Robust Linear Static Panel Data Models Using ε-Contamination
Badi Baltagi (),
Georges Bresson (),
Anoop Chaturvedi () and
Guy Lacroix ()
No 8661, IZA Discussion Papers from Institute for the Study of Labor (IZA)
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: 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)
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Forthcoming in: Journal of Econometrics, 2017
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Working Paper: Robust Linear Static Panel Data Models Using ε-Contamination (2017)
Working Paper: Robust linear static panel data models using ε-contamination (2015)
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