The purpose of this paper is to review recently development 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 the explain how to construct analytical bias...

We discuss a modified objective function strategy to obtain estimators without bias to order 1/T in nonlinear dynamic panel models with multiple effects. Estimation proceeds from a bias corrected objective function relative to some target infeasible criterion. We consider a determinant based...

The purpose of this paper is to formulate procedures for the analysis of the time series behaviour of micro panel data subject to censoring. We assume an autoregressive model with random effects for a latent variable which is only partly observed due to a selection mechanism. Our methods are...

This chapter focuses on two of the developments in panel data econometrics since the Handbook chapter by Chamberlain (1984). The first objective of this chapter is to provide a review of linear panel data models with predetermined variables. We discuss the implications of assuming that...

This paper reviews the existing approaches to deal with panel data binary choice models with individual effects. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and the paper points to several aspects that deserve further...

In this paper we study the effects of unemployment benefit duration and the business cycle on unemployment duration. We construct durations for individuals entering unemployment from a longitudinal sample of Spanish men in 1987-1994. Estimated discrete hazard models indicate that receipt of...

In this paper we derive the asymptotic properties of within groups (WG), GMM and LIML estimators for an autoregressive model with random effects when both T and N tend to infinity. GMM and LIML are consistent and asymptotically equivalent to the WG estimator. When T/N-0 the fixed T results for...

Many approaches to estimation of panel models are based on an average or integrated likelihood that assigns weights to different values of the individual effects. Fixed effects, random effects, and Bayesian approaches all fall in this category. We provide a characterization of the class of...

We develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity. Bias corrected conditional score estimators, random effects maximum likelihood (RML) in levels and first differences, and estimators that impose mean...

Two-step instrumental variable estimators for dynamic panel data models are considered that are asymptotically efficient under some auxiliary assumptions, but remain consistent when the assumptions are violated. Asymptotic efficiency is defined in relation to the information bound for the...