Bayesian Forecasting in Economics and Finance: A Modern Review

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

Bayesian Forecasting in Economics and Finance: A Modern Review

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arxiv 2212.03471 v2 pith:7PIJHZAN submitted 2022-12-07 econ.EM stat.APstat.ME

Bayesian Forecasting in Economics and Finance: A Modern Review

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

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Anastasios Panagiotelis

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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; and with sufficient computational detail given to assist the reader with implementation.

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