GMM estimation of linear panel data models with time-varying individual effects

Abstract

This paper considers models for panel data in which the individual effects vary over time. The temporal pattern of variation is arbitrary, but it is the same for all individuals. The model thus allows one to control for time-varying unobservables that are faced by all individuals (e.g., macro-economic events) and to which individuals may respond differently. A generalized within estimator is consistent under strong assumptions on the errors, but it is dominated by a generalized method of moments estimator. This is perhaps surprising, because the generalized within estimator is the MLE under normality. The efficiency gains from imposing second-moment error assumptions are evaluated; they are substantial when the regressors and effects are weakly correlated.