Ejercicio Detección de Autocorrelación

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

modelo <- lm(price ~ lotsize + sqrft + bdrms, data = hprice1)

a) Prueba de 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,alternative = "two.sided",iterations = 1000)

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

b) Prueba del Multiplicador de Lagrange (verifique autocorrelación de primer y segundo orden).

bgtest(modelo, order = 1)

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

bgtest(modelo, order = 2)

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