A regularization approach to common correlated effects estimation

Summary Cross‐section average‐augmented panel regressions introduced by Pesaran (2006) have been a popular empirical tool to estimate panel data models with common factors. However, the corresponding common correlated effects (CCEs) estimator can be sensitive to the number of cross‐section averages used and/or the static factor representation for observables. In this paper, we show that most of the corresponding problems documented in the literature can be solved once cross‐section averages are appropriately regularized, thus extending the applicability of the CCE setup. As the standard plug‐in variance estimators are not able to account for all sources of estimation uncertainty, we suggest the use of cross‐section bootstrap to construct confidence intervals. The proposed procedure is illustrated both using real and simulated data.

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