IDEAS home Printed from https://ideas.repec.org/p/nst/samfok/19623.html
   My bibliography  Save this paper

Dynamic time series modelling and forecasting of COVID-19 in Norway

Author

Listed:
  • Gunnar BÃ¥rdsen

    (Department of Economics, Norwegian University of Science and Technology and Department of Economics, University of Oslo)

  • Ragnar Nymoen

    (Department of Economics, University of Oslo)

Abstract

A framework for forecasting new COViD-19 cases jointly with hospital admissions and hospital beds with COVID-19 cases is presented. This project, dubbed CovidMod, produced 21-days ahead forecasts each working day from March 2021 to April 2022, and forecast errors that were used to assess forecast accuracy. A comparison with the forecasts of the Norwegian Institute of Public Health (NIPH), with dates of origin in the same period, favours the CovidMod forecasts in terms of lower RMSFEs (Root Mean Squared Forecast Errors), both for new cases and for hospital beds. Another comparison, with the short term forecasts (7 day horizon) produced by a forecasting project at the University of Oxford, shows only little difference in terms of the RMSFEs of new cases. Next, we present a further development of the model which allows the effects of policy responses to a central model parameter to be forecasted by an estimated smooth-transition function. The forecasting performance of the resulting non-linear model is demonstrated, and it is suggested as a possible way forward in the development of relevant forecasting tools in general and for pandemics in particular.

Suggested Citation

  • Gunnar BÃ¥rdsen & Ragnar Nymoen, 2023. "Dynamic time series modelling and forecasting of COVID-19 in Norway," Working Paper Series 19623, Department of Economics, Norwegian University of Science and Technology.
    • Handle: RePEc:nst:samfok:19623
    as

    Download full text from publisher

    File URL: http://www.svt.ntnu.no/iso/WP/2023/1_23.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2022. "Short-term forecasting of the coronavirus pandemic," International Journal of Forecasting, Elsevier, vol. 38(2), pages 453-466.
    2. Ragnar Nymoen, 2019. "Dynamic Econometrics for Empirical Macroeconomic Modelling," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 11479, February.
    3. Korolev, Ivan, 2021. "Identification and estimation of the SEIRD epidemic model for COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 63-85.
    4. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
    5. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bårdsen, Gunnar & Nymoen, Ragnar, 2023. "Dynamic time series modelling and forecasting of COVID-19 in Norway," Memorandum 3/2023, Oslo University, Department of Economics.
    2. Fernández-Villaverde, Jesús & Jones, Charles I., 2022. "Estimating and simulating a SIRD Model of COVID-19 for many countries, states, and cities," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    3. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    4. Lokman Gunduz & Ahmet Faruk Aysan & Rifgi Bugra Bagci & Hatice Karahan, 2024. "Explosive Behavior in COVID-19 and Policy Responses: Lessons Learned for Public Health Management," SAGE Open, , vol. 14(1), pages 21582440231, March.
    5. Difang Huang & Ying Liang & Boyao Wu & Yanyi Ye, 2024. "Estimating the Impact of Social Distance Policy in Mitigating COVID-19 Spread with Factor-Based Imputation Approach," Papers 2405.12180, arXiv.org.
    6. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    7. M. Hashem Pesaran & Cynthia Fan Yang, 2022. "Matching theory and evidence on Covid‐19 using a stochastic network SIR model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1204-1229, September.
    8. Erie Febrian & Aldrin Herwany, 2009. "Volatility Forecasting Models and Market Co-Integration: A Study on South-East Asian Markets," Working Papers in Economics and Development Studies (WoPEDS) 200911, Department of Economics, Padjadjaran University, revised Sep 2009.
    9. David Murrell & Weiqiu Yu, 2000. "The Effect of the Harmonized Sales Tax on Consumer Prices in Atlantic Canada," Canadian Public Policy, University of Toronto Press, vol. 26(4), pages 451-460, December.
    10. Chaohua Dong & Jiti Gao & Oliver Linton & Bin peng, 2020. "On Time Trend of COVID-19: A Panel Data Study," Monash Econometrics and Business Statistics Working Papers 22/20, Monash University, Department of Econometrics and Business Statistics.
    11. Thomas Dohmen & Hartmut F. Lehmann & Mark E. Schaffer, 2014. "Wage Policies of a Russian Firm and the Financial Crisis of 1998: Evidence from Personnel Data, 1997 to 2002," ILR Review, Cornell University, ILR School, vol. 67(2), pages 504-531, April.
    12. Jun Ma & Mark E. Wohar, 2013. "An Unobserved Components Model that Yields Business and Medium-Run Cycles," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1351-1373, October.
    13. Pär Österholm, 2005. "The Taylor Rule: A Spurious Regression?," Bulletin of Economic Research, Wiley Blackwell, vol. 57(3), pages 217-247, July.
    14. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    15. Ang, Andrew & Piazzesi, Monika & Wei, Min, 2006. "What does the yield curve tell us about GDP growth?," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 359-403.
    16. Hortaçsu, Ali & Liu, Jiarui & Schwieg, Timothy, 2021. "Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 106-129.
    17. Michaelides, Panayotis & Milios, John, 2009. "TFP change, output gap and inflation in the Russian Federation (1994-2006)," Journal of Economics and Business, Elsevier, vol. 61(4), pages 339-352, July.
    18. Babula, Ronald A., 1997. "Economic Effects Of A Countervailing Duty Order On The U.S. Lamb Meat Industry," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 26(01), pages 1-12, April.
    19. Giancarlo Lutero & Marco Marini, 2010. "Direct vs Indirect Forecasts of Foreign Trade Unit Value Indices," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(2-3), pages 73-96, October.
    20. jose ramos pires manso, 2004. "Economical Versus Political Cycles In An Iberian Manufacturing Sector," Industrial Organization 0404003, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nst:samfok:19623. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anne Larsen (email available below). General contact details of provider: https://edirc.repec.org/data/isontno.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.