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Robust Dynamic Panel Data Models Using e-Contamination

Badi Baltagi (), Georges Bresson (), Anoop Chaturvedi () and Guy Lacroix ()

No 13214, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This paper extends the work of Baltagi et al. (2018) to the popular dynamic panel data model. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)'s g-priors for the variance-covariance matrices. We propose a general "toolbox" for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. The paper contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

Keywords: two-stage hierarchy; dynamic model; panel data; e-contamination; type-II maximum likelihood posterior density; g-priors; robust Bayesian estimator (search for similar items in EconPapers)
JEL-codes: C11 C23 C26 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2020-05
New Economics Papers: this item is included in nep-ecm and nep-ore
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Published - published in: Advances in Econometrics, 2022, 43 B3 (Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology), 307 - 336

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Working Paper: Robust Dynamic Panel Data Models Using ?-contamination (2020) Downloads
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