Focused Bayesian prediction

Summary 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 t...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2021-08, Vol.36 (5), p.517-543
Main Authors: Loaiza‐Maya, Ruben, Martin, Gael M., Frazier, David T.
Format: Article
Language:English
Subjects:
Accuracy
Bayesian analysis
Econometrics
Prediction models
Predictions
Simulation
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Summary:Summary 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.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2810