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Focused Bayesian prediction

Ruben Loaiza‐Maya, Gael M. Martin and David T. Frazier
Authors registered in the RePEc Author Service: Rubén Albeiro Loaiza Maya ()

Journal of Applied Econometrics, 2021, vol. 36, issue 5, 517-543

Abstract: We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over a class of plausible predictive models. After observing data, we update the prior to a posterior over these models, via a criterion that captures a user‐specified measure of predictive accuracy. Under regularity, this update yields posterior concentration onto the element of the predictive class that maximizes the expectation of the accuracy measure. In a series of simulation experiments and empirical examples, we find notable gains in predictive accuracy relative to conventional likelihood‐based prediction.

Date: 2021
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https://doi.org/10.1002/jae.2810

Related works:
Working Paper: Focused Bayesian Prediction (2020) Downloads
Working Paper: Focused Bayesian Prediction (2020) Downloads
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