0<\/span>s\u00a0<\/span>e\u00a0<\/span>m\u00a0<\/span>p\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>2<\/span><\/span>l\u00a0<\/span>s\u00a0<\/span>a\u00a0<\/span>l\u00a0<\/span>e\u00a0<\/span>s\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>3<\/span><\/span>l\u00a0<\/span>e\u00a0<\/span>m\u00a0<\/span>p\u00a0<\/span>l\u00a0<\/span>o\u00a0<\/span>y<\/span><\/span><\/span>lsdorunpag=\u03b1+\u03b21hrsmimetropag+\u03b22lsunlmis+\u03b23lmimetropagloy<\/span><\/span><\/div>\n
\n
linear_model <- lm<\/span>(lscrap ~<\/span> hrsemp +<\/span> lsales +<\/span> lemploy, data =<\/span> jtrain_1987_nonunion)<\/code><\/pre>\n<\/div>\n
Finalmente, imprima el diagn\u00f3stico estad\u00edstico de resumen completo del modelo.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/tr>\n
<\/td>\nVariable dependiente:<\/em><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nlscrap<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
hrsemp<\/td>\n-0.029 (0.023)<\/td>\n<\/tr>\n
lsales<\/td>\n-0,962\u00a0**<\/sup>\u00a0(0,453)<\/td>\n<\/tr>\n
lemploy<\/td>\n0,761\u00a0*<\/sup>\u00a0(0,407)<\/td>\n<\/tr>\n
Constante<\/td>\n12.458\u00a0**<\/sup>\u00a0(5.687)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Observaciones<\/td>\n29<\/td>\n<\/tr>\n
R\u00a02<\/sup><\/td>\n0.262<\/td>\n<\/tr>\n
R\u00a02<\/sup>\u00a0ajustado<\/td>\n0.174<\/td>\n<\/tr>\n
Residual Std.\u00a0Error<\/td>\n1.376 (df = 25)<\/td>\n<\/tr>\n
Estad\u00edstica F<\/td>\n2.965\u00a0*<\/sup>\u00a0(df \u200b\u200b= 3; 25)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Nota:<\/em><\/td>\np <0.1;\u00a0p <0.05;\u00a0<\/strong><\/em>p <0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
\n
Cap\u00edtulo 5: An\u00e1lisis de regresi\u00f3n m\u00faltiple: Asint\u00f3ticos OLS<\/h2>\n
Example 5.3:<\/code>\u00a0Modelo econ\u00f3mico del crimen<\/strong><\/p>\n
De J. Grogger (1991),\u00a0Certeza vs. Severidad de castigo<\/em>\u00a0, Economic Inquiry 29, 297-309.\u00a0El profesor Grogger amablemente proporcion\u00f3 un subconjunto de los datos que utiliz\u00f3 en su art\u00edculo.<\/p>\n
n\u00a0<\/span>a\u00a0<\/span>r\u00a0<\/span>r\u00a0<\/span>86\u00a0<\/span>=\u00a0<\/span>\u03b2<\/span>0<\/span><\/span>+\u00a0<\/span>\u03b2<\/span>1<\/span><\/span>p\u00a0<\/span>c\u00a0<\/span>n\u00a0<\/span>v\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>2<\/span><\/span>a\u00a0<\/span>v\u00a0<\/span>g<\/span>s\u00a0<\/span>e\u00a0<\/span>n\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>3<\/span><\/span>t\u00a0<\/span>o\u00a0<\/span>t\u00a0<\/span>t\u00a0<\/span>i\u00a0<\/span>m\u00a0<\/span>e\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>4<\/span><\/span>p\u00a0<\/span>t\u00a0<\/span>i\u00a0<\/span>m\u00a0<\/span>e\u00a0<\/span>86\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>5<\/span><\/span>q<\/span>e\u00a0<\/span>m\u00a0<\/span>p\u00a0<\/span>86\u00a0<\/span>+\u00a0<\/span>\u03bc<\/span><\/span><\/span>norteunrr86=\u03b20+\u03b21pagdonortev+\u03b22unvgramosminorte+\u03b23tottyometromi+\u03b24pagtyometromi86+\u03b25qmimetropag86+\u03bc<\/span><\/span><\/div>\n
