{"id":3230,"date":"2022-06-08T21:31:31","date_gmt":"2022-06-08T21:31:31","guid":{"rendered":"https:\/\/ricovictor.com\/?p=3230"},"modified":"2026-01-29T21:42:41","modified_gmt":"2026-01-29T21:42:41","slug":"variables-dicotomas-dummy-en-modelos-de-regresion","status":"publish","type":"post","link":"https:\/\/ricovictor.com\/index.php\/2022\/06\/08\/variables-dicotomas-dummy-en-modelos-de-regresion\/","title":{"rendered":"Variables Dic\u00f3tomas (Dummy) en Modelos de Regresi\u00f3n"},"content":{"rendered":"\t\t
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Las variables Dic\u00f3tomas , tambi\u00e9n llamadas Dummy en ingl\u00e9s, son aquellas variables de car\u00e1cter cualitativo que podemos a\u00f1adir a modelos de regresi\u00f3n<\/strong>. Por ejemplo, en econom\u00eda variables cualitativas como sexo o raza pueden influir en muchos estudios. De forma que podemos a\u00f1adir a modelos de caracter cuantitativo tambi\u00e9n variables cualitativas.<\/p>

Una manera de \u201ccuantificar\u201d tales atributos es mediante variables artificiales que toman los valores 0 o 1, donde 1 indica la presencia (o posesi\u00f3n) de ese atributo y 0 su ausencia. Un ejemplo que luego veremos en el tutorial es establecer si existe cambio estructural o no, poniendo 1 si existe ese cambio y 0 si no se da.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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C\u00d3DIGO R<\/h2>

\u00a0<\/p>

#VARIABLES DICOTOMAS,BINARIAS,CATEGORICAS O DUMMY<\/p>

# Creamos las variable dicotoma a partir del punto de ruptura estructural<\/p>

# 0 antes de la ruptura
# 1 despu\u00e9s de la ruptura<\/p>


#install.packages(\u00abquantmod\u00bb)
library(quantmod)
#install.packages(\u00abtseries\u00bb)
library(tseries)
#install.packages(\u00abfImport\u00bb)
library(fImport)<\/p>


Ahorro <- c(61,68.6,63.6,89.6, 97.6,104.4,96.4,92.5,112.6,130.1,161.8,199.1,205.5,
167,235.7,206.2,196.5,168.4,189.1,187.8,208.7,246.4,272.6,214.4,189.4,249.3)<\/p>

Ingreso <- c( 727.1,790.2,855.3,965, 1054.2,1159.2,1273,1401.4,1580.1,1769.5,1973.3,2200.2,2347.3,
2522.4,2810,3002,3187.6,3363.1,3640.8,3894.5,4166.8,4343.7,4613.7,4790.2,
5021.7,5320.8)<\/p>

s <- as.Date(\u00ab1970-12-31\u00bb)
e <- as.Date(\u00ab1995-12-31\u00bb)<\/p>

seq(from=s, to=e, by=\u00bbyear\u00bb)<\/p>

A\u00f1o <- seq(from=s, to=e, by=\u00bbyear\u00bb)<\/p>

tabla_ahorro_ingres <- data.frame(A\u00f1o,Ahorro,Ingreso)<\/p>

## Periodos que analizaremos- 1970 a 1981
## 1982 a 1995<\/p>

plot(tabla_ahorro_ingres$Ahorro,type=\u00bbl\u00bb)
abline(v=12)<\/p>

plot(tabla_ahorro_ingres$Ingreso,type=\u00bbl\u00bb)
abline(v=12)<\/p>


#######<\/p>

#######<\/p>

ML_1 <- lm(tabla_ahorro_ingres$Ahorro[1:12]~tabla_ahorro_ingres$Ingreso[1:12])
summary(ML_1)
SCR_1<-sum(residuals(ML_1)^2)<\/p>

ML_2 <- lm(tabla_ahorro_ingres$Ahorro[13:26]~tabla_ahorro_ingres$Ingreso[13:26])
summary(ML_2)<\/p>

