A heteroskedasticity robust Breusch-Pagan test for contemporaneous correlation in dynamic panel data models

This paper proposes a heteroskedasticity-robust Breusch-Pagan test of the null hypothesis of zero cross-section (or contemporaneous) correlation in linear panel data models. The procedure allows for either xed, strictly exogenous and/or lagged de- pendent regressor variables, as well as quite general forms of both non-normality and heteroskedasticity in the error distribution. Whilst the asymptotic validity of the test procedure, under the null, is predicated on the number of time series observations, T, being large relative to the number of cross-section units, N, independence of the cross-sections is not assumed. Across a variety of experimental designs, a Monte Carlo study suggests that, in general (but not always), the predictions from asymptotic the- ory provide a good guide to the finite sample behaviour of the test. In particular, with skewed errors and/or when N=T is not small, discrepancies can occur. However, for all the experimental designs, any one of three asymptotically valid wild bootstrap approximations (that are considered in this paper) gives very close agreement between the nominal and empirical signi cance levels of the test. Moreover, in comparison with wild bootstrap version of the original Breusch-Pagan test (Godfrey and Yamagata, 2011) the corresponding version of the heteroskedasticity-robust Breusch-Pagan test is more reliable. As an illustration, the proposed tests are applied to a dynamic growth model for a panel of 20 OECD countries.