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 7.829630Estime el siguiente modelo:
price = ˆα + ˆα1(lotsize) + ˆα2(sqrft) + ˆα3(bdrms) + E
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=stargazerModelo.precio<-lm(formula = price~lotsize+sqrft+bdrms,data=hprice1)
stargazer(Modelo.precio,title = "Modelo del Precio", type = "html",digits = 4)| Dependent variable: | |
| price | |
| lotsize | 0.0021*** |
| (0.0006) | |
| sqrft | 0.1228*** |
| (0.0132) | |
| bdrms | 13.8525 |
| (9.0101) | |
| Constant | -21.7703 |
| (29.4750) | |
| Observations | 88 |
| R2 | 0.6724 |
| Adjusted R2 | 0.6607 |
| Residual Std. Error | 59.8335 (df = 84) |
| F Statistic | 57.4602*** (df = 3; 84) |
| Note: | p<0.1; p<0.05; p<0.01 |
library(lmtest)## Warning: package 'lmtest' was built under R version 4.2.3## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.2.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericdwtest(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-Rechazar H0 sí P Value ≤ α
No se rechaza la hipotesis H0, ya que el p value (0.62) es mayor que el nivel de signifciacia (0.05), por lo tanto, se concluye que no existe autocorrelación entre las variables.
Usando el estadistico Durbin-Watson
Para las 3 variables explicativas que se tomaron y las 88
observaciones que posee la prueba (las cuales se redondean a 90), se
obtiene que dl=1.589 y du=1.726.
NO se rechaza H0, debido a que se encuentra dentro de la zona de no rechazo y a la vez, está entre los valores dl (1.589) y 4-dl (2.411), lo que nos indica que NO hay presencia de autocorrelación.
library(car)## Loading required package: carDatadurbinWatsonTest(Modelo.precio,simulate = TRUE,reps = 1000)## lag Autocorrelation D-W Statistic p-value
## 1 -0.05900522 2.109796 0.608
## Alternative hypothesis: rho != 0Autocorrelación de Orden Superior (Orden “m”)
library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## 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, unionlibrary(tidyr)## Warning: package 'tidyr' was built under R version 4.2.3library(stargazer)
library(kableExtra)## Warning: package 'kableExtra' was built under R version 4.2.3##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowsresid<-Modelo.precio$residuals
cbind(resid,hprice1) %>% as.data.frame() %>% mutate(lag_1=dplyr::lag(resid,1),
lag_2=dplyr::lag(resid,2)) %>%
replace_na(list(lag_1=0,lag_2=0)) -> data_prueba_BG
kable(head(data_prueba_BG,n=6))| resid | price | assess | bdrms | lotsize | sqrft | colonial | lprice | lassess | llotsize | lsqrft | lag_1 | lag_2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| -45.639765 | 300.000 | 349.1 | 4 | 6126 | 2438 | 1 | 5.703783 | 5.855359 | 8.720297 | 7.798934 | 0.000000 | 0.000000 |
| 74.848732 | 370.000 | 351.5 | 3 | 9903 | 2076 | 1 | 5.913503 | 5.862210 | 9.200593 | 7.638198 | -45.639765 | 0.000000 |
| -8.236558 | 191.000 | 217.7 | 3 | 5200 | 1374 | 0 | 5.252274 | 5.383118 | 8.556414 | 7.225481 | 74.848732 | -45.639765 |
| -12.081520 | 195.000 | 231.8 | 3 | 4600 | 1448 | 1 | 5.273000 | 5.445875 | 8.433811 | 7.277938 | -8.236558 | 74.848732 |
| 18.093192 | 373.000 | 319.1 | 4 | 6095 | 2514 | 1 | 5.921578 | 5.765504 | 8.715224 | 7.829630 | -12.081520 | -8.236558 |
| 62.939597 | 466.275 | 414.5 | 5 | 8566 | 2754 | 1 | 6.144775 | 6.027073 | 9.055556 | 7.920810 | 18.093192 | -12.081520 |
reg.aux.bg<-lm(resid~lotsize+sqrft+bdrms+lag_1+lag_2,data = data_prueba_BG)
resumen.BG<-summary(reg.aux.bg)
n<-nrow(data_prueba_BG)
gl<-2
R.2.BG<-resumen.BG$r.squared
LM.BG<-n*R.2.BG
VC<-qchisq(p=0.95,df=gl)
p.value.bg<-1-pchisq(q = LM.BG,df=gl)
salida.BG<-c(LM.BG,VC,p.value.bg)
names(salida.BG)<-c("LM BG", "Valor Crítico", "Valor P")
stargazer(salida.BG, title = "Prueba de BG",type = "html", digits = 6)| LM BG | Valor Crítico | Valor P |
| 3.033403 | 5.991465 | 0.219435 |
-Rechazar H0 sí P Value ≤ α
No se rechaza la hipotesis H0, debido a que p value (0.21) es mayor que el nivel de significacia (0.05) en la condición de primer orden, por lo tanto, se concluye que no existe autocorrelación entre las variables.
library(lmtest)
bgtest(Modelo.precio,order = 1)##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: Modelo.precio
## LM test = 0.39362, df = 1, p-value = 0.5304-Rechazar H0 sí P Value ≤ α
No se rechaza la hipotesis H0, debido a que p value (0.53) es mayor que el nivel de significacia (0.05) en la condición de primer orden, por lo tanto, se concluye que no existe autocorrelación entre las variables.
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-Rechazar H0 sí P Value ≤ α
No se rechaza la hipotesis H0, debido a que p value (0.21) es mayor que el nivel de significacia (0.05) en la condición de segundo orden, por lo tanto, se concluye que no existe autocorrelación entre las variables.