@TechReport{iza:izadps:dp8661,
            	author={Baltagi, Badi H. and Bresson, Georges and Chaturvedi, Anoop and Lacroix, Guy},
            	title={Robust Linear Static Panel Data Models Using ε-Contamination},
            	year={2014},
	month={Nov},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={8661},
            	url={https://www.iza.org/publications/dp8661},
	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 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. We illustrate the performance of our estimator relative to classic panel estimators using data on earnings and crime.},
	keywords={ε-contamination;hyper g-priors;type-II maximum likelihood posterior density;panel data;robust Bayesian estimator;three-stage hierarchy},
            }