library(wooldridge)
data(hprice1)
head(force(hprice1),n=5)
## price assess bdrms lotsize sqrft colonial lprice lassess llotsize lsqrft
## 1 300 349.1 4 6126 2438 1 5.703783 5.855359 8.720297 7.798934
## 2 370 351.5 3 9903 2076 1 5.913503 5.862210 9.200593 7.638198
## 3 191 217.7 3 5200 1374 0 5.252274 5.383118 8.556414 7.225482
## 4 195 231.8 3 4600 1448 1 5.273000 5.445875 8.433811 7.277938
## 5 373 319.1 4 6095 2514 1 5.921578 5.765504 8.715224 7.829630
Estimar el modelo
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
Modelo_lineal<- lm(price~lotsize+sqrft+bdrms, data = hprice1)
stargazer(Modelo_lineal,tittle = "Modelo Estimado",type = "html")
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Dependent variable:
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price
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lotsize
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0.002***
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(0.001)
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sqrft
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0.123***
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(0.013)
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bdrms
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13.853
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(9.010)
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Constant
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-21.770
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(29.475)
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Observations
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88
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R2
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0.672
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Adjusted R2
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0.661
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Residual Std. Error
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59.833 (df = 84)
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F Statistic
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57.460*** (df = 3; 84)
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Note:
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p<0.1; p<0.05;
p<0.01
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Calculo manual
library(stargazer)
Residuales<-Modelo_lineal$residuals
Data_Prueba_White<- as.data.frame(cbind(Residuales,hprice1))
Regresion_auxiliar<-lm(I(Residuales^2)~lotsize+sqrft+bdrms+I(lotsize^2)+I(sqrft^2)+I(bdrms^2)+lotsize*sqrft+lotsize*bdrms+sqrft*bdrms,data = Data_Prueba_White)
sumario<-summary(Regresion_auxiliar)
n<-nrow(Data_Prueba_White)
R_2<-sumario$r.squared
LM_w<-n*R_2
gl=3+3+3
p_value<-1-pchisq(q=LM_w,df=gl)
VC<-qchisq(p=0.95,df=gl)
salida_white<-c(LM_w,VC,p_value)
names(salida_white)<-c("LMw","Valor critico","P value")
stargazer(salida_white,title = "Resultados de la prueba de White",type = "html", digits = 8)
Resultados de la prueba de White
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LMw
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Valor critico
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P value
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33.73166000
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16.91898000
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0.00009953
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Como 0.00009953<0.05 Se rechaza la H0, por lo tanto hay evidencia
de que la varianza de los residuos es heterocedastica
Prueba de White usando la libreria “lmtest”
library(lmtest)
## Loading required package: zoo
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## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
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## as.Date, as.Date.numeric
prueba_white<- bptest(Modelo_lineal,~I(lotsize^2)+I(sqrft^2)+I(bdrms^2)+lotsize*sqrft+lotsize*bdrms+sqrft*bdrms, data = Data_Prueba_White)
print(prueba_white)
##
## studentized Breusch-Pagan test
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## data: Modelo_lineal
## BP = 33.732, df = 9, p-value = 9.953e-05
Grafica
library(fastGraph)
gl<-3+3+3
vc<-qchisq(p=0.95,df=gl)
shadeDist(xshade = prueba_white$statistic,
ddist = "dchisq",
parm1 = vc,
lower.tail = FALSE, col = c("black","orange"))
Como LMw>VC se rechaza la H0, por lo tanto hay evidencia de que
la varianza de los residuos es heterocedastica