CARGAMOS LOS DATOS

library(wooldridge)
library(gt)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
data(hprice1)
hprice1 %>% 
  head(5)%>%
  gt() %>%
  tab_header("Evidencia empírica para estimar el modelo.")
Evidencia empírica para estimar el modelo.
price assess bdrms lotsize sqrft colonial lprice lassess llotsize lsqrft
300 349.1 4 6126 2438 1 5.703783 5.855359 8.720297 7.798934
370 351.5 3 9903 2076 1 5.913503 5.862210 9.200593 7.638198
191 217.7 3 5200 1374 0 5.252274 5.383118 8.556414 7.225482
195 231.8 3 4600 1448 1 5.273000 5.445875 8.433811 7.277938
373 319.1 4 6095 2514 1 5.921578 5.765504 8.715224 7.829630

1. ESTIMACION DEL 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
options(scipen = 9999)
ModeloHeterocedasticidad <- lm(formula = price~lotsize+sqrft+ bdrms, data = hprice1)
stargazer(ModeloHeterocedasticidad, title = "Modelo Estimado", type = "text", digits = 7)
## 
## Modelo Estimado
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                price           
## -----------------------------------------------
## lotsize                    0.0020677***        
##                             (0.0006421)        
##                                                
## sqrft                      0.1227782***        
##                             (0.0132374)        
##                                                
## bdrms                       13.8525200         
##                             (9.0101450)        
##                                                
## Constant                    -21.7703100        
##                            (29.4750400)        
##                                                
## -----------------------------------------------
## Observations                    88             
## R2                           0.6723622         
## Adjusted R2                  0.6606609         
## Residual Std. Error    59.8334800 (df = 84)    
## F Statistic         57.4602300*** (df = 3; 84) 
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
library(stargazer)
#CALCULO MANUAL.
Ui <- ModeloHeterocedasticidad$residuals
Mat_X <- model.matrix(ModeloHeterocedasticidad)
MatrizX <- Mat_X[,-1]
VectorY <- hprice1$price
Data.PruebaWhite <- as.data.frame(cbind(Ui,VectorY, MatrizX ))
RegresionAuxiliar <- lm(formula = I(Ui^2)~lotsize+sqrft+bdrms+I(lotsize^2) + I(sqrft^2) + I(bdrms^2) + lotsize*sqrft+lotsize*bdrms+ sqrft*bdrms, data = Data.PruebaWhite)
sumario <- summary(RegresionAuxiliar)
n <- nrow(Data.PruebaWhite)
R2 <- sumario$r.squared
LMw <- n*R2
Gl <- 3+3+3
P.Value <- 1-pchisq(q = LMw, df = Gl)
Vc <- qchisq(p = 0.95, df = Gl)
Salida_Whitw <- c(LMw, Vc, P.Value)
names(Salida_Whitw) <- c("LMw", "Valor Critico", "P Value")
stargazer(Salida_Whitw, title = "Resultado de la prueba de White", type = "text", digits = 7)
## 
## Resultado de la prueba de White
## ==================================
## LMw        Valor Critico  P Value 
## ----------------------------------
## 33.7316600  16.9189800   0.0000995
## ----------------------------------

PRUEBA WHITW VERIFICAR SI SU VARIANZA RESIDUAL ES HOMOCEDASTICA.

PRUEBA WHITE “LIBRERIA lmtest”

library(lmtest)
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
options(scipen = 999999)
PruebaWhite <- bptest(ModeloHeterocedasticidad, ~I(lotsize^2) + I(sqrft^2) + I(bdrms^2) + lotsize*sqrft+lotsize*bdrms+ sqrft*bdrms, data = hprice1)
print(PruebaWhite)
## 
##  studentized Breusch-Pagan test
## 
## data:  ModeloHeterocedasticidad
## BP = 33.732, df = 9, p-value = 0.00009953

P-value es manor o igual a 0.05 se rechaza la hipotesis nula por lo tanto hay evidencia de que por lo menos hay un coeficiente distinto de cero.

RESULTADOS DE FORMA GRAFICA

library(fastGraph)
PW <- PruebaWhite$statistic
gl <- PruebaWhite$parameter
VC <- qchisq(p = 0.05, df = gl,lower.tail = FALSE)
shadeDist(PW, ddist = "dchisq", 
          parm1 = gl, 
          lower.tail = FALSE, xmin = 0, 
          sub= paste("VC:", round(VC, digits = 3),"  "," ",
                     "LMw:", round(PW,digits = 3)), 
          main = "Prueba de White")