Bayesian Econometrics

Abstract

Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed to a large and growing number of applications. One of the main advantages of Bayesian inference is to deal with different and many sources of uncertainty, including data, model, parameter, parameter restriction uncertainties, in a unified and coherent framework. This Special Issue focuses on exercises where one or more of these features are crucial. Applications include risk measurement in international and financial markets, forecasting, assessment of policy effectiveness in macro and monetary economics. Papers that contain original research on this theme are actively solicited.