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THE VALUE OF ROBUST STATISTICAL FORECASTS IN THE COVID-19 PANDEMIC

Published online by Cambridge University Press:  23 June 2021

Jennifer L. Castle*
Affiliation:
Magdalen College, University of Oxford, Oxford, United Kingdom Climate Econometrics, Nuffield College, University of Oxford, Oxford, United Kingdom
Jurgen A. Doornik
Affiliation:
Climate Econometrics, Nuffield College, University of Oxford, Oxford, United Kingdom Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford, United Kingdom
David F. Hendry
Affiliation:
Climate Econometrics, Nuffield College, University of Oxford, Oxford, United Kingdom Institute for New Economic Thinking at the Oxford Martin School, University of Oxford, Oxford, United Kingdom
*
*Corresponding author. Email: jennifer.castle@magd.ox.ac.uk

Abstract

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.

Type
Research Article
Copyright
© National Institute Economic Review, 2021

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