Robust linear static panel data models using ε-contamination

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17 Août 2017
Types de publication: 
Cahier de recherche
Badi H. Baltagia
Georges Bresson
Anoop Chaturvedi
Guy Lacroix
Axe de recherche: 
Enjeux économiques et financiers
hyper g-priors
type-II maximum likelihood posterior density
panel data
robust Bayesian estimator
three-stage hierarchy
Classification JEL: 

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 coeffcients 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.


Baltagi : Department of Economics and Center for Policy Research, Syracuse University, Syracuse, New York, U.S.A. and Department of Economics, Leicester University, Leicester, UK -

Bresson: Université Paris II / Sorbonne Universités, France -

Chaturvedi: University of Allahabad, India -

Lacroix: Départment d'économique, Université Laval, Québec, Canada -