Bayesian forecasting in economics and finance: A modern review

Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn, Rubén Loaiza-Maya, Florian Huber, Gary Koop, John Maheu, Didier Nibbering and Anastasios Panagiotelis

International Journal of Forecasting, 2024, vol. 40, issue 2, 811-839

Abstract: 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.

Keywords: Bayesian prediction; Macroeconomics; Finance; Marketing; Electricity demand; Bayesian computational methods; Loss-based Bayesian prediction (search for similar items in EconPapers)
Date: 2024
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Working Paper: Bayesian Forecasting in Economics and Finance: A Modern Review (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:2:p:811-839

DOI: 10.1016/j.ijforecast.2023.05.002

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