Focused Bayesian prediction

Research output: Contribution to journalArticleResearchpeer-review

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
Number of pages35
JournalJournal of Applied Econometrics
DOIs
Publication statusAccepted/In press - 2021

Keywords

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

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