Adjustment and unobserved heterogeneity in dynamic stochastic frontier models

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...

Stochastic Frontier Models

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...

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...

Endogeneity in Stochastic Frontier Models

Stochastic frontier models are typically estimated by maximum likelihood (MLE) orcorrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that ...