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Open AccessFeature PaperArticle

Bayesian Model Averaging Using Power-Expected-Posterior Priors

1
Statistics Lab, Department of Mathematics, National Technical University of Athens, Zografou Campus, 15780 Athens, Greece
2
Computational and Bayesian Statistics Lab, Department of Statistics, Athens University of Economics and Business, 10434 Athens, Greece
*
Author to whom correspondence should be addressed.
Econometrics 2020, 8(2), 17; https://doi.org/10.3390/econometrics8020017
Received: 18 February 2020 / Revised: 15 March 2020 / Accepted: 6 May 2020 / Published: 11 May 2020
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
This paper focuses on the Bayesian model average (BMA) using the power–expected– posterior prior in objective Bayesian variable selection under normal linear models. We derive a BMA point estimate of a predicted value, and present computation and evaluation strategies of the prediction accuracy. We compare the performance of our method with that of similar approaches in a simulated and a real data example from economics. View Full-Text
Keywords: Bayesian model averaging; Bayesian variable selection; expected–posterior priors; imaginary training samples; power–expected–posterior priors Bayesian model averaging; Bayesian variable selection; expected–posterior priors; imaginary training samples; power–expected–posterior priors
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Fouskakis, D.; Ntzoufras, I. Bayesian Model Averaging Using Power-Expected-Posterior Priors. Econometrics 2020, 8, 17.

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