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Persistent link: https://www.econbiz.de/10011411477
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...
Persistent link: https://www.econbiz.de/10012834582
The paper develops a general Bayesian framework for robust linear static panel data models using ε-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...
Persistent link: https://www.econbiz.de/10013042986
The paper develops a general Bayesian framework for robust linear static panel data models using ε-contamination. A two-step approach is employed to derive the conditional type-II maximum likelihood (ML-II) posterior distribution of the coeffcients and individual effects. The ML-II posterior...
Persistent link: https://www.econbiz.de/10012919765
Persistent link: https://www.econbiz.de/10013194595
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...
Persistent link: https://www.econbiz.de/10013211880
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...
Persistent link: https://www.econbiz.de/10012210757
Persistent link: https://www.econbiz.de/10012162338
Persistent link: https://www.econbiz.de/10012152834
Persistent link: https://www.econbiz.de/10011974556