Common Correlated Effects Estimation of Dynamic Panels with Cross-Sectional Dependence

We study estimation of dynamic panel data models with error cross-sectional dependence generated by an unobserved common factor. We show that for a temporally dependent factor, the standard within groups (WG) estimator is inconsistent even as both N and T tend to infinity. Next we investigate the properties of the common correlated effects pooled (CCEP) estimator of Pesaran [Econometrica, 2006] which eliminates the cross-sectional dependence using cross-sectional averages of the data. In contrast to the static case, the CCEP estimator is only consistent if next to N also T tends to infinity. It is shown that for the most relevant parameter settings, the asymptotic bias of the CCEP estimator is larger than that of the infeasible WG estimator, which includes the common factors as regressors. Restricting the CCEP estimator results in a somewhat smaller asymptotic bias. The small sample proper- ties of the various estimators are analysed using Monte Carlo experiments. The simulation results suggest that the CCEP estimator can be used to estimate dynamic panel data models provided T is not too small. The size of N is of less importance.