IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-20-00405.html
   My bibliography  Save this article

Predicting the COVID-19 pandemic in Canada and the US

Author

Listed:
  • Ba Chu

    (Carleton University)

  • Shafiullah Qureshi

    (Carleton University)

Abstract

We propose a time series model with the quartic trend function to make short-term forecasts of the COVID-19 confirmed cases in Canada and the U.S. Our one- to seven- days ahead out-of-sample forecast exercise demonstrates that the quartic trend model can produce very competitive short-term forecasts relative to the benchmark Susceptible, Infected, and Recovered (SIR) model. The bootstrap distance-based test of independence and the XGBoost algorithm reveals a strong link between the coronavirus case count and relevant Google Trends features (defined by search intensities of various keywords that the public entered in the Google internet search engine during this pandemic). Moreover, dynamic linear panel data models suggest a statistically significant relationship between the coronavirus case count and people's mobility trend provided by Google Mobility Reports (GMR) during the pandemic period.

Suggested Citation

  • Ba Chu & Shafiullah Qureshi, 2020. "Predicting the COVID-19 pandemic in Canada and the US," Economics Bulletin, AccessEcon, vol. 40(3), pages 2565-2585.
    • Handle: RePEc:ebl:ecbull:eb-20-00405
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2020/Volume40/EB-20-V40-I3-P223.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Dhaene, Geert & Jochmans, Koen, 2016. "Bias-corrected estimation of panel vector autoregressions," Economics Letters, Elsevier, vol. 145(C), pages 98-103.
    2. Han, Chirok & Phillips, Peter C. B., 2010. "Gmm Estimation For Dynamic Panels With Fixed Effects And Strong Instruments At Unity," Econometric Theory, Cambridge University Press, vol. 26(1), pages 119-151, February.
    3. Jinyong Hahn & Whitney Newey, 2004. "Jackknife and Analytical Bias Reduction for Nonlinear Panel Models," Econometrica, Econometric Society, vol. 72(4), pages 1295-1319, July.
    4. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    5. Blundell,Richard & Newey,Whitney & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521871549, January.
    6. Thiemo Fetzer & Lukas Hensel & Johannes Hermle & Christopher Roth, 2021. "Coronavirus Perceptions and Economic Anxiety," The Review of Economics and Statistics, MIT Press, vol. 103(5), pages 968-978, December.
    7. repec:hal:spmain:info:hdl:2441/7si2u15cul9u5a44sevcgkbaa9 is not listed on IDEAS
    8. Blundell,Richard & Newey,Whitney & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521692106, January.
    9. Blundell,Richard & Newey,Whitney K. & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521871532, January.
    10. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    11. Blundell,Richard & Newey,Whitney K. & Persson,Torsten (ed.), 2007. "Advances in Economics and Econometrics," Cambridge Books, Cambridge University Press, number 9780521692090, January.
    12. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    13. Sepehr Rafieenasab & Amir-Pouyan Zahiri & Ehsan Roohi, 2020. "Prediction of peak and termination of novel coronavirus COVID-19 epidemic in Iran," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(11), pages 1-17, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sen, Anindya & Baker, John David & Zhang, Qihuang & Agarwal, Rishav Raj & Lam, Jean-Paul, 2023. "Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 225-242.
    2. Lidia Betcheva & Feryal Erhun & Antoine Feylessoufi & Peter Fryers & Paulo Gonçalves & Houyuan Jiang & Paul Kattuman & Tom Pape & Anees Pari & Stefan Scholtes & Carina Tyrrell, 2024. "An Adaptive Research Approach to COVID-19 Forecasting for Regional Health Systems in England," Interfaces, INFORMS, vol. 54(6), pages 500-516, November.

