This article reviews recent advances in ﬁxed eﬀect estimation of panel data models for long panels, where the number of time periods is relatively large. We focus on semiparametric models with unobserved individual and time eﬀects, where the distribution of the outcome variable conditional on covariates and unobserved eﬀects is speciﬁed parametrically, while the distribution of the unobserved eﬀects is left unrestricted. Compared to existing reviews on long panels (Arellano & Hahn, 2007; a section in Arellano & Bonhomme, 2011) we discuss models with both individual and time eﬀects, split-panel Jackknife bias corrections, unbalanced panels, distribution and quantile eﬀects, and other extensions. Understanding and correcting the incidental parameter bias caused by the estimation of many ﬁxed eﬀects is our main focus, and the unifying theme is that the order of this bias is given by the simple formula p/n for all models discussed, with p the number of estimated parameters and n the total sample size.
3 October 2017
Working Paper (CWP42/17)