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Testing the predictive accuracy of COVID-19 forecasts

Author

Listed:
  • Laura Coroneo
  • Fabrizio Iacone
  • Alessia Paccagnini
  • Paulo Santos Monteiro

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.

Suggested Citation

  • Laura Coroneo & Fabrizio Iacone & Alessia Paccagnini & Paulo Santos Monteiro, 2020. "Testing the predictive accuracy of COVID-19 forecasts," Discussion Papers 20/10, Department of Economics, University of York.
  • Handle: RePEc:yor:yorken:20/10
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    Cited by:

    1. Marcelo Medeiros & Alexandre Street & Davi Vallad~ao & Gabriel Vasconcelos & Eduardo Zilberman, 2020. "Short-Term Covid-19 Forecast for Latecomers," Papers 2004.07977, arXiv.org, revised Sep 2021.

    More about this item

    Keywords

    Forecast evaluation; Forecasting tests; Epidemic.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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