n\u00a0<\/span>a\u00a0<\/span>r\u00a0<\/span>r\u00a0<\/span>86\u00a0<\/span>:<\/span><\/span><\/span>norteunrr86:<\/span><\/span><\/span>n\u00famero de veces arrestado, 1986.\u00a0p<\/span><\/span><\/span><\/span><\/span>c<\/span><\/span><\/span><\/span><\/span>n<\/span><\/span><\/span><\/span><\/span>v<\/span><\/span><\/span><\/span><\/span>:<\/span><\/span><\/span><\/span><\/span>
\n<\/span><\/span><\/span><\/span><\/span><\/span><\/span>pagdonortev:<\/span><\/span><\/span>proporci\u00f3n de detenciones previas que conducen a condenas\u00a0a<\/span><\/span><\/span><\/span><\/span>v<\/span><\/span><\/span><\/span><\/span>g<\/span><\/span><\/span><\/span><\/span>
\n<\/span><\/span><\/span>s\u00a0<\/span>e\u00a0<\/span>n\u00a0<\/span>:<\/span><\/span><\/span>unvgramosminorte:<\/span><\/span><\/span>oraci\u00f3n promedio cumplida, duraci\u00f3n en meses.\u00a0t<\/span><\/span><\/span><\/span><\/span>o<\/span><\/span><\/span><\/span><\/span>t<\/span><\/span><\/span><\/span><\/span>t<\/span><\/span><\/span><\/span><\/span>i<\/span><\/span><\/span><\/span><\/span>m<\/span><\/span><\/span><\/span><\/span>e<\/span><\/span><\/span><\/span><\/span>:<\/span><\/span><\/span><\/span><\/span>
\n<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>tottyometromi:<\/span><\/span><\/span>tiempo en prisi\u00f3n desde que cumpli\u00f3 18 a\u00f1os, duraci\u00f3n en meses.\u00a0p<\/span><\/span><\/span><\/span><\/span>t<\/span><\/span><\/span><\/span><\/span>i<\/span><\/span><\/span><\/span><\/span>m<\/span><\/span><\/span><\/span><\/span>e<\/span><\/span><\/span><\/span><\/span>86<\/span><\/span><\/span><\/span><\/span>:<\/span><\/span><\/span><\/span><\/span>
\n<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>pagtyometromi86:<\/span><\/span><\/span>meses de prisi\u00f3n durante 1986.\u00a0q<\/span><\/span><\/span><\/span><\/span>
\n<\/span>e\u00a0<\/span>m\u00a0<\/span>p\u00a0<\/span>86\u00a0<\/span>:<\/span><\/span><\/span>qmimetropag86:<\/span><\/span><\/span>\u00a0cuartos empleados, 1986.<\/p>\n
Cargue el\u00a0crime1<\/code>conjunto de datos.<\/p>\n
\n
data<\/span>(\"crime1\"<\/span>)\r\n?crime1<\/code><\/pre>\n<\/div>\n
Estime el modelo.<\/p>\n
\n
restricted_model <- lm<\/span>(narr86 ~<\/span> pcnv +<\/span> ptime86 +<\/span> qemp86, data =<\/span> crime1)<\/code><\/pre>\n<\/div>\n
Crea una nueva variable que\u00a0restricted_model_u<\/code>contenga los residuos\u00a0\u03bc<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>~<\/span><\/span><\/span><\/span><\/span><\/span>\u03bc~<\/span><\/span><\/span>\u00a0de la regresi\u00f3n anterior.<\/p>\n
\n
restricted_model_u <- restricted_model$<\/span>residuals<\/code><\/pre>\n<\/div>\n
A continuaci\u00f3n, regresi\u00f3n\u00a0pcnv, ptime86, qemp86, avgsen<\/code>, y\u00a0tottime<\/code>, contra los residuos\u00a0\u03bc<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>~<\/span><\/span><\/span><\/span><\/span><\/span>\u03bc~<\/span><\/span><\/span>guardado en\u00a0restricted_model_u<\/code>.<\/p>\n
