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Robust linear static panel data models using ε-contamination

Badi Baltagi (), Georges Bresson (), Anoop Chaturvedi () and Guy Lacroix ()

Journal of Econometrics, 2018, vol. 202, issue 1, 108-123

Abstract: The paper develops a general Bayesian framework for robust linear static panel data models usingε-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.

Keywords: ε-contamination; Hyper g-priors; Type-II maximum likelihood posterior density; Panel data; Robust Bayesian estimator; Three-stage hierarchy (search for similar items in EconPapers)
JEL-codes: C11 C23 C26 (search for similar items in EconPapers)
Date: 2018
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Related works:
Working Paper: Robust linear static panel data models using e-contamination (2017) Downloads
Working Paper: Robust Linear Static Panel Data Models Using ε-Contamination (2017) Downloads
Working Paper: Robust linear static panel data models using ε-contamination (2015) Downloads
Working Paper: Robust Linear Static Panel Data Models Using ?-Contamination (2014) Downloads
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