Robust Linear Static Panel Data Models Using ε-Contamination
Badi Baltagi (),
Georges Bresson (),
Anoop Chaturvedi () and
Guy Lacroix ()
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Anoop Chaturvedi: University of Allahabad
No 208, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University
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.
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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Journal Article: Robust linear static panel data models using ε-contamination (2018)
Working Paper: Robust linear static panel data models using e-contamination (2017)
Working Paper: Robust linear static panel data models using ε-contamination (2015)
Working Paper: Robust Linear Static Panel Data Models Using ?-Contamination (2014)
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