\u03bc<\/span>~<\/span><\/span><\/span><\/span>=\u00a0<\/span>\u03b2<\/span>1<\/span><\/span>p\u00a0<\/span>c\u00a0<\/span>n\u00a0<\/span>v\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>2<\/span><\/span>a\u00a0<\/span>v\u00a0<\/span>g<\/span>s\u00a0<\/span>e\u00a0<\/span>n\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>3<\/span><\/span>t\u00a0<\/span>o\u00a0<\/span>t\u00a0<\/span>t\u00a0<\/span>i\u00a0<\/span>m\u00a0<\/span>e\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>4<\/span><\/span>p\u00a0<\/span>t\u00a0<\/span>i\u00a0<\/span>m\u00a0<\/span>e\u00a0<\/span>86\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>5<\/span><\/span>q<\/span>e\u00a0<\/span>m\u00a0<\/span>p\u00a0<\/span>86<\/span><\/span><\/span>\u03bc~=\u03b21pagdonortev+\u03b22unvgramosminorte+\u03b23tottyometromi+\u03b24pagtyometromi86+\u03b25qmimetropag86<\/span><\/span><\/div>\n
\n
LM_u_model <- lm<\/span>(restricted_model_u ~<\/span> pcnv +<\/span> ptime86 +<\/span> qemp86 +<\/span> avgsen +<\/span> tottime, \r\n    data =<\/span> crime1)\r\nsummary<\/span>(LM_u_model)$<\/span>r.square<\/code><\/pre>\n<\/div>\n
## [1] 0.001493846<\/code><\/pre>\n
L\u00a0<\/span>M<\/span>=\u00a0<\/span>2\u00a0<\/span>,\u00a0<\/span>725\u00a0<\/span>(\u00a0<\/span>0.0015\u00a0<\/span>)<\/span><\/span><\/span>LMETRO=2,725(0.0015)<\/span><\/span><\/div>\n
\n
LM_test <- nobs<\/span>(LM_u_model) *<\/span> 0.0015<\/span>\r\nLM_test<\/code><\/pre>\n<\/div>\n
## [1] 4.0875<\/code><\/pre>\n
\n
qchisq<\/span>(1<\/span> -<\/span> 0.10<\/span>, 2<\/span>)<\/code><\/pre>\n<\/div>\n
## [1] 4.60517<\/code><\/pre>\n
El\u00a0p-<\/em>\u00a0valor es:<\/p>\n
PAG<\/span>(\u00a0<\/span>X<\/span>2<\/span>2<\/span><\/span><\/span><\/span>>\u00a0<\/span>4.09\u00a0<\/span>)\u00a0<\/span>\u2248\u00a0<\/span>0.129<\/span><\/span><\/span>PAG(X22>4.09)\u22480.129<\/span><\/span><\/div>\n
\n
1<\/span>-<\/span>pchisq<\/span>(LM_test, 2<\/span>)<\/code><\/pre>\n<\/div>\n
## [1] 0.129542<\/code><\/pre>\n<\/div>\n
\n
Cap\u00edtulo 6: Regresi\u00f3n m\u00faltiple: otros problemas<\/h2>\n
Example 6.1:<\/code>\u00a0Efectos de la contaminaci\u00f3n en los precios de la vivienda, estandarizados.<\/strong><\/p>\n
p\u00a0<\/span>r\u00a0<\/span>i\u00a0<\/span>c\u00a0<\/span>e\u00a0<\/span>=\u00a0<\/span>\u03b2<\/span>0<\/span><\/span>+\u00a0<\/span>\u03b2<\/span>1<\/span><\/span>n\u00a0<\/span>o\u00a0<\/span>x\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>2<\/span><\/span>c\u00a0<\/span>r\u00a0<\/span>i\u00a0<\/span>m\u00a0<\/span>e\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>3<\/span><\/span>r\u00a0<\/span>o\u00a0<\/span>o\u00a0<\/span>m\u00a0<\/span>s\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>4<\/span><\/span>re<\/span>yo\u00a0<\/span>s\u00a0<\/span>t\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>5<\/span><\/span>s\u00a0<\/span>t\u00a0<\/span>r\u00a0<\/span>a\u00a0<\/span>t\u00a0<\/span>i\u00a0<\/span>o\u00a0<\/span>+\u00a0<\/span>\u03bc<\/span><\/span><\/span>pagryodomi=\u03b20+\u03b21norteoX+\u03b22doryometromi+\u03b23roometros+\u03b24reyost+\u03b25struntyoo+\u03bc<\/span><\/span><\/div>\n
p\u00a0<\/span>r\u00a0<\/span>i\u00a0<\/span>c\u00a0<\/span>e<\/span><\/span><\/span>pagryodomi<\/span><\/span><\/span>: precio medio de la vivienda.<\/p>\n
n\u00a0<\/span>o\u00a0<\/span>x<\/span><\/span><\/span>norteoX<\/span><\/span><\/span>: Concentraci\u00f3n de \u00f3xido nitroso;\u00a0partes por mill\u00f3n.<\/p>\n
c\u00a0<\/span>r\u00a0<\/span>i\u00a0<\/span>m\u00a0<\/span>e<\/span><\/span><\/span>doryometromi<\/span><\/span><\/span>: n\u00famero de cr\u00edmenes reportados per capita.<\/p>\n
