Learning, forecasting and structural breaks
提出一种在模型参数可能发生不可预测变化时进行预测的通用方法，利用贝叶斯学习和模型比较得到考虑未来可能发生结构突变的预测密度，并给出最近一次突变的后验分布估计。
Abstract We provide a general methodology for forecasting in the presence of structural breaks induced by unpredictable changes to model parameters. Bayesian methods of learning and model comparison are used to derive a predictive density that takes into account the possibility that a break will occur before the next observation. Estimates for the posterior distribution of the most recent break are generated as a by‐product of our procedure. We discuss the importance of using priors that accurately reflect the econometrician's opinions as to what constitutes a plausible forecast. Several applications to macroeconomic time‐series data demonstrate the usefulness of our procedure. Copyright © 2008 John Wiley & Sons, Ltd.