This paper presents a simple approach to deal with sample selection in models with multiplicative errors. Models for non-negative limited dependent variables such as counts fit this framework. The approach builds on a specification of the conditional mean of the outcome only and is, therefore,...

Empirical models for panel data frequently feature fixed effects in both directions of the panel. Settings where this is prevalent include student-teacher interaction, the allocation of workers to firms, and the import-export flows between countries. Estimation of such fixed-effect models is...

Empirical models for panel data frequently feature fixed effects in both directions of the panel. Settings where this is prevalent include student-teacher interaction, the allocation of workers to firms, and the import-export flows between countries. Estimation of such fixed-effect models is...

This note discusses a class of models for panel data that accommodate between‐group heterogeneity that is allowed to exhibit positive within‐group variance. Such a set‐up generalizes the traditional fixed‐effect paradigm in which between‐group heterogeneity is limited to univariate...

A constructive proof of identification of multilinear decompositions of multiway arrays is presented. It can be applied to show identification in a variety of multivariate latent structures. Examples are finite-mixture models and hidden Markov models. The key step to show identification is the...

A constructive proof of identification of multilinear decompositions of multiway arrays is presented. It can be applied to show identification in a variety of multivariate latent structures. Examples are finite-mixture models and hidden Markov models. The key step to show identification is the...

We propose a jackknife for reducing the order of the bias of maximum likelihood estimates of nonlinear dynamic fixed-effect panel models.

Maximum-likelihood estimates of nonlinear panel data models with fixed effects are generally not consistent as the number of units, N, grows large while the number of time periods, T, stays fixed. The inconsistency can be viewed as a consequence of the bias of the score function, where the...

Maximum-likelihood estimation of nonlinear models with fixed effects is subject to the incidental-parameter problem. This typically implies that point estimates suffer from large bias and confidence intervals have poor coverage. This paper presents a jackknife method to reduce this bias and to...

We calculate the bias of the profile score for the regression coefficients in a multistratum autoregressive model with stratum-specific intercepts. The bias is free of incidental parameters. Centering the profile score delivers an unbiased estimating equation and, upon integration, an adjusted...