A simple reformulation of the common correlated effects model
在固定T面板数据设定下研究因子模型估计，大幅放宽了Pesaran（2006）提出的共同相关效应假设，简化了渐近分析，并给出了考虑解释变量样本均值抽样变异的渐近正态性结果，对实证研究者有参考价值。
.We study estimation of factor models in a fixed-T panel data setting and significantly relax the common correlated effects (CCE) assumptions pioneered by Pesaran (2006) and used in many papers since. This allows us to make three important contributions. First, our assumptions greatly extend the applicability of the CCEP estimator and allow it to be generalized in empirically relevant ways. Second, our assumptions considerably simplify the asymptotic analysis of the estimators. Third, we provide a straightforward asymptotic normality result that accounts for the sampling variability of the sample means of the explanatory variables, which is important in the fixed-T setting.