Robust Linear Static Panel Data Models Using ε-Contamination - Badi H. Baltagi; Georges Bresson; Anoop Chaturvedi; Guy Lacroix (Institute for the Study of Labor (IZA), 2014)
The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coefficients and individual effects. The ML-II posterior densities are weighted averages of the Bayes estimator under a base prior and the data-dependent empirical Bayes estimator. Two-stage and three stage hierarchy estimators are developed and their finite sample performance is investigated through a series of Monte Carlo experiments. These include standard random effects as well as Mundlak-type, Chamberlain-type and Hausman-Taylor-type models. The simulation results underscore the relatively good performance of the three-stage hierarchy estimator. Within a single theoretical framework, our Bayesian approach encompasses a variety of specifications while conventional methods require separate estimators for each case. We illustrate the performance of our estimator relative to classic panel estimators using data on earnings and crime.
Keywords: C11; C23; C26; 330; ε-contamination; hyper g-priors; type-II maximum likelihood posterior density; panel data; robust Bayesian estimator; three-stage hierarchy