Fixed effects estimators of panel models can be severely biased because of the well-known incidental parameters problem. We show that this bias can be reduced by using a panel jackknife or an analytical bias correction motivated by large T. We give bias corrections for averages over the fixed...

We study a nonlinear panel data model in which the fixed effects are assumed to have finite support. The fixed effects estimator is known to have the incidental parameters problem. We contribute to the literature by making a qualitative observation that the incidental parameters problem in this...

We study the asymptotic distribution of three-step estimators of a finite dimensional parameter vector where the second step consists of one or more nonparametric regressions on a regressor that is estimated in the first step. The first step estimator is either parametric or non-parametric....

We establish strict stationarity and strong mixing properties of the dynamic Tobit process. Using these results we show that the regularity conditions for bias corrections in general non-linear dynamic panel models are satisfied for the dynamic Tobit model.

We provide a framework for evaluating and improving multivariate density forecasts. Among other things, the multivariate framework lets us evaluate the adequacy of density forecasts involving cross-variable interactions, such as time-varying conditional correlations. We also provide conditions...

This paper investigates a simple dynamic linear panel regression model with both fixed effects and time effects. Using "large n and large T " asymptotics, we approximate the distribution of the fixed effect estimator of the autoregressive parameter in the dynamic linear panel model and derive...