Testing the predictive accuracy of COVID-19 forecasts

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

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: We test the predictive accuracy of forecasts of the number of COVID-19 fatalities produced by several forecasting teams and collected by the United States Centers for Disease Control and Prevention during the first and second waves of the epidemic in the United States. We find three main results. First, at the short horizon (1-week ahead) no forecasting team outperforms a simple time-series benchmark. Second, at longer horizons (3- and 4-week ahead) forecasters are more successful and sometimes outperform the benchmark, in particular during the first wave of the epidemic. Third, one of the best performing forecasts is the Ensemble forecast, that combines all available predictions using uniform weights. In view of these results, collecting a wide range of forecasts and combining them in an ensemble forecast may be a superior 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)
Pages: 35 pages
Date: 2021-07
New Economics Papers: this item is included in nep-for and nep-ore
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https://cama.crawford.anu.edu.au/sites/default/fil ... agnini_monteiro0.pdf (application/pdf)

Related works:
Journal Article: Testing the predictive accuracy of COVID-19 forecasts (2023)
Working Paper: Testing the predictive accuracy of COVID-19 forecasts (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2021-52

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