Bias-Corrected Common Correlated Effects Pooled Estimation In Homogeneous Dynamic Panels

This paper extends the Common Correlated Effects Pooled (CCEP) estimator designed by Pesaran (2006) to dynamic homogeneous models. For static panels, this estimator is consistent as the number of cross-sections (N) goes to infinity irrespectively of the time series dimension (T). However, it suffers from a large bias in dynamic models when T is fixed (Everaert and De Groote, 2016). We develop a bias-corrected CCEP estimator based on an asymptotic bias expression that is valid for a multi-factor error structure provided that a sufficient number of cross-sectional averages, and lags thereof, are added to the model. We show that the resulting CCEPbc estimator is consistent as N tends to infinity, both for T fixed or T growing large, and derive its limiting distribution. Monte Carlo experiments show that our bias correction performs very well. It is nearly unbiased, even when T and/or N are small, and hence offers a strong improvement over the severely biased CCEP estimator. CCEPbc is also found to be superior to alternative bias correction methods available in the literature in terms of bias, variance and inference.