标题： 经济学与金融学中的贝叶斯预测：现代综述 显示英文标题

标题： Bayesian Forecasting in Economics and Finance: A Modern Review

摘要： 贝叶斯统计学方法提供了一种有原则且一致的预测方法。所有用于刻画任何预测问题的未知量——模型、参数、潜在状态——的不确定性都可以被明确量化，并通过积分或平均过程纳入预测分布中。伴随着方法的优雅性，贝叶斯预测现在得到了蓬勃发展的贝叶斯计算领域的支持，这使得几乎可以为任何问题（无论大小或复杂程度）生成贝叶斯预测。本文综述了经济学和金融领域中贝叶斯预测的现状。目的是为读者提供该领域的现代方法概述，并将其置于一定的历史背景中；同时提供足够的计算细节以帮助读者实现这些方法。 显示英文摘要

摘要： 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.