We consider forecast comparison in the presence of instability when this affects only a short period of time. We demonstrate that global tests do not perform well in this case, as they were not designed to capture very short-lived instabilities, and their power vanishes altogether when the magnitude of the shock is very large. We then discuss and propose approaches that are more suitable to detect such situations, such as nonparametric methods (S test or MAX procedure). We illustrate these results in different Monte Carlo exercises and in evaluating the nowcast of the quarterly US nominal GDP from the Survey of Professional Forecasters (SPF) against a naive benchmark of no growth, over the period that includes the GDP instability brought by the Covid-19 crisis. We recommend that the forecaster should not pool the sample, but exclude the short periods of high local instability from the evaluation exercise.

翻译：我们考虑在不稳定性仅影响短期时段时的预测比较问题。研究表明，全局检验在此类情形下表现不佳——其设计初衷并非捕捉短暂的不稳定性，且当冲击幅度极大时检验效力完全消失。我们继而讨论并提出更适用于检测此类状况的方法，例如非参数方法（S检验或MAX程序）。通过多项蒙特卡罗模拟实验，以及在专业预测者调查（SPF）对2020年新冠疫情引发GDP不稳定性期间美国季度名义GDP的即时预测评估中（相较于零增长朴素基准模型），我们验证了上述结论。我们建议预测者不应合并样本，而需从评估练习中剔除高局部不稳定的短期时段。