Robust linear static panel data models using ε-contamination

Badi H. Baltagi, Georges Bresson, Anoop Chaturvedi, Guy Lacroix

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

The paper develops a general Bayesian framework for robust linear static panel data models usingε-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.

Original languageEnglish (US)
Pages (from-to)108-123
Number of pages16
JournalJournal of Econometrics
Volume202
Issue number1
DOIs
StatePublished - Jan 2018

Keywords

  • Hyper g-priors
  • Panel data
  • Robust Bayesian estimator
  • Three-stage hierarchy
  • Type-II maximum likelihood posterior density
  • ε-contamination

ASJC Scopus subject areas

  • Economics and Econometrics

Fingerprint Dive into the research topics of 'Robust linear static panel data models using ε-contamination'. Together they form a unique fingerprint.

  • Cite this