Guia Practica 3; Pruebas de Normalidad

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.4.4

library(readr)

## Warning: package 'readr' was built under R version 3.4.4

ejemp_regresion <- read.csv("D:/GUIA 2 Econometria/ejemplo_regresion.csv",sep = ",")
head(ejemp_regresion,n=6)

##     X1   X2    Y
## 1 3.92 7298 0.75
## 2 3.61 6855 0.71
## 3 3.32 6636 0.66
## 4 3.07 6506 0.61
## 5 3.06 6450 0.70
## 6 3.11 6402 0.72

Estimando el modelo.

library(stargazer)
options(scipen = 9999)
modelo_lineal<-lm(formula = Y~X1+X2,data = ejemp_regresion)
stargazer(modelo_lineal, title= "Ejemplo de Regresion Multiple", type = "text", digist= 8)

## 
## Ejemplo de Regresion Multiple
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                  Y             
## -----------------------------------------------
## X1                           0.237***          
##                               (0.056)          
##                                                
## X2                          -0.0002***         
##                              (0.00003)         
##                                                
## Constant                     1.564***          
##                               (0.079)          
##                                                
## -----------------------------------------------
## Observations                    25             
## R2                             0.865           
## Adjusted R2                    0.853           
## Residual Std. Error       0.053 (df = 22)      
## F Statistic           70.661*** (df = 2; 22)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
## 
## Ejemplo de Regresion Multiple
## =
## 8
## -

Ajuste de los residuos a la distribucion normal.

library(fitdistrplus)

## Warning: package 'fitdistrplus' was built under R version 3.4.4

## Warning: package 'npsurv' was built under R version 3.4.4

## Warning: package 'lsei' was built under R version 3.4.4

library(stargazer)
fit_normal<-fitdist(data = modelo_lineal$residuals, distr= "norm")
plot(fit_normal)

summary(fit_normal)

## Fitting of the distribution ' norm ' by maximum likelihood 
## Parameters : 
##                        estimate  Std. Error
## mean 0.000000000000000007770748 0.010000382
## sd   0.050001911895951975384200 0.007058615
## Loglikelihood:  39.41889   AIC:  -74.83778   BIC:  -72.40002 
## Correlation matrix:
##      mean sd
## mean    1  0
## sd      0  1

Prueba de Normalidad de Jarque - Bera

library(normtest)

## Warning: package 'normtest' was built under R version 3.4.4

jb.norm.test(modelo_lineal$residuals)

## 
##  Jarque-Bera test for normality
## 
## data:  modelo_lineal$residuals
## JB = 0.93032, p-value = 0.484

qqnorm(modelo_lineal$residuals)
qqline(modelo_lineal$residuals)

hist(modelo_lineal$residuals, main = "Histograma de los residuos", xlab = "Residuos", ylab = "Frecuencia")

Comentario

Prueba de Normalidad de Kolmogorov - Smirnov

library(nortest)

## Warning: package 'nortest' was built under R version 3.4.4

lillie.test(modelo_lineal$residuals)

## 
##  Lilliefors (Kolmogorov-Smirnov) normality test
## 
## data:  modelo_lineal$residuals
## D = 0.082345, p-value = 0.9328

qqnorm(modelo_lineal$residuals)
qqline(modelo_lineal$residuals)

hist(modelo_lineal$residuals, main = "Histograma", xlab = "REsiduos", ylab = "Frecuencia")

Prueba de Normalidad de Chapiro - Wilk.

shapiro.test(modelo_lineal$residuals)

## 
##  Shapiro-Wilk normality test
## 
## data:  modelo_lineal$residuals
## W = 0.97001, p-value = 0.6453