plm (version 1.2-0)

pbsytest: Bera, Sosa-Escudero and Yoon Locally--Robust Lagrange Mulitiplier Tests for Panel Models

Description

Test for residual serial correlation (or individual random effects) locally robust vs. individual random effects (serial correlation) for panel models and joint test by Baltagi and Li.

Usage

pbsytest(x,...)
## S3 method for class 'panelmodel':
pbsytest(x, test = c("ar","re","j"), ...)
## S3 method for class 'formula':
pbsytest(x, data, ..., test = c("ar","re","j"))

Arguments

x

an object of class "formula" or of class panelmodel,

data

a data.frame,

test

a character string indicating which test to perform : first-order serial correlation (ar), random effects (re) or joint test for either of them (j).

...

further arguments.

Value

• An object of class "htest".

Details

These Lagrange multiplier tests are robust vs. local misspecification of the alternative hypothesis, i.e. they test the null of serially uncorrelated residuals against AR(1) residuals in a pooling model, allowing for local departures from the assumption of no random effects; or they test the null of no random effects allowing for local departures from the assumption of no serial correlation in residuals. They use only the residuals of the pooled OLS model and correct for local misspecification as outlined in Bera et al. (2001). The joint test is due to Baltagi and Li (1991) and is added for convenience under this same interface.

References

Bera, A.K., Sosa-Escudero, W. and Yoon, M. (2001), Tests for the error component model in the presence of local misspecification, Journal of Econometrics, 101, pp. 1-23. Baltagi, B. and Li, Q. (1991), A joint test for serial correlation and random individual effects, Statistics and Probability Letters, 11, pp. 277-280.

See Also

plmtest for individual and/or time random effects tests based on a correctly specified model; pbltest, pbgtest and pdwtest for serial correlation tests in random effects models.

Examples

## Example in Bera et al.
data(Grunfeld, package="plm")
## Bera et al. use a subset of the original Grunfeld data,
## so results are slightly different here
## default is AR testing
pbsytest(inv ~ value + capital, data=Grunfeld, index = c("firm","year"))
pbsytest(inv ~ value + capital, data=Grunfeld, test="re", index = c("firm","year"))
pbsytest(inv ~ value + capital, data=Grunfeld, test="j", index = c("firm","year"))