A regularization approach to common correlated effects estimation
提出对横截面平均进行正则化，解决了共同相关效应估计器对横截面平均数量和静态因子表示的敏感性问题，并建议使用横截面自助法构建置信区间。
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.