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ejercicio autocorrelacion

library(wooldridge)
data(hprice1)
head(force(hprice1),n=5) #mostrar las primeras 5 observaciones

##   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

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)
Modelo_autocorrelacion <- lm(formula = price~lotsize+sqrft+ bdrms, data = hprice1)
stargazer(Modelo_autocorrelacion, title = "Modelo Estimado", type = "text", digits = 5)

## 
## Modelo Estimado
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                price           
## -----------------------------------------------
## lotsize                     0.00207***         
##                              (0.00064)         
##                                                
## sqrft                       0.12278***         
##                              (0.01324)         
##                                                
## bdrms                        13.85252          
##                              (9.01015)         
##                                                
## Constant                     -21.77031         
##                             (29.47504)         
##                                                
## -----------------------------------------------
## Observations                    88             
## R2                            0.67236          
## Adjusted R2                   0.66066          
## Residual Std. Error     59.83348 (df = 84)     
## F Statistic          57.46023*** (df = 3; 84)  
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01

Durbin Watson

library(lmtest)

## Loading required package: zoo

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

dwtest(Modelo_autocorrelacion, alternative = "two.sided",iterations = 1000)

## 
##  Durbin-Watson test
## 
## data:  Modelo_autocorrelacion
## DW = 2.1098, p-value = 0.6218
## alternative hypothesis: true autocorrelation is not 0

#Prueba del Multiplicador de Lagrange

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

library(tidyr)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

Ui <- Modelo_autocorrelacion$residuals
Mat_x <- model.matrix(Modelo_autocorrelacion)
MatInformación <- Mat_x[,-1]  
Endogena <- hprice1$price
cbind(Ui, Endogena, MatInformación) %>%
  as.data.frame()%>%
  mutate(Lag1 = dplyr::lag(Ui, 1),
         Lag2 = dplyr::lag(Ui, 2))%>%
  replace_na(list(Lag1=0,Lag2=0)) -> Data.PruebaBG
#kable(head(x = Data.PruebaBG, n = 7))

  #Regresión auxiliar.
Regresion.AuxiliarBG <- lm(Ui~lotsize+sqrft+bdrms + Lag1 + Lag2, data = Data.PruebaBG)
ResumenBG <- summary(Regresion.AuxiliarBG)

  #Estadístico LMbg.
R2 <- ResumenBG$r.squared
n <- nrow(Data.PruebaBG)
LMbg <- n*R2

    #Grados de libertad.
gl <- 2 #xq se esta verificando autocorrelación de orden dos (Lag1+Lag2+...+Lagn= gl)

    #P Value.
P_Value <- 1-pchisq(q = LMbg, df = gl)

    #Valor Critico.
VC <- qchisq(p = 0.05,df = gl,lower.tail = FALSE)
#VC <- qchisq(p = 0.95,df = gl,lower.tail = TRUE)

    #Resultados.
library(stargazer)
SalidaBG <-c(LMbg, VC,P_Value)
names(SalidaBG) <- c("LMbg", "Valor Crítico","P Value")
stargazer(SalidaBG, title = "Resultado de la prueba de Breusch Godfrey", type = "text", digits = 5)

## 
## Resultado de la prueba de Breusch Godfrey
## =============================
## LMbg    Valor Crítico P Value
## -----------------------------
## 3.03340    5.99146    0.21943
## -----------------------------

primer orden

library(lmtest)
Prueba_Multiplicador_Lagrange <- bgtest(Modelo_autocorrelacion, order = 1)
Prueba_Multiplicador_Lagrange

## 
##  Breusch-Godfrey test for serial correlation of order up to 1
## 
## data:  Modelo_autocorrelacion
## LM test = 0.39362, df = 1, p-value = 0.5304

segundo orden

library(lmtest)
Prueba_Multiplicador_Lagrange <- bgtest(Modelo_autocorrelacion, order = 2)
Prueba_Multiplicador_Lagrange

## 
##  Breusch-Godfrey test for serial correlation of order up to 2
## 
## data:  Modelo_autocorrelacion
## LM test = 3.0334, df = 2, p-value = 0.2194