Understanding Bias in Nonlinear Panel Models: Some Recent Developments

The purpose of this paper is to review recently developed methods of estimation of nonlinear fixed effects panel data models with reduced bias properties. We begin by describing fixed effects estimators and the incidental parameters problem. Next we explain how to construct analytical bias correction of estimators, followed by bias correction of the moment equation, and bias corrections for the concentrated likelihood. We then turn to discuss other approaches leading to bias correction based on orthogonalization and their extensions. The remaining sections consider quasi maximum likelihood estimation for dynamic models, the estimation of marginal effects, and automatic methods based on simulation.