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

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  • Antonio Pacifico

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.

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  • Antonio Pacifico, 2021. "Robust open Bayesian analysis: Overfitting, model uncertainty, and endogeneity issues in multiple regression models," Econometric Reviews, Taylor & Francis Journals, vol. 40(2), pages 148-176, February.
    • Handle: RePEc:taf:emetrv:v:40:y:2021:i:2:p:148-176
    DOI: 10.1080/07474938.2020.1770996
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    Cited by:

    1. Antonio Pacifico, 2022. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure," Econometrics, MDPI, vol. 10(3), pages 1-24, July.
    2. Antonio Pacifico, 2023. "The Impact of Socioeconomic and Environmental Indicators on Economic Development: An Interdisciplinary Empirical Study," JRFM, MDPI, vol. 16(5), pages 1-16, May.

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