Robust dynamic panel data models using ε-contamination

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23 Janvier 2020
Types de publication: 
Cahier de recherche
Badi H. Baltagia
Georges Bresson
Anoop Chaturvedi
Guy Lacroix
Axe de recherche: 
Enjeux économiques et financiers
dynamic model
type-II maximum likelihood posterior density
panel data
robust Bayesian estimator
two-stage hierarchy
Classification JEL: 

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 e-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the econtamination 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 `a 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.


Badi H. Baltagi : Department of Economics and Center for Policy Research, Syracuse University.
Georges Bresson : Department of Economics, Université Paris II.
Anoop Chaturvedi : Department of Statistics, University of Allahabad, India.
Guy Lacroix : Department of Economics, Université Laval.