    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. Schumann, Martin & Severini, Thomas A. & Tripathi, Gautam, 2021. "Integrated likelihood based inference for nonlinear panel data models with unobserved effects," Journal of Econometrics, Elsevier, vol. 223(1), pages 73-95.
    2. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    3. Fernández-Val, Iván, 2009. "Fixed effects estimation of structural parameters and marginal effects in panel probit models," Journal of Econometrics, Elsevier, vol. 150(1), pages 71-85, May.
    4. Daniel Czarnowske & Amrei Stammann, 2019. "Fixed Effects Binary Choice Models: Estimation and Inference with Long Panels," Papers 1904.04217, arXiv.org, revised Oct 2020.
    5. Shuowen Chen, 2022. "Indirect Inference for Nonlinear Panel Models with Fixed Effects," Papers 2203.10683, arXiv.org, revised Apr 2022.
    6. Joakim Westerlund & Jörg Breitung, 2013. "Lessons from a Decade of IPS and LLC," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 547-591, August.
    7. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    8. repec:hal:spmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    9. repec:spo:wpmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    10. repec:spo:wpecon:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    11. repec:hal:wpspec:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    12. Hoderlein, Stefan & White, Halbert, 2012. "Nonparametric identification in nonseparable panel data models with generalized fixed effects," Journal of Econometrics, Elsevier, vol. 168(2), pages 300-314.
    13. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    14. repec:spo:wpmain:info:hdl:2441/eu4vqp9ompqllr09ij4j0h0h1 is not listed on IDEAS
    15. Andersen, Torben G. & Fusari, Nicola & Todorov, Viktor & Varneskov, Rasmus T., 2019. "Unified inference for nonlinear factor models from panels with fixed and large time span," Journal of Econometrics, Elsevier, vol. 212(1), pages 4-25.
    16. Jesus M. Carro & Alejandra Traferri, 2014. "State Dependence And Heterogeneity In Health Using A Bias‐Corrected Fixed‐Effects Estimator," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(2), pages 181-207, March.
    17. Pakel, Cavit, 2019. "Bias reduction in nonlinear and dynamic panels in the presence of cross-section dependence," Journal of Econometrics, Elsevier, vol. 213(2), pages 459-492.
    18. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers 31/17, Institute for Fiscal Studies.
    19. repec:hal:spmain:info:hdl:2441/eu4vqp9ompqllr09j0031f620 is not listed on IDEAS
    20. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," Department of Economics Working Papers 2020-09, McMaster University.
    21. Ole E. Barndorff-Nielsen & Silja Kinnebrock & Neil Shephard, 2008. "Measuring downside risk - realised semivariance," OFRC Working Papers Series 2008fe01, Oxford Financial Research Centre.
    22. Fernández-Val, Iván & Savchenko, Yevgeniya & Vella, Francis, 2017. "Evaluating the role of income, state dependence and individual specific heterogeneity in the determination of subjective health assessments," Economics & Human Biology, Elsevier, vol. 25(C), pages 85-98.
    23. St'ephane Bonhomme & Martin Weidner, 2018. "Minimizing Sensitivity to Model Misspecification," Papers 1807.02161, arXiv.org, revised Oct 2021.
    24. repec:spo:wpecon:info:hdl:2441/eu4vqp9ompqllr09ij4j0h0h1 is not listed on IDEAS
    25. Wang, Wuyi & Su, Liangjun, 2021. "Identifying latent group structures in nonlinear panels," Journal of Econometrics, Elsevier, vol. 220(2), pages 272-295.
    26. Bo E. Honor√ & Daniel Kaufmann & Sarah Lein, 2012. "Asymmetries in Price-Setting Behavior: New Microeconometric Evidence from Switzerland," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44, pages 211-236, December.
    27. De Lira Salvatierra, Irving & Patton, Andrew J., 2015. "Dynamic copula models and high frequency data," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 120-135.

    More about this item

    Keywords

    Google Trends (GT) data; COVID-19 forecasts; Panel data; Google Mobility Trends; SIR model; Quartic trend function; Bootstrap; XGBoost.;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:ebl:ecbull:eb-20-00405. 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: John P. Conley (email available below). General contact details of provider: .

    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.