Bayesian Forecasting in Economics and Finance: A Modern Review
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; and with sufficient computational detail given to assist the reader with implementation.
Keywords
Cite
@article{arxiv.2212.03471,
title = {Bayesian Forecasting in Economics and Finance: A Modern Review},
author = {Gael M. Martin and David T. Frazier and Worapree Maneesoonthorn and Ruben Loaiza-Maya and Florian Huber and Gary Koop and John Maheu and Didier Nibbering and Anastasios Panagiotelis},
journal= {arXiv preprint arXiv:2212.03471},
year = {2023}
}
Comments
The paper is now published online at: https://doi.org/10.1016/j.ijforecast.2023.05.002