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 means 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.
ε-Contamination, Hyper g-Priors, Type-II Maximum Likelihood Posterior Density, Panel Data, Robust Bayesian Estimator, Three-Stage Hierarchy.
Working Papers Series
Econometrics | Economic History | Economics | Public Affairs, Public Policy and Public Administration
Baltagi, Badi H.; Bresson, Georges; Chaturvedi, Anoop; and Lacroix, Guy, "Robust Linear Static Panel Data Models Using ε-Contamination" (2017). Center for Policy Research. 239.
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