Tests for AR(1) disturbances in panel models.
pbnftest(x, ...) ## S3 method for class 'panelmodel' pbnftest(x, test = c("bnf", "lbi"), ...) ## S3 method for class 'formula' pbnftest( x, data, test = c("bnf", "lbi"), model = c("pooling", "within", "random"), ... )
an object of class
only relevant for formula interface: further arguments
to specify the model to test (arguments passed on to plm()),
a character indicating the test to be performed, either
a character indicating on which type of model the test
shall be performed (
test = "bnf", gives the (modified) BNF statistic,
the generalised Durbin-Watson statistic for panels. For balanced
and consecutive panels, the reference is
Bhargava/Franzini/Narendranathan (1982). The modified BNF is given
for unbalanced and/or non-consecutive panels (d1 in formula 16 of
test = "lbi" yields Baltagi–Wu's LBI statistic
\insertCiteBALT:WU:99plm, the locally best invariant test which
is based on the modified BNF statistic.
No specific variants of these tests are available for random effect models. As the within estimator is consistent also under the random effects assumptions, the test for random effect models is performed by taking the within residuals.
No p-values are given for the statistics as their distribution is quite difficult. \insertCiteBHAR:FRAN:NARE:82;textualplm supply tabulated bounds for p = 0.05 for the balanced case and consecutive case.
For large N, \insertCiteBHAR:FRAN:NARE:82plm suggest it is sufficient to check whether the BNF statistic is < 2 to test against positive serial correlation.
An object of class
pdwtest() for the original Durbin–Watson test using
(quasi-)demeaned residuals of the panel model without taking
the panel structure into account.
pwfdtest() for other serial correlation
tests for panel models.
data("Grunfeld", package = "plm") # formula interface, replicate Baltagi/Wu (1999), table 1, test case A: data_A <- Grunfeld[!Grunfeld[["year"]] %in% c("1943", "1944"), ] pbnftest(inv ~ value + capital, data = data_A, model = "within") pbnftest(inv ~ value + capital, data = data_A, test = "lbi", model = "within") # replicate Baltagi (2013), p. 101, table 5.1: re <- plm(inv ~ value + capital, data = Grunfeld, model = "random") pbnftest(re) pbnftest(re, test = "lbi")
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