Bayesian methods in economics and finance: A unified survey and taxonomy

Abstract

This special issue brings together 21 papers that reflect the expanding scope and methodological evolution of Bayesian econometrics. We introduce a new organizing taxonomy that distinguishes between Traditional Bayesian Approaches - rooted in conjugate priors, standard likelihood-based estimation, and established Markov Chain Monte Carlo (MCMC) techniques - and Contemporary Bayesian Frontiers, characterized by nonparametric methods, variational inference, high-dimensional shrinkage, and integration with machine learning. We further classify contributions across two broad domains covering applications in Economics (focusing on macroeconomics, microeconomics, and climate econometrics) and Finance (focusing on asset pricing, volatility, and bank business models). Across these fields, four cross-cutting themes emerge: (1) high-dimensionality, volatility, and time-varying dynamics; (2) structural identification and model uncertainty; (3) semiparametric and nonparametric flexibility; and (4) data quality, granularity, and novel data structures. This survey synthesizes the methodological and empirical contributions of the special issue, proposes a unifying framework for organizing recent advances in Bayesian econometrics, and identifies unresolved challenges and promising directions for future research.