r\u00a0<\/span>o\u00a0<\/span>o\u00a0<\/span>m\u00a0<\/span>s<\/span><\/span><\/span>roometros<\/span><\/span><\/span>: n\u00famero promedio de habitaciones en casas en la comunidad.<\/p>\n
re<\/span>i\u00a0<\/span>s\u00a0<\/span>t<\/span><\/span><\/span>reyost<\/span><\/span><\/span>: distancia ponderada de la comunidad a 5 centros de empleo.<\/p>\n
s\u00a0<\/span>t\u00a0<\/span>r\u00a0<\/span>a\u00a0<\/span>t\u00a0<\/span>i\u00a0<\/span>o<\/span><\/span><\/span>struntyoo<\/span><\/span><\/span>: proporci\u00f3n promedio de alumnos por maestro en las escuelas de la comunidad.<\/p>\n
z<\/span>p\u00a0<\/span>r\u00a0<\/span>i\u00a0<\/span>c\u00a0<\/span>e<\/span><\/span>1.<\/span><\/span><\/span><\/span>=\u00a0<\/span>\u03b2<\/span>1<\/span><\/span>z<\/span>n\u00a0<\/span>o\u00a0<\/span>x\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>2<\/span><\/span>z<\/span>c\u00a0<\/span>r\u00a0<\/span>i\u00a0<\/span>m\u00a0<\/span>e\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>3<\/span><\/span>z<\/span>r\u00a0<\/span>o\u00a0<\/span>o\u00a0<\/span>m\u00a0<\/span>s\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>4<\/span><\/span>z<\/span>re<\/span>yo\u00a0<\/span>s\u00a0<\/span>t\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>5<\/span><\/span>z<\/span>s\u00a0<\/span>t\u00a0<\/span>r\u00a0<\/span>a\u00a0<\/span>t\u00a0<\/span>i\u00a0<\/span>o<\/span><\/span><\/span>zpagryodomi^=\u03b21znorteoX+\u03b22zdoryometromi+\u03b23zroometros+\u03b24zreyost+\u03b25zstruntyoo<\/span><\/span><\/div>\n
Cargue los\u00a0hprice2<\/code>datos y vea la documentaci\u00f3n.<\/p>\n
\n
data<\/span>(\"hprice2\"<\/span>)\r\n?hprice2<\/code><\/pre>\n<\/div>\n
Datos de\u00a0Hedonic Housing Prices y Demand for Clean Air<\/em>\u00a0, por Harrison, D. y DLRubinfeld, Journal of Environmental Economics and Management 5, 81-102.\u00a0Diego Garc\u00eda, un ex Ph.D.\u00a0estudiante de econom\u00eda en el MIT, amablemente proporcion\u00f3 estos datos, que obtuvo del libro Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, por DA Belsey, E. Kuh, y R. Welsch, 1990. Nueva York: Wiley.<\/p>\n
Estime el coeficiente con el\u00a0lm<\/code>modelo de regresi\u00f3n\u00a0habitual\u00a0, pero esta vez, coeficientes estandarizados envolviendo cada variable con la\u00a0scale<\/code>funci\u00f3n\u00a0de R\u00a0:<\/p>\n
\n
housing_standard <- lm<\/span>(scale<\/span>(price) ~<\/span> 0<\/span> +<\/span> scale<\/span>(nox) +<\/span> scale<\/span>(crime) +<\/span> scale<\/span>(rooms) +<\/span> \r\n    scale<\/span>(dist) +<\/span> scale<\/span>(stratio), data =<\/span> hprice2)<\/code><\/pre>\n<\/div>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/tr>\n
<\/td>\nVariable dependiente:<\/em><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nescala (precio)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
escala (nox)<\/td>\n-0.340\u00a0***<\/sup>\u00a0(0.044)<\/td>\n<\/tr>\n
escala (crimen)<\/td>\n-0.143\u00a0***<\/sup>\u00a0(\u00a00.031\u00a0)<\/td>\n<\/tr>\n
escala (habitaciones)<\/td>\n0.514\u00a0***<\/sup>\u00a0(0.030)<\/td>\n<\/tr>\n
escala (dist)<\/td>\n-0.235\u00a0***<\/sup>\u00a0(0.043)<\/td>\n<\/tr>\n
escala (stratio)<\/td>\n-0.270\u00a0***<\/sup>\u00a0(0.030)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Observaciones<\/td>\n506<\/td>\n<\/tr>\n
