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 coeffcients 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.
Baltagi : Department of Economics and Center for Policy Research, Syracuse University, Syracuse, New York, U.S.A. and Department of Economics, Leicester University, Leicester, UK - email@example.com
Bresson: Université Paris II / Sorbonne Universités, France - firstname.lastname@example.org
Chaturvedi: University of Allahabad, India - email@example.com
Lacroix: Départment d'économique, Université Laval, Québec, Canada - Guy.Lacroix@ecn.ulaval.ca