Bayesian forecasting in economics and finance: A modern review

The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem – model, parameters, latent states – is able to be quantified explicitly and factored into the forecast distribution vi...

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Bibliographic Details
Published inInternational journal of forecasting Vol. 40; no. 2; pp. 811 - 839
Main Authors Martin, Gael M., Frazier, David T., Maneesoonthorn, Worapree, Loaiza-Maya, Rubén, Huber, Florian, Koop, Gary, Maheu, John, Nibbering, Didier, Panagiotelis, Anastasios
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.04.2024
Subjects
Bayesian computational methods
Bayesian prediction
Electricity demand
Finance
Loss-based Bayesian prediction
Macroeconomics
Marketing
Loss-based Bayesian prediction
Finance
Bayesian computational methods
Bayesian prediction
Macroeconomics
Electricity demand
Marketing
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Summary:The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem – model, parameters, latent states – is able to be quantified explicitly and factored into the forecast distribution via the process of integration or averaging. Allied with the elegance of the method, Bayesian forecasting is now underpinned by the burgeoning field of Bayesian computation, which enables Bayesian forecasts to be produced for virtually any problem, no matter how large or complex. The current state of play in Bayesian forecasting in economics and finance is the subject of this review. The aim is to provide the reader with an overview of modern approaches to the field, set in some historical context, with sufficient computational detail given to assist the reader with implementation.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2023.05.002