Hi, This question is regarding white test.
My Regresion is ID~ Utilidades.
When I do the white stadistic mannually
I did the auxiliary regresion
Residuals^2~ Utilidades+Utilidades^2
The White stadistic is n*R^2 = 13.6998
But when I use the function whited.htest the Test stadistic is 25.8605.
Many Thanks in advance for your help.
Here you have the details:
# Update the data
datos<-read.table("tabla 11.5.txt", header=TRUE)
# Create the Regresion.
regresion<-lm(ID~Utilidades, data=datos)
# Create the Regresion.
summary(regresion)
##
## Call:
## lm(formula = ID ~ Utilidades, data = datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6627.6 -1255.6 -256.5 1216.3 5915.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 114.39061 959.03765 0.119 0.906541
## Utilidades 0.36316 0.08915 4.073 0.000884 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2676 on 16 degrees of freedom
## Multiple R-squared: 0.5091, Adjusted R-squared: 0.4784
## F-statistic: 16.59 on 1 and 16 DF, p-value: 0.0008844
# Create the White test Manualy
residuos<-residuals(regresion)
residuos2<-residuos^2
residuos2
# Creating the Auxiliary regresion.
datos$Utilidades2<-(datos$Utilidades)^2
regresion2<-lm(residuos2~datos$Utilidades+datos$Utilidades2)
summary(regresion2)
##
## Call:
## lm(formula = residuos2 ~ datos$Utilidades + datos$Utilidades2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12878603 -3082841 396014 3403670 9796936
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.515e+06 3.063e+06 1.148 0.26913
## datos$Utilidades -1.583e+03 8.065e+02 -1.963 0.06853 .
## datos$Utilidades2 1.355e-01 3.705e-02 3.656 0.00234 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6446000 on 15 degrees of freedom
## Multiple R-squared: 0.7611, Adjusted R-squared: 0.7293
## F-statistic: 23.9 on 2 and 15 DF, p-value: 2.168e-05
R2= 0.7611
n=18
n.R2=n*R2
n.R2
#White.staditic is
## [1] 13.6998
model1<-var(datos)
dataset<-data.frame(residuos2,datos$Utilidades)
model1 <- VAR(dataset, p = 1)
whites.htest(model1)
##
## White's Test for Heteroskedasticity:
## ====================================
##
## No Cross Terms
##
## H0: Homoskedasticity
## H1: Heteroskedasticity
##
## Test Statistic:
## 25.8605
##
## Degrees of Freedom:
## 12
##
## P-value:
## 0.0112