R\u00a02<\/sup><\/td>\n0.636<\/td>\n<\/tr>\n
R\u00a02<\/sup>\u00a0ajustado<\/td>\n0.632<\/td>\n<\/tr>\n
Residual Std.\u00a0Error<\/td>\n0.606 (df = 501)<\/td>\n<\/tr>\n
Estad\u00edstica F<\/td>\n174.822\u00a0***<\/sup>\u00a0(df \u200b\u200b= 5; 501)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Nota:<\/em><\/td>\np <0.1;\u00a0p <0.05;\u00a0<\/strong><\/em>p <0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Example 6.2:<\/code>\u00a0Efectos de la contaminaci\u00f3n en los precios de la vivienda, t\u00e9rmino interactivo cuadr\u00e1tico<\/strong><\/p>\n
Modificar el modelo de vivienda, agregando un t\u00e9rmino cuadr\u00e1tico en las\u00a0habitaciones<\/em>\u00a0:<\/p>\n
l\u00a0<\/span>o\u00a0<\/span>g<\/span>(\u00a0<\/span>p\u00a0<\/span>r\u00a0<\/span>i\u00a0<\/span>c\u00a0<\/span>e\u00a0<\/span>)\u00a0<\/span>=\u00a0<\/span>\u03b2<\/span>0<\/span><\/span>+\u00a0<\/span>\u03b2<\/span>1<\/span><\/span>l\u00a0<\/span>o\u00a0<\/span>g<\/span>(\u00a0<\/span>n\u00a0<\/span>o\u00a0<\/span>x\u00a0<\/span>)\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>2<\/span><\/span>l\u00a0<\/span>o\u00a0<\/span>g<\/span>(\u00a0<\/span>d<\/span>i\u00a0<\/span>s\u00a0<\/span>t\u00a0<\/span>)\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>3<\/span><\/span>r\u00a0<\/span>o\u00a0<\/span>o\u00a0<\/span>m\u00a0<\/span>s\u00a0<\/span>+\u00a0<\/span>\u03b2<\/span>4<\/span><\/span>r\u00a0<\/span>o\u00a0<\/span>o\u00a0<\/span>m\u00a0<\/span>s<\/span>2<\/span><\/span>+\u00a0<\/span>\u03b2<\/span>5<\/span><\/span>s\u00a0<\/span>t\u00a0<\/span>r\u00a0<\/span>a\u00a0<\/span>t\u00a0<\/span>i\u00a0<\/span>o\u00a0<\/span>+\u00a0<\/span>\u03bc<\/span><\/span><\/span>logramo(pagryodomi)=\u03b20+\u03b21logramo(norteoX)+\u03b22logramo(reyost)+\u03b23roometros+\u03b24roometros2+\u03b25struntyoo+\u03bc<\/span><\/span><\/div>\n
\n
housing_interactive <- lm<\/span>(lprice ~<\/span> lnox +<\/span> log<\/span>(dist) +<\/span> rooms+<\/span>I<\/span>(rooms^<\/span>2<\/span>) +<\/span> stratio, data =<\/span> hprice2)<\/code><\/pre>\n<\/div>\n
Compare los resultados con el modelo de\u00a0example 6.1<\/code>.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/tr>\n
<\/td>\nVariable dependiente:<\/em><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nescala (precio)<\/td>\nlprice<\/td>\n<\/tr>\n
<\/td>\n(1)<\/td>\n(2)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
escala (nox)<\/td>\n-0.340\u00a0***<\/sup>\u00a0(0.044)<\/td>\n<\/td>\n<\/tr>\n
escala (crimen)<\/td>\n-0.143\u00a0***<\/sup>\u00a0(\u00a00.031\u00a0)<\/td>\n<\/td>\n<\/tr>\n
escala (habitaciones)<\/td>\n0.514\u00a0***<\/sup>\u00a0(0.030)<\/td>\n<\/td>\n<\/tr>\n
escala (dist)<\/td>\n-0.235\u00a0***<\/sup>\u00a0(0.043)<\/td>\n<\/td>\n<\/tr>\n
escala (stratio)<\/td>\n-0.270\u00a0***<\/sup>\u00a0(0.030)<\/td>\n<\/td>\n<\/tr>\n
lnox<\/td>\n<\/td>\n-0.902\u00a0***<\/sup>\u00a0(\u00a00.115\u00a0)<\/td>\n<\/tr>\n
log (dist)<\/td>\n<\/td>\n-0.087\u00a0**<\/sup>\u00a0(0.043)<\/td>\n<\/tr>\n
habitaciones<\/td>\n<\/td>\n-0.545\u00a0***<\/sup>\u00a0(0.165)<\/td>\n<\/tr>\n
Yo (habitaciones2)<\/td>\n<\/td>\n0.062\u00a0***<\/sup>\u00a0(0.013)<\/td>\n<\/tr>\n
stratio<\/td>\n<\/td>\n-0.048\u00a0***<\/sup>\u00a0(0.006)<\/td>\n<\/tr>\n
