The Covid-19 pandemic has put forecasting under the spotlight, pitting epidemiological models against extrapolative time-series devices. We have been producing real-time short-term forecasts of confirmed cases and deaths using robust statistical models since 20 March 2020. The forecasts are adaptive to abrupt structural change, a major feature of the pandemic data due to data measurement errors, definitional and testing changes, policy interventions, technological advances and rapidly changing trends. The pandemic has also led to abrupt structural change in macroeconomic outcomes. Using the same methods, we forecast aggregate UK unemployment over the pandemic. The forecasts rapidly adapt to the employment policies implemented when the UK entered the first lockdown. The difference between our statistical and theory based forecasts provides a measure of the effect of furlough policies on stabilising unemployment, establishing useful scenarios had furlough policies not been implemented.

中文翻译：

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稳健统计预测在 COVID-19 大流行中的价值

Covid-19 大流行将预测置于聚光灯下，将流行病学模型与外推时间序列设备相提并论。自 2020 年 3 月 20 日以来，我们一直在使用强大的统计模型对确诊病例和死亡人数进行实时短期预测。这些预测能够适应突然的结构变化，这是由于数据测量错误、定义和测试导致的大流行数据的主要特征变化、政策干预、技术进步和快速变化的趋势。大流行还导致宏观经济结果的结构性突然变化。使用相同的方法，我们预测大流行期间英国的总体失业率。这些预测迅速适应英国进入第一次封锁时实施的就业政策。