Robust linear static panel data models using epsilon-contamination

Baltagi, Badi H. and Bresson, Georges and Chaturvedi, Anoop and Lacroix, Guy (2014): Robust linear static panel data models using epsilon-contamination.

Abstract

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

• Item Type: MPRA Paper
• Language: English
• Keywords: epsilon-contamination, hyper g-priors, type II maximum likelihood posterior density, panel data, robust Bayesian estimator, three-stage hierarchy.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C26 - Instrumental Variables (IV) Estimation

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