Constante<\/td>\n<\/td>\n13.385\u00a0***<\/sup>\u00a0(0.566)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Observaciones<\/td>\n506<\/td>\n506<\/td>\n<\/tr>\n
R\u00a02<\/sup><\/td>\n0.636<\/td>\n0.603<\/td>\n<\/tr>\n
R\u00a02<\/sup>\u00a0ajustado<\/td>\n0.632<\/td>\n0.599<\/td>\n<\/tr>\n
Residual Std.\u00a0Error<\/td>\n0.606 (df = 501)<\/td>\n0,259 (df = 500)<\/td>\n<\/tr>\n
Estad\u00edstica F<\/td>\n174.822\u00a0***<\/sup>\u00a0(df \u200b\u200b= 5; 501)<\/td>\n151.770\u00a0***<\/sup>\u00a0(df \u200b\u200b= 5; 500)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Nota:<\/em><\/td>\np <0.1;\u00a0p <0.05;\u00a0<\/strong><\/em>p <0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
<\/div>\n
\n\n\n\n\n\n\n\n
<\/td>\n0.587<\/td>\n<\/tr>\n
Residual Std.\u00a0Error<\/td>\n1.843 (df = 53)<\/td>\n<\/tr>\n
Estad\u00edstica F<\/td>\n40.094\u00a0***<\/sup>\u00a0(df \u200b\u200b= 2; 53)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Nota:<\/em><\/td>\np <0.1;\u00a0p <0.05;\u00a0<\/strong><\/em>p <0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Example 10.11:<\/code>\u00a0Efectos estacionales de las presentaciones antidumping<\/strong><\/p>\n
CM Krupp y PS Pollard (1999),\u00a0Las respuestas del mercado a las leyes antidumping: algunas pruebas de la industria qu\u00edmica de los Estados Unidos<\/em>\u00a0, Canadian Journal of Economics 29, 199-227.\u00a0El Dr. Krupp amablemente proporcion\u00f3 los datos.\u00a0Son datos mensuales que abarcan desde febrero de 1978 hasta diciembre de 1988.<\/p>\n
\n
data<\/span>(\"barium\"<\/span>)\r\n?barium<\/code><\/pre>\n<\/div>\n
\n
barium_imports <- lm<\/span>(lchnimp ~<\/span> lchempi +<\/span> lgas +<\/span> lrtwex +<\/span> befile6 +<\/span> affile6 +<\/span> \r\n    afdec6, data =<\/span> barium)<\/code><\/pre>\n<\/div>\n
Estime un nuevo modelo,\u00a0barium_seasonal<\/code>que tenga en cuenta la estacionalidad mediante la adici\u00f3n de variables ficticias contenidas en los datos.\u00a0Calcule el\u00a0anova<\/code>entre los dos modelos.<\/p>\n
\n
barium_seasonal <- lm<\/span>(lchnimp ~<\/span> lchempi +<\/span> lgas +<\/span> lrtwex +<\/span> befile6 +<\/span> affile6 +<\/span> \r\n    afdec6 +<\/span> feb +<\/span> mar +<\/span> apr +<\/span> may +<\/span> jun +<\/span> jul +<\/span> aug +<\/span> sep +<\/span> oct +<\/span> nov +<\/span> dec, \r\n    data =<\/span> barium)\r\nbarium_anova <- anova<\/span>(barium_imports, barium_seasonal)<\/code><\/pre>\n<\/div>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/tr>\n
<\/td>\nVariable dependiente:<\/em><\/td>\n<\/tr>\n
<\/td>\n<\/td>\n<\/tr>\n
<\/td>\nlchnimp<\/td>\n<\/tr>\n
<\/td>\n(1)<\/td>\n(2)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
lchempi<\/td>\n3.117\u00a0***<\/sup>\u00a0(0.479)<\/td>\n3.265\u00a0***<\/sup>\u00a0(0.493)<\/td>\n<\/tr>\n
lgas<\/td>\n0.196 (0.907)<\/td>\n-1.278 (1.389)<\/td>\n<\/tr>\n
lrtwex<\/td>\n0,983\u00a0**<\/sup>\u00a0(0,400)<\/td>\n0,663 (0,471)<\/td>\n<\/tr>\n
befile6<\/td>\n0.060 (0.261)<\/td>\n0.140 (0.267)<\/td>\n<\/tr>\n
affile6<\/td>\n-0.032 (0.264)<\/td>\n0.013 (0.279)<\/td>\n<\/tr>\n
afdec6<\/td>\n-0.565\u00a0*<\/sup>\u00a0(0.286)<\/td>\n-0.521\u00a0*<\/sup>\u00a0(0.302)<\/td>\n<\/tr>\n
feb<\/td>\n<\/td>\n-0.418 (0.304)<\/td>\n<\/tr>\n
