Testing the predictive accuracy of COVID-19 forecasts

Laura Coroneo, Fabrizio Iacone, Alessia Paccagnini and Paulo Santos Monteiro

Discussion Papers from Department of Economics, University of York

Abstract: We test the predictive accuracy of forecasts for the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention (CDC), both at the national and state levels. We find three main results. First, at short-horizon (1-week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3 and 4-weeks ahead) forecasters are more successful and sometimes outperform the benchmark. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available forecasts using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a safer approach for health authorities, rather than relying on a small number of forecasts.

Keywords: Forecast evaluation; Forecasting tests; Epidemic. (search for similar items in EconPapers)
JEL-codes: C12 C53 I18 (search for similar items in EconPapers)
Date: 2020-10
New Economics Papers: this item is included in nep-for and nep-ore
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Testing the predictive accuracy of COVID-19 forecasts (2023)
Working Paper: Testing the predictive accuracy of COVID-19 forecasts (2021)
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