Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/223656 
Year of Publication: 
2020
Series/Report no.: 
IZA Discussion Papers No. 13214
Publisher: 
Institute of Labor Economics (IZA), Bonn
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 misspecication 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 nite 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 specications and their associated estimation methods as special cases.
Subjects: 
robust Bayesian estimator
g-priors
type-II maximum likelihood posterior density
e-contamination
panel data
dynamic model
two-stage hierarchy
JEL: 
C11
C23
C26
Document Type: 
Working Paper

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