Forecasting in the Presence of Instabilities: How We Know Whether Models Predict Well and How to Improve Them
指导如何在经济时间序列普遍存在的不稳定性下评估和改进模型的预测能力，包括预测金融危机、资产价格波动等实例，并强调参数突变并非预测性能变化的必要条件。
This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007–08, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth, and inflation. In the context of unstable environments, I discuss how to assess models’ forecasting ability; how to robustify models’ estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models’ parameters are neither necessary nor sufficient to generate time variation in models’ forecasting performance: thus, one should not test for breaks in models’ parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models’ forecasting performance are more appropriate than traditional, average measures.