mar<\/td>\n<\/td>\n0.059 (0.265)<\/td>\n<\/tr>\n
abr<\/td>\n<\/td>\n-0.451\u00a0*<\/sup>\u00a0(0.268)<\/td>\n<\/tr>\n
mayo<\/td>\n<\/td>\n0.033 (0.269)<\/td>\n<\/tr>\n
jun<\/td>\n<\/td>\n-0.206 (0.269)<\/td>\n<\/tr>\n
jul<\/td>\n<\/td>\n0.004 (0.279)<\/td>\n<\/tr>\n
ago<\/td>\n<\/td>\n-0.157 (0.278)<\/td>\n<\/tr>\n
sep<\/td>\n<\/td>\n-0.134 (0.268)<\/td>\n<\/tr>\n
oct<\/td>\n<\/td>\n0.052 (0.267)<\/td>\n<\/tr>\n
nov<\/td>\n<\/td>\n-0.246 (0.263)<\/td>\n<\/tr>\n
dic<\/td>\n<\/td>\n0.133 (0.271)<\/td>\n<\/tr>\n
Constante<\/td>\n-17.803 (21.045)<\/td>\n16.779 (32.429)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Observaciones<\/td>\n131<\/td>\n131<\/td>\n<\/tr>\n
R\u00a02<\/sup><\/td>\n0.305<\/td>\n0.358<\/td>\n<\/tr>\n
R\u00a02<\/sup>\u00a0ajustado<\/td>\n0.271<\/td>\n0.262<\/td>\n<\/tr>\n
Residual Std.\u00a0Error<\/td>\n0.597 (df = 124)<\/td>\n0.601 (df = 113)<\/td>\n<\/tr>\n
Estad\u00edstica F<\/td>\n9.064\u00a0***<\/sup>\u00a0(df \u200b\u200b= 6; 124)<\/td>\n3.712\u00a0***<\/sup>\u00a0(df \u200b\u200b= 17; 113)<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Nota:<\/em><\/td>\np <0.1;\u00a0p <0.05;\u00a0<\/strong><\/em>p <0.01<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n\n\n\n\n\n\n\n\n
<\/td>\n<\/tr>\n
Estad\u00edstica<\/td>\nnorte<\/td>\nMedia<\/td>\nSt. Dev.<\/td>\nMin.<\/td>\nMax<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
Res.Df<\/td>\n2<\/td>\n118.500<\/td>\n7.778<\/td>\n113<\/td>\n124<\/td>\n<\/tr>\n
RSS<\/td>\n2<\/td>\n42.545<\/td>\n2.406<\/td>\n40.844<\/td>\n44.247<\/td>\n<\/tr>\n
Df<\/td>\n1<\/td>\n11,000<\/td>\n<\/td>\n11<\/td>\n11<\/td>\n<\/tr>\n
Suma de Sq<\/td>\n1<\/td>\n3.403<\/td>\n<\/td>\n3.403<\/td>\n3.403<\/td>\n<\/tr>\n
F<\/td>\n1<\/td>\n0.856<\/td>\n<\/td>\n0.856<\/td>\n0.856<\/td>\n<\/tr>\n
Pr (> F)<\/td>\n1<\/td>\n0.585<\/td>\n<\/td>\n0.585<\/td>\n0.585<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n
<\/div>\n
<\/div>\n
<\/div>\n
\n
\n
Econometr\u00eda Aplicada con R<\/h3>\n
es mi proyecto registrado en el Consejo Divisional de la DCSH.<\/p>\n
\n
Este proyecto presenta\u00a0 los m\u00e9todos de la\u00a0 econometr\u00eda aplicada que utiliza el sistema R para computaci\u00f3n y gr\u00e1ficos estad\u00edsticos.\u00a0Presenta ejemplos pr\u00e1cticos para una amplia gama de modelos econom\u00e9tricos, desde modelos de regresi\u00f3n lineal cl\u00e1sicos para secciones transversales, series de tiempo o datos de panel y los modelos comunes no lineales de microeconometr\u00eda, como modelos logit, probit y tobit, hasta modelos semiparam\u00e9tricos recientes. extensiones\u00a0Adem\u00e1s, proporciona un cap\u00edtulo sobre programaci\u00f3n, que incluye simulaciones, optimizaci\u00f3n y una introducci\u00f3n a las herramientas R que permite una investigaci\u00f3n econom\u00e9trica reproducible.\u00a0Un paquete R que acompa\u00f1a a este libro, AER, est\u00e1 disponible en la Red Integral de Archivos R (CRAN) en\u00a0https:\/\/CRAN.R-project.org\/package=AER<\/a>\u00a0.<\/p>\n<\/blockquote>\n
 <\/p>\n<\/div>\n
\n
Bibliograf\u00eda<\/h2>\n
Yves Croissant, Giovanni Millo (2008).\u00a0Econometr\u00eda de datos de panel en R: El paquete de plm<\/em>\u00a0.\u00a0Journal of Statistical Software 27 (2).\u00a0URL www.jstatsoft.org\/v27\/i02\/.<\/p>\n
