Count Data Models with Correlated Unobserved Heterogeneity

Count Data Models with Correlated Unobserved Heterogeneity

As previously argued, the correlation between included and omitted regressors generally causes inconsistency of standard estimators for count data models. Non-linear instrumental variables estimation of an exponential model under conditional moment restrictions is one of the proposed remedies. This approach is extended here by fully exploiting the model assumptions and thereby improving efficie...

Nonparametric Discrete Choice Models with Unobserved Heterogeneity

In this research, we provide a new method to estimate discrete choice models with unobserved heterogeneity that can be used with either cross-sectional or panel data. The method imposes nonparametric assumptions on the systematic subutility functions and on the distributions of the unobservable random vectors and the heterogeneity parameter. The estimators are computationally feasible and stron...

Observing Unobserved Heterogeneity: Using Process Data to Enhance Choice Models

Flexible Correlated Random Effects Estimation in Panel Models with Unobserved Heterogeneity

In this paper, we consider identification in a correlated random effects model for panel data. We assume that the likelihood for each individual in the panel is known up to a finite dimensional common parameter and an individual specific parameter. We allow the distribution of unobserved individual specific effects to depend on observed explanatory variables and make no assumptions about the pa...

Unobserved heterogeneity in panel time series models

Recently, the large T panel literature has emphasized unobserved, time-varying heterogeneity that may stem from omitted common variables or global shocks that a¤ect each individual unit di¤erently. These latent common factors induce cross-section dependence and may lead to inconsistent regression coe¢ cient estimates if they are correlated with the explanatory variables. Moreover, if the proces...