Robust open Bayesian analysis: Overfitting, model uncertainty, and endogeneity issues in multiple regression models

Antonio Pacifico

Econometric Reviews, 2021, vol. 40, issue 2, 148-176

Abstract: The paper develops a computational method to deal with some open issues related to Bayesian model averaging for multiple linear models: overfitting, model uncertainty, endogeneity issues, and misspecified dynamics. The methodology takes the name of Robust Open Bayesian procedure. It is robust because the Bayesian inference is performed with a set of priors rather than a single prior and open because the model class is not fully known in advance, but rather is defined iteratively by MCMC algorithm. Conjugate informative priors are used to compute exact posterior probabilities. Empirical and simulated examples describe the functioning and performance of the procedure. Discussions with related works are also accounted for.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2020.1770996 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:40:y:2021:i:2:p:148-176

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2020.1770996

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().