The value of robust statistical forecasts in the COVID-19 pandemic - Castle, JL; Doornik, J; Hendry, D (Cambridge University Press, 2021)
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 gap between our statistical and theory-based forecasts provide a measure of the effect of furlough policies on stabilizing unemployment, establishing useful scenarios had furlough policies not been implemented.