Marek Hlavac (2015).\u00a0stargazer: Regresi\u00f3n con formato correcto y tablas de estad\u00edsticas de resumen<\/em>\u00a0.\u00a0Paquete R versi\u00f3n 5.2.\u00a0https:\/\/CRAN.R-project.org\/package=stargazer<\/a><\/p>\n
Christian Kleiber y Achim Zeileis (2008).\u00a0Econometr\u00eda Aplicada con R<\/em>\u00a0.\u00a0Nueva York: Springer-Verlag.\u00a0ISBN 978-0-387-77316-2.\u00a0URL\u00a0https:\/\/CRAN.R-project.org\/package=AER<\/a><\/p>\n
Franz Mohr (2015).\u00a0prais: Prais-Winsten Procedimiento de estimaci\u00f3n para AR (1) Correlaci\u00f3n en serie<\/em>\u00a0.\u00a0Versi\u00f3n del paquete R 0.1.1.\u00a0https:\/\/CRAN.R-project.org\/package=prais<\/a><\/p>\n
R Core Team (2018).\u00a0R: Un lenguaje y entorno para la inform\u00e1tica estad\u00edstica<\/em>\u00a0.\u00a0R Foundation for Statistical Computing, Viena, Austria.\u00a0URL\u00a0https:\/\/www.R-project.org\/<\/a>\u00a0.<\/p>\n
Hadley Wickham y Winston Chang (2016).\u00a0devtools: herramientas para hacer que los paquetes R en desarrollo sean m\u00e1s f\u00e1ciles<\/em>\u00a0.\u00a0Paquete R versi\u00f3n 1.12.0.\u00a0https:\/\/CRAN.R-project.org\/package=devtools<\/a><\/p>\n
Hadley Wickham.\u00a0prueba de que: comience con las pruebas<\/em>\u00a0.\u00a0Paquete R versi\u00f3n 1.0.2.\u00a0https:\/\/CRAN.R-project.org\/package=testthat<\/a><\/p>\n
 <\/p>\n
EL LIBRO DE WOOLDRIDGE, ES UN CLASICO. YA VAMOS EN LA 4ta. edici\u00f3n en espa\u00f1ol y la 6ta en ingl\u00e9s.<\/p>\n
Jeffrey M. Wooldridge (2016).\u00a0Econometr\u00eda introductoria: un enfoque moderno<\/em>\u00a0.\u00a0Mason, Ohio: South-Western Cengage Learning.<\/p>\n
 <\/p>\n
AGRADEZCO ESPECIALMENTE AL Dr. Yihui Xie,\u00a0 \u00a0por sus amables recomendaciones para mejorar este sitio. El Dr. Yihui Xie, actualmente trabaja en RStudio, Inc.<\/p>\n
Yihui Xie (2018).\u00a0knitr: Un paquete de prop\u00f3sito general para la Generaci\u00f3n de informes din\u00e1micos en I<\/em>\u00a0.\u00a0Paquete R versi\u00f3n 1.16.\u00a0https:\/\/CRAN.R-project.org\/package=knitr<\/a><\/p>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"
tI.1 <- read.table(“https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-I.1.txt”, skip = 6, header = T) View(t1.2) y <- t1.2[,1] x <- t1.2[,2] modelo1.2 <- lm(y~x) t3.2 <- read.table(“https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-3.2.txt”, skip = 6, header = T) View(t3.2) y <- t3.2[,1] x <- t3.2[,2] t2.7 <- read.table(“https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/wp-content\/uploads\/2023\/09\/Table-2.7.txt”, skip = 9, header = T) View(t2.7) y <- t2.7[,1] x1 <- t2.7[,2] x2 <- t2.7[,7] modelo2.7 … Seguir leyendo Datasets para R<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-64","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/pages\/64","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=64"}],"version-history":[{"count":17,"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/pages\/64\/revisions"}],"predecessor-version":[{"id":404,"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=\/wp\/v2\/pages\/64\/revisions\/404"}],"wp:attachment":[{"href":"https:\/\/dcsh.izt.uam.mx\/proyectos\/r\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=64"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}