Test of serial correlation for (the idiosyncratic component of) the errors in panel models.
pdwtest(x, ...)
# S3 method for class 'panelmodel'
pdwtest(x, ...)
# S3 method for class 'formula'
pdwtest(x, data, ...)an object of class "panelmodel" or of class
"formula",
further arguments to be passed on to dwtest,
e.g., alternative, see lmtest::dwtest() for
further details.
a data.frame,
An object of class "htest".
This Durbin–Watson test uses the auxiliary model on
(quasi-)demeaned data taken from a model of class plm which may
be a pooling (the default), random or within model. It
performs a Durbin–Watson test (using dwtest from package
lmtest on the residuals of the (quasi-)demeaned model,
which should be serially uncorrelated under the null of no serial
correlation in idiosyncratic errors. The function takes the
demeaned data, estimates the model and calls dwtest. Thus, this
test does not take the panel structure of the residuals into
consideration; it shall not be confused with the generalized
Durbin-Watson test for panels in pbnftest.
Durbin J, Watson GS (1950). “Testing for Serial Correlation in Least Squares Regression: I.” Biometrika, 37(3/4), 409–428. ISSN 00063444.
Durbin J, Watson GS (1951). “Testing for serial correlation in least squares regression. II.” Biometrika, 38(1-2), 159-178. ISSN 0006-3444, doi:10.1093/biomet/38.1-2.159 .
Durbin J, Watson GS (1971). “Testing for Serial Correlation in Least Squares Regression. III.” Biometrika, 58(1), 1–19. ISSN 00063444.
Wooldridge JM (2002). Econometric Analysis of Cross–Section and Panel Data. MIT Press.
Wooldridge JM (2010). Econometric Analysis of Cross–Section and Panel Data, 2nd edition. MIT Press.
lmtest::dwtest() for the Durbin–Watson test
in lmtest, pbgtest() for the analogous
Breusch–Godfrey test for panel models,
lmtest::bgtest() for the Breusch–Godfrey test for
serial correlation in the linear model. pbltest(),
pbsytest(), pwartest() and
pwfdtest() for other serial correlation tests for
panel models.
For the Durbin-Watson test generalized to panel data models see
pbnftest().
data("Grunfeld", package = "plm")
g <- plm(inv ~ value + capital, data = Grunfeld, model="random")
pdwtest(g)
#>
#> Durbin-Watson test for serial correlation in panel models
#>
#> data: inv ~ value + capital
#> DW = 0.99636, p-value = 2.819e-13
#> alternative hypothesis: serial correlation in idiosyncratic errors
#>
pdwtest(g, alternative="two.sided")
#>
#> Durbin-Watson test for serial correlation in panel models
#>
#> data: inv ~ value + capital
#> DW = 0.99636, p-value = 5.638e-13
#> alternative hypothesis: serial correlation in idiosyncratic errors
#>
## formula interface
pdwtest(inv ~ value + capital, data=Grunfeld, model="random")
#>
#> Durbin-Watson test for serial correlation in panel models
#>
#> data: inv ~ value + capital
#> DW = 0.99636, p-value = 2.819e-13
#> alternative hypothesis: serial correlation in idiosyncratic errors
#>