Focused Bayesian Prediction

Ruben Loaiza-Maya (ruben.loaizamaya@monash.edu), Gael Martin (gael.martin@monash.edu) and David Frazier (david.frazier@monash.edu)
Authors registered in the RePEc Author Service: Rubén Albeiro Loaiza Maya

No 1/20, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: loss-based prediction; Bayesian forecasting; proper scoring rules; stochastic volatility model; expected shortfall; M4 forecasting competition (search for similar items in EconPapers)
JEL-codes: C11 C53 C58 (search for similar items in EconPapers)
Pages: 44
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: Focused Bayesian prediction (2021)
Working Paper: Focused Bayesian Prediction (2020)
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