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Robust linear static panel data models using ε-contamination

journal contribution
posted on 04.09.2017, 15:24 by Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix
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.

History

Citation

Journal of Econometrics, 2017, in press

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/Department of Economics

Version

AM (Accepted Manuscript)

Published in

Journal of Econometrics

Publisher

Elsevier

issn

0304-4076

Acceptance date

18/07/2017

Copyright date

2017

Available date

26/08/2019

Publisher version

http://www.sciencedirect.com/science/article/pii/S0304407617301446

Notes

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.

Language

en

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