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...

We develop methods for testing the hypothesis that an econometric model is undeerindentified and inferring the nature of the failed identification. By adopting a generalized-method-of moments perspective, we feature directly the structural relations and we allow for nonlinearity in the...

We study the identification of panel models with linear individual-specific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a trade-off between...

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...

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...