Focused Bayesian prediction

Bibliographic Data

ID21653408
AuthorsRubén Loaiza‐Maya (Department of Econometrics and Business Statistics Monash University and Australian Centre of Excellence in Mathematics and Statistics Melbourne Victoria Australia), Gael M Martin (Department of Econometrics and Business Statistics Monash University and Australian Centre of Excellence in Mathematics and Statistics Melbourne Victoria Australia, corresponding author), David T Frazier (0000-0002-3446-2516, Department of Econometrics and Business Statistics Monash University and Australian Centre of Excellence in Mathematics and Statistics Melbourne Victoria Australia)
Year2021
Volume36
Issue5
Pages517-543
Publication date2021-08-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Applied Econometrics (JOURNAL)
Journal identifiersISSN: 1099-1255 • E-ISSN: 0883-7252
PublisherWiley (PUBLISHER • GB)
DOI10.1002/jae.2810
OpenAlexW2996797158
LanguageEN
References cited54

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

Bayesian probability · Data mining · Machine learning · Posterior probability · Computer Science · Forecasting Techniques and Applications · Gaussian Processes and Bayesian Inference · Statistical Methods and Bayesian Inference · Artificial Intelligence

Tools

Open DOIOpen Access