Bayesian Forecasting in the 21st Century: A Modern Review

Gael Martin (), David Frazier (), Ruben Loaiza-Maya (), Florian Huber, Gary Koop, John Maheu, Didier Nibbering () and Anastasios Panagiotelis ()

No 1/23, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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 factored into the forecast distribution, with forecasts conditioned only on what is known or observed. 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 is the subject of this review. The aim is to provide readers with an overview of modern approaches to the field, set in some historical context. Whilst our primary focus is on applications in the fields of economics and finance, and their allied disciplines, sufficient general details about implementation are provided to aid and inform all investigators.

Keywords: Bayesian prediction; macroeconomics; finance; marketing; electricity demand; Bayesian computational methods; loss-based Bayesian prediction (search for similar items in EconPapers)
JEL-codes: C01 C11 C53 (search for similar items in EconPapers)
Pages: 57
Date: 2023
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-for
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