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Robust linear static panel data models using ε-contamination
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DOI：10.1016/j.jeconom.2017.07.002.png)
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The paper develops a general Bayesian framework for robust linear static panel data models using epsilon-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. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
epsilon-contamination
Hyper g-priors
Type-II maximum likelihood posterior density
Panel data
Robust Bayesian estimator
Three-stage hierarchy
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