library(wooldridge)
data(hprice1)
head(force(hprice1))
## price assess bdrms lotsize sqrft colonial lprice lassess llotsize
## 1 300.000 349.1 4 6126 2438 1 5.703783 5.855359 8.720297
## 2 370.000 351.5 3 9903 2076 1 5.913503 5.862210 9.200593
## 3 191.000 217.7 3 5200 1374 0 5.252274 5.383118 8.556414
## 4 195.000 231.8 3 4600 1448 1 5.273000 5.445875 8.433811
## 5 373.000 319.1 4 6095 2514 1 5.921578 5.765504 8.715224
## 6 466.275 414.5 5 8566 2754 1 6.144775 6.027073 9.055556
## lsqrft
## 1 7.798934
## 2 7.638198
## 3 7.225482
## 4 7.277938
## 5 7.829630
## 6 7.920810
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_autocorrelacion<-lm(price = ˆα + ˆα1(lotsize) + ˆα2(sqrft) + ˆα3(bdrms) +ε, data = hprice1)
## Warning: In lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
## extra argument 'price' will be disregarded
stargazer(modelo_autocorrelacion,title = "Modelo para autocorrelacion", type = "text")
##
## Modelo para autocorrelacion
## ===============================================
## Dependent variable:
## ---------------------------
## NA
## -----------------------------------------------
## assess 1.233***
## (0.097)
##
## bdrms 2.652
## (2.118)
##
## lotsize 0.00003
## (0.0003)
##
## sqrft 0.008
## (0.019)
##
## colonial -0.410
## (3.390)
##
## lprice 276.094***
## (10.186)
##
## lassess -396.819***
## (38.182)
##
## llotsize 1.755
## (5.791)
##
## lsqrft -3.954
## (41.502)
##
## Constant 606.372**
## (246.442)
##
## -----------------------------------------------
## Observations 88
## R2 0.986
## Adjusted R2 0.984
## Residual Std. Error 12.921 (df = 78)
## F Statistic 602.196*** (df = 9; 78)
## ===============================================
## Note: *p<0.1; **p<0.05; ***p<0.01
summary(modelo_autocorrelacion)
##
## Call:
## lm(data = hprice1, price = ˆα + ˆα1(lotsize) + ˆα2(sqrft) +
## ˆα3(bdrms) + ε)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.299 -4.117 0.383 4.547 53.462
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.064e+02 2.464e+02 2.461 0.0161 *
## assess 1.233e+00 9.706e-02 12.704 < 2e-16 ***
## bdrms 2.652e+00 2.118e+00 1.252 0.2143
## lotsize 3.452e-05 2.524e-04 0.137 0.8916
## sqrft 8.147e-03 1.859e-02 0.438 0.6624
## colonial -4.100e-01 3.390e+00 -0.121 0.9041
## lprice 2.761e+02 1.019e+01 27.107 < 2e-16 ***
## lassess -3.968e+02 3.818e+01 -10.393 2.23e-16 ***
## llotsize 1.755e+00 5.791e+00 0.303 0.7627
## lsqrft -3.954e+00 4.150e+01 -0.095 0.9243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.92 on 78 degrees of freedom
## Multiple R-squared: 0.9858, Adjusted R-squared: 0.9842
## F-statistic: 602.2 on 9 and 78 DF, p-value: < 2.2e-16
Prueba de Durbin Watson
Usando libreria LMTEST
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 = 1.948, p-value = 0.7947
## alternative hypothesis: true autocorrelation is not 0
Usando libreria Car
library(car)
## Loading required package: carData
durbinWatsonTest(modelo_autocorrelacion, simulate= TRUE, reps=1000)
## lag Autocorrelation D-W Statistic p-value
## 1 0.02406761 1.947959 0.708
## Alternative hypothesis: rho != 0
En ambos casos se rechaza la la presencia de autocorrelacion es
decir que no se rechaza la H0 ya que el P_valor es mayor al nivel de
significancia, P_valor > a 0.05
Prueba del Multiplicador de Lagrange (verifique autocorrelación de
primer y segundo orden).
segundo orden
library(lmtest)
bgtest(modelo_autocorrelacion, order = 2)
##
## Breusch-Godfrey test for serial correlation of order up to 2
##
## data: modelo_autocorrelacion
## LM test = 0.059689, df = 2, p-value = 0.9706
como el p_valor es mayor que el nivel de signoficancia,
0.9706>0.05 no se rechaza H0 por lo que se concucle que los residuos
del modelo no siguen una autocorrelacion de orden 2
Primer orden
library(lmtest)
bgtest(modelo_autocorrelacion, order = 1)
##
## Breusch-Godfrey test for serial correlation of order up to 1
##
## data: modelo_autocorrelacion
## LM test = 0.057869, df = 1, p-value = 0.8099
como el p_valor es mayor que el nivel de signoficancia,
0.8099>0.05 no se rechaza H0 por lo que se concucle que los residuos
del modelo no siguen una autocorrelacion de orden 1