Bayesian Forecasting in Economics and Finance: A Modern Review

TL;DR

This paper reviews modern Bayesian forecasting methods in economics and finance, emphasizing their ability to quantify uncertainty and the role of computational advances in enabling complex, real-world applications.

Contribution

It provides a comprehensive overview of recent Bayesian forecasting approaches, including historical context and computational techniques for practical implementation.

Findings

Bayesian methods effectively quantify uncertainty in forecasts.

Computational advances enable application to complex economic and financial models.

The review highlights the integration of Bayesian inference with modern computational tools.

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.