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Short-term forecasting of the Coronavirus Pandemic - 2020-04-27

Author

Listed:
  • Jennifer L. Castle

    (Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Magdalen College, University of Oxford)

  • Jurgen A. Doornik

    (Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Climate Econometrics, Nuffield College, University of Oxford)

  • David F. Hendry

    (Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Climate Econometrics, Nuffield College, University of Oxford)

Abstract

We have been publishing real-time forecasts of confirmed cases and deaths for COVID-19 online at www.doornik.com/COVID-19 from mid-March 2020. These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative of short term developments, without requiring other assumptions of how the SARS-CoV-2 virus is spreading, or whether preventative policies are effective. As such they are complementary to forecasts from epidemiological models. The forecasts are based on extracting trends from windows of the data, applying machine learning, and then computing forecasts by applying some constraints to this flexible extracted trend. The methods have previously been applied to various other time series data and have performed well. They are also effective in this setting, providing better forecasts than some epidemiological models.

Suggested Citation

  • Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
    • Handle: RePEc:nuf:econwp:2006
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    File URL: https://www.nuffield.ox.ac.uk/economics/Papers/2020/2020W06_COVID-19_shortterm_forecasts.pdf
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    References listed on IDEAS

    as
    1. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    2. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, vol. 3(2), pages 1-25, April.
    3. Jennifer L. Castle & Jurgen A. Doornik & David Hendry, 2019. "Some forecasting principles from the M4 competition," Economics Papers 2019-W01, Economics Group, Nuffield College, University of Oxford.
    4. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, Decembrie.
    5. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    6. Castle, Jennifer & Shephard, Neil (ed.), 2009. "The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry," OUP Catalogue, Oxford University Press, number 9780199237197, Decembrie.
    7. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2020. "Card forecasts for M4," International Journal of Forecasting, Elsevier, vol. 36(1), pages 129-134.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    2. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    5. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2023. "Robust Discovery of Regression Models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 31-51.
    6. Bozkir, Cem D.C. & Ozmemis, Cagri & Kurbanzade, Ali Kaan & Balcik, Burcu & Gunes, Evrim D. & Tuglular, Serhan, 2023. "Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study," European Journal of Operational Research, Elsevier, vol. 304(1), pages 276-291.
    7. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.

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    More about this item

    Keywords

    Autometrics; Cardt; COVID-19; Epidemiology; Forecasting; Forecast averaging; Machine learning; Smoothing; Trend Indicator Saturation.;
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