SCR_2<-sum(residuals(ML_2)^2)<\/p>

ML_3 <- lm(tabla_ahorro_ingres$Ahorro~tabla_ahorro_ingres$Ingreso)
summary(ML_3)
SCR_3<-sum(residuals(ML_3)^2)<\/p>

# vemos claramente que la realci\u00f3n entre ahorro y el IPD no es la misma
# en los dos subperiodos<\/p>


#sumamos SCR1 y SCR2<\/p>

SCRrn <- SCR_1 + SCR_2<\/p>

SCRrn<\/p>

## CREACI\u00d3N DEL ESTADISTICO F<\/p>

num_F <- ((SCR_3-SCRrn)\/2)
den_F <- SCRrn\/(12+14-4) #SCRrn\/n1 + n2 \u2013 2*k<\/p>

Valor_F<- num_F\/den_F
#2 Y 22 GL DENOMINADOR
F_al_0.99 <- 5.72 # 2 gl en numerador y 22 denominador<\/p>

## c\u00f3digo para directo desde R sin mirar la tabla<\/p>

ft<- qf(0.99,2,(12+14)-2*2)
ft
#qf(0.99,2,22)<\/p>

p <- df(Valor_F,2,22)*2
p<\/p>


Valor_F
#95.75 valor tabla
# distirbucion F (2 y 22)<\/p>

#Si valor F calculado < valor critico tabla F = NO hay cambio estructural
#si valor F calculado > valor critico tabla F = Hay cambio estructural<\/p>

\u00a0<\/p>

# TEST DE SHOW CON LOS PAQUETES EN R<\/p>

#install.packages(\u00abstrucchange\u00bb)
#install.packages(\u00abRtools\u00bb)
library(strucchange)<\/p>

sctest (Ahorro~Ingreso, type = \u00abChow\u00bb, point = 12 )<\/p>

## Vams a crear nuestra variable dic\u00f3toma o dummy<\/p>

12+14<\/p>

dum_var <- as.matrix(c(rep(0,12),rep(1,14)))<\/p>


# una vez tenemos la varible la introducimos en el modelo de regresi\u00f3n lineal<\/p>

#MODELO REGRESI\u00d3N LINEAL CON DUMMY<\/p>

ML_3 <- lm(tabla_ahorro_ingres$Ahorro~tabla_ahorro_ingres$Ingreso)
summary(ML_3)<\/p>


ML_DUM <- lm(tabla_ahorro_ingres$Ahorro~tabla_ahorro_ingres$Ingreso+dum_var)
summary(ML_DUM)<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Las variables Dic\u00f3tomas , tambi\u00e9n llamadas Dummy en ingl\u00e9s, son aquellas variables de car\u00e1cter cualitativo que podemos a\u00f1adir a modelos de regresi\u00f3n. Por ejemplo, en econom\u00eda variables cualitativas como sexo o raza pueden influir en muchos estudios. De forma que podemos a\u00f1adir a modelos de caracter cuantitativo tambi\u00e9n variables cualitativas. Una manera de \u201ccuantificar\u201d tales …<\/p>\n

Variables Dic\u00f3tomas (Dummy) en Modelos de Regresi\u00f3n<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":19,"featured_media":3232,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[2],"tags":[],"yoast_head":"\nVariables Dic\u00f3tomas (Dummy) en Modelos de Regresi\u00f3n - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ricovictor.com\/index.php\/2022\/06\/08\/variables-dicotomas-dummy-en-modelos-de-regresion\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Variables Dic\u00f3tomas (Dummy) en Modelos de Regresi\u00f3n - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas\" \/>\n<meta property=\"og:description\" content=\"Las variables Dic\u00f3tomas , tambi\u00e9n llamadas Dummy en ingl\u00e9s, son aquellas variables de car\u00e1cter cualitativo que podemos a\u00f1adir a modelos de regresi\u00f3n. Por ejemplo, en econom\u00eda variables cualitativas como sexo o raza pueden influir en muchos estudios. 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