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Title: Robust linear static panel data models using ε-contamination
Authors: Baltagi, Badi H.
Bresson, Georges
Chaturvedi, Anoop
Lacroix, Guy
First Published: 26-Aug-2017
Publisher: Elsevier
Citation: Journal of Econometrics, 2017, in press
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 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.
DOI Link: 10.1016/j.jeconom.2017.07.002
ISSN: 0304-4076
Embargo on file until: 26-Aug-2019
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © Elsevier, 2017. After an embargo period this version of the paper will be an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License (, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Description: The file associated with this record is under embargo until 24 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Published Articles, Dept. of Economics

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