IDEAS home Printed from https://ideas.repec.org/a/ris/apltrx/0398.html
   My bibliography  Save this article

Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries

Author

Listed:
  • Fantazzini, Dean

    (Moscow School of Economics, Moscow State University, Moscow;)

Abstract

The ability of Google Trends data to forecast the number of new daily cases and deaths of COVID-19 is examined using a dataset of 158 countries. The analysis includes the computations of lag correlations between confirmed cases and Google data, Granger causality tests, and an out-of-sample forecasting exercise with 18 competing models with a forecast horizon of 14 days ahead. This evidence shows that Google-augmented models outperform the competing models for most of the countries. This is significant because Google data can complement epidemiological models during difficult times like the ongoing COVID-19 pandemic, when official statistics maybe not fully reliable and/or published with a delay. Moreover, real-time tracking with online-data is one of the instruments that can be used to keep the situation under control when national lockdowns are lifted and economies gradually reopen.

Suggested Citation

  • Fantazzini, Dean, 2020. "Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 33-54.
    • Handle: RePEc:ris:apltrx:0398
    as

    Download full text from publisher

    File URL: http://pe.cemi.rssi.ru/pe_2020_59_033-054.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Dean Fantazzini, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-27, November.
    2. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, June.
    3. Park, Joon Y. & Phillips, Peter C.B., 1989. "Statistical Inference in Regressions with Integrated Processes: Part 2," Econometric Theory, Cambridge University Press, vol. 5(1), pages 95-131, April.
    4. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    5. Nicholson, William B. & Matteson, David S. & Bien, Jacob, 2017. "VARX-L: Structured regularization for large vector autoregressions with exogenous variables," International Journal of Forecasting, Elsevier, vol. 33(3), pages 627-651.
    6. Martin S Eichenbaum & Sergio Rebelo & Mathias Trabandt, 2021. "The Macroeconomics of Epidemics [Economic activity and the spread of viral diseases: Evidence from high frequency data]," Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5149-5187.
    7. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    8. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    9. Cecilia de Almeida Marques-Toledo & Carolin Marlen Degener & Livia Vinhal & Giovanini Coelho & Wagner Meira & Claudia Torres Codeço & Mauro Martins Teixeira, 2017. "Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(7), pages 1-20, July.
    10. Hsiao, Cheng & Wan, Shui Ki, 2014. "Is there an optimal forecast combination?," Journal of Econometrics, Elsevier, vol. 178(P2), pages 294-309.
    11. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    12. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    13. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    14. Franses, Philip Hans & Paap, Richard, 2004. "Periodic Time Series Models," OUP Catalogue, Oxford University Press, number 9780199242030, Decembrie.
    15. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    16. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    17. Niels Haldrup & Peter Lildholdt, 2002. "On the Robustness of Unit Root Tests in the Presence of Double Unit Roots," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(2), pages 155-171, March.
    18. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    19. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    20. Maddala, G S & Wu, Shaowen, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 631-652, Special I.
    21. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    22. 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.
    23. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    24. Stock, James H. & Watson, Mark W., 1989. "Interpreting the evidence on money-income causality," Journal of Econometrics, Elsevier, vol. 40(1), pages 161-181, January.
    25. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    26. Yue Teng & Dehua Bi & Guigang Xie & Yuan Jin & Yong Huang & Baihan Lin & Xiaoping An & Dan Feng & Yigang Tong, 2017. "Dynamic Forecasting of Zika Epidemics Using Google Trends," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-10, January.
    27. R. Scott Hacker & Abdulnasser Hatemi-J, 2006. "Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application," Applied Economics, Taylor & Francis Journals, vol. 38(13), pages 1489-1500.
    28. Mauro Costantini & Claudio Lupi, 2013. "A Simple Panel-CADF Test for Unit Roots," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 276-296, April.
    29. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    30. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    31. David A Broniatowski & Michael J Paul & Mark Dredze, 2013. "National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    32. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    33. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    34. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Measurement

    Citations

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


    Cited by:

    1. Schneider, Tim & Meub, Lukas & Bizer, Kilian, 2021. "Consumer information in a market for expert services: Experimental evidence," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 94(C).
    2. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    3. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    4. Nelson Mileu & Nuno M. Costa & Eduarda M. Costa & André Alves, 2022. "Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data," Data, MDPI, vol. 7(8), pages 1-17, July.

