%0 Report
        %A Baltagi, Badi H.
%A Bresson, Georges
%A Chaturvedi, Anoop
%A Lacroix, Guy
        %T Robust Linear Static Panel Data Models Using ε-Contamination
        %D 2014
        %8 2014 Nov
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 8661
        %U https://www.iza.org/index.php/publications/dp8661
        %X 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.
        %K ε-contamination
%K hyper g-priors
%K type-II maximum likelihood posterior density
%K panel data
%K robust Bayesian estimator
%K three-stage hierarchy