Focused Bayesian Prediction

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

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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: 2019-12, Revised 2020-08
New Economics Papers: this item is included in nep-ets and nep-ore
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Focused Bayesian prediction (2021)
Working Paper: Focused Bayesian Prediction (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1912.12571

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