    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. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, June.
    2. David Greasley & Les Oxley, 2010. "Cliometrics And Time Series Econometrics: Some Theory And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 24(5), pages 970-1042, December.
    3. Emirmahmutoglu, Furkan & Kose, Nezir, 2011. "Testing for Granger causality in heterogeneous mixed panels," Economic Modelling, Elsevier, vol. 28(3), pages 870-876, May.
    4. 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.
    5. Fahmida Khatun & Syed Yusuf Saadat, 2022. "The Optimum Level of Income Inequality in South Asia: An Econometric Analysis," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 23(1), pages 7-29, March.
    6. Asta Ndongo & Ibrahima Thione Diop, 2021. "Economic and Monetary Integration in ECOWAS Countries: A Panel VAR Approach to Identify Macroeconomic Shocks," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 7(2), pages 61-87, December.
    7. repec:zbw:rwirep:0557 is not listed on IDEAS
    8. Campo, Jacobo & Mendoza, Henry, 2018. "Gasto público y crecimiento económico: un análisis regional para Colombia, 1984-2012," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 88, pages 77-108, January.
    9. Akinlo Taiwo & Simon-Oke O. Olayemi, 2015. "Re-examine foreign direct investment and economic growth: Panel co-integration and causality tests for sub-Saharan African countries," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 18(1), pages 73-86, March.
    10. Fr餩ric Laurin, 2012. "Trade and regional growth in Spain: panel cointegration in a small sample," Applied Economics, Taylor & Francis Journals, vol. 44(4), pages 435-447, February.
    11. Santos, Carlos Filipe & Fuinhas, José Alberto & Marques, António Cardoso, 2014. "O nexus energia-crescimento e o nível da auto-suficiência na produção de petróleo: análise com macro painel [Energy-growth nexus and oil self-sufficiency: macro panel analysis]," MPRA Paper 57008, University Library of Munich, Germany.
    12. Pilbeam, K. & Litsios, I., 2015. "An Empirical Analysis of the Nexus between Investment, Fiscal Balances and Current Account Balances in Greece, Portugal and Spain," Working Papers 15/18, Department of Economics, City University London.
    13. Litsios, Ioannis & Pilbeam, Keith, 2017. "An empirical analysis of the nexus between investment, fiscal balances and current account balances in Greece, Portugal and Spain," Economic Modelling, Elsevier, vol. 63(C), pages 143-152.
    14. Costantini, Valeria & Martini, Chiara, 2010. "The causality between energy consumption and economic growth: A multi-sectoral analysis using non-stationary cointegrated panel data," Energy Economics, Elsevier, vol. 32(3), pages 591-603, May.
    15. Ärshiya Ämiri & Mikael Linden, 2016. "Income and total expenditure on health in OECD countries: Evidence from panel data and Hsiao's version of Granger non-causality tests," Economics and Business Letters, Oviedo University Press, vol. 5(1), pages 1-9.
    16. Jäger, Philipp & Schmidt, Torsten, 2016. "The political economy of public investment when population is aging: A panel cointegration analysis," European Journal of Political Economy, Elsevier, vol. 43(C), pages 145-158.
    17. McCarl, Bruce A. & Villavicencio, Xavier & Wu, Ximing, 2009. "The Effect of Climate Change over Agricultural Factor Productivity: Some Econometric Considerations," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49452, Agricultural and Applied Economics Association.
    18. Jacobo Campo & Henry Mendoza, 2018. "Public expenditure and economic growth: a regional analysis for Colombia, 1984-2012," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 88, pages 77-108, Enero - J.
    19. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911.
    20. Xie, Zixiong & Chen, Shyh-Wei, 2014. "Untangling the causal relationship between government budget and current account deficits in OECD countries: Evidence from bootstrap panel Granger causality," International Review of Economics & Finance, Elsevier, vol. 31(C), pages 95-104.
    21. Lin, Boqiang & Wesseh Jr., Presley K., 2014. "Energy consumption and economic growth in South Africa reexamined: A nonparametric testing apporach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 840-850.

    More about this item

    Keywords

    Covid-19; Google Trends; VAR; ARIMA; ARIMA-X; ETS; LASSO; SIR model.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I19 - Health, Education, and Welfare - - Health - - - Other

    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:ris:apltrx:0398. 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: Anatoly Peresetsky (email available below). General contact details of provider: http://appliedeconometrics.cemi.rssi.ru/ .

    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.