Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/106601
Authors: 
Baltagi, Badi H.
Bresson, Georges
Chaturvedi, Anoop
Lacroix, Guy
Year of Publication: 
2014
Series/Report no.: 
IZA Discussion Papers 8661
Abstract: 
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.
Subjects: 
ε-contamination
hyper g-priors
type-II maximum likelihood posterior density
panel data
robust Bayesian estimator
three-stage hierarchy
JEL: 
C11
C23
C26
Document Type: 
Working Paper

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