Robust Linear Static Panel Data Models Using Ε-Contamination
CRREP working paper serie 2017-06
36 Pages Posted: 22 May 2018
Date Written: August 01, 2017
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 coeffcients 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.
Keywords: ε-contamination, hyper g-priors, type-II maximum likelihood posterior density, panel data, robust Bayesian estimator, three-stage hierarchy
JEL Classification: C11, C23, C26
Suggested Citation: Suggested Citation