Robust linear static panel data models using ε -contamination

Citation data:

Journal of Econometrics, ISSN: 0304-4076, Vol: 202, Issue: 1, Page: 108-123

Publication Year:
2018
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DOI:
10.1016/j.jeconom.2017.07.002
Author(s):
Badi H. Baltagi; Georges Bresson; Anoop Chaturvedi; Guy Lacroix
Publisher(s):
Elsevier BV
Tags:
Economics, Econometrics and Finance
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article description
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.