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
library(wooldridge)
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_precio<-lm(formula = price~lotsize+sqrft+bdrms, data=hprice1)
stargazer(modelo_precio,title = "modelo precio",type="text")
## 
## modelo precio
## ===============================================
##                         Dependent variable:    
##                     ---------------------------
##                                price           
## -----------------------------------------------
## lotsize                      0.002***          
##                               (0.001)          
##                                                
## sqrft                        0.123***          
##                               (0.013)          
##                                                
## bdrms                         13.853           
##                               (9.010)          
##                                                
## Constant                      -21.770          
##                              (29.475)          
##                                                
## -----------------------------------------------
## Observations                    88             
## R2                             0.672           
## Adjusted R2                    0.661           
## Residual Std. Error      59.833 (df = 84)      
## F Statistic           57.460*** (df = 3; 84)   
## ===============================================
## Note:               *p<0.1; **p<0.05; ***p<0.01
library(car)
## Loading required package: carData
durbinWatsonTest(modelo_precio,simulate=TRUE,reps=1000)
##  lag Autocorrelation D-W Statistic p-value
##    1     -0.05900522      2.109796   0.574
##  Alternative hypothesis: rho != 0
library(lmtest)
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
dwtest(modelo_precio,alternative = "two.sided",iterations = 1000)
## 
##  Durbin-Watson test
## 
## data:  modelo_precio
## DW = 2.1098, p-value = 0.6218
## alternative hypothesis: true autocorrelation is not 0

#No se rechaza la hipotesis nula, por lo que se descarta la presencia de autocorrelación

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:car':
## 
##     recode
## 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
library(stargazer)
residuos<-modelo_precio$residuals
cbind(residuos,hprice1)%>%
  as.data.frame()%>%
  mutate(Lag_1=dplyr::lag(residuos,1),
         Lag_2=dplyr::lag(residuos,2))%>%
  replace_na(list(Lag_1=0,Lag_2=0))->data_prueba_BG

regresion_auxiliar_BG<-lm(residuos~lotsize+sqrft+bdrms+Lag_1+Lag_2,data=data_prueba_BG)
sumario_BG<-summary(regresion_auxiliar_BG)
R_2_BG<-sumario_BG$r.squared
n<-nrow(data_prueba_BG)
gl<-2
LM_BG<-n*R_2_BG
p_value<-1-pchisq(q=LM_BG,df=gl)
VC<-qchisq(p=0.95,df=gl)
salida_bg<-c(LM_BG,VC,p_value)
names(salida_bg)<-c("LMbg","Valor Critico","p value")
stargazer(salida_bg, title = "Resultados de la prueba Breusch Godfrey",digits=6,type = "text")
## 
## Resultados de la prueba Breusch Godfrey
## ===============================
## LMbg     Valor Critico p value 
## -------------------------------
## 3.033403   5.991465    0.219435
## -------------------------------
library(lmtest)
bgtest(modelo_precio,order=2)
## 
##  Breusch-Godfrey test for serial correlation of order up to 2
## 
## data:  modelo_precio
## LM test = 3.0334, df = 2, p-value = 0.2194

#No se rechaza la hipótesis nula por lo que los residuos del modelo no siguen autocorrelación de orden 2

library(lmtest)
bgtest(modelo_precio,order=2)
## 
##  Breusch-Godfrey test for serial correlation of order up to 2
## 
## data:  modelo_precio
## LM test = 3.0334, df = 2, p-value = 0.2194

#No se rechaza la hipótesis nula por lo que los residuos del modelo no siguen autocorrelación de orden 1