Document Type

Working Paper

Date

10-2021

Keywords

Dynamic Model, ε-Contamination, g-Priors, Type-II Maximum Likelihood Posterior Density, Panel Data, Robust Bayesian Estimator, Two-Stage Hierarchy

Language

English

Acknowledgements

This paper is written in honor of M. Hashem Pesaran for his many contributions to econometrics. In particular, heterogeneous panel data, Bayesian estimation of dynamic panel data models, random coefficient models for panel data and cross-section dependence in panels. We would like to thank Jean-Michel Etienne for help and support with Stata Mata codes. We also thank an anonymous referee and Cheng Hsiao for useful comments and suggestions. Many thanks to the participants of the 2020 Econometric Society/Bocconi University Virtual World Congress for fruitful comments. Anoop Chaturvedi gratefully acknowledges the Department of Economics, Université Paris II for his visiting professorship and facilities to carry out this work. The usual disclaimers apply.

Disciplines

Economic Policy | Economics | Public Affairs, Public Policy and Public Administration

Description/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.

ISSN

1525-3066

Additional Information

Working paper no. 240

Source

Local input

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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