Description/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 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.
Document Type
Working Paper
Date
Fall 9-2017
Keywords
ε-Contamination, Hyper g-Priors, Type-II Maximum Likelihood Posterior Density, Panel Data, Robust Bayesian Estimator, Three-Stage Hierarchy.
Language
English
Series
Working Papers Series
Disciplines
Econometrics | Economic History | Economics | Public Affairs, Public Policy and Public Administration
ISSN
1525-3066
Recommended Citation
Baltagi, Badi H.; Bresson, Georges; Chaturvedi, Anoop; and Lacroix, Guy, "Robust Linear Static Panel Data Models Using ε-Contamination" (2017). Center for Policy Research. 239.
https://surface.syr.edu/cpr/239
Accessible PDF version
Source
Local input
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Included in
Econometrics Commons, Economic History Commons, Public Affairs, Public Policy and Public Administration Commons
Additional Information
Working paper no. 208