Focused Bayesian prediction

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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.

Original languageEnglish
Pages (from-to)517-543
Number of pages27
JournalJournal of Applied Econometrics
Volume36
Issue number5
DOIs
Publication statusPublished - Aug 2021

Keywords

  • Loss-based Bayesian forecasting
  • Proper scoring rules
  • Stochastic volatility
  • Expected shortfall
  • Murphy diagram
  • M4 forecasting competition

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