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Focused Bayesian prediction
Author(s) -
LoaizaMaya Ruben,
Martin Gael M.,
Frazier David T.
Publication year - 2021
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2810
Subject(s) - computer science , bayesian probability , measure (data warehouse) , posterior probability , process (computing) , data mining , machine learning , artificial intelligence , operating system
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.

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