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Predicting Recessions in Real-Time: Mining Google Trends and Electronic Payments Data for Clues

Author

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  • Greg Tkacz

    (St. Francis Xavier University)

Abstract

Many official economic indicators are released with a time lag, released infrequently and often require revision. In this Commentary, I discuss new sources of electronically recorded data that are both timely and reflect the real-time intentions of millions (or billions) of agents. Specifically, I consider whether Google searches and the growth of electronic payments variables, such as debit and credit card transactions, would have predicted the 2008 – 2009 recession. Not too long ago, Canadian empirical macroeconomic researchers would have to wait two months for the release of the monthly National Accounts in order to update their models and forecasts. However, in the last 10 years to 20 years, technological innovations have resulted in vast amounts of other data being recorded electronically and stored. New data series are now being generated at a rate faster than analysts can study them. Due to the emergence of Google as the dominant search engine, its search-term usage can provide a snapshot of current group interests in numerous issues, such as economics, politics, health, etc. In principle, if many people are entering the same economic search terms, this could provide a clue about changing conditions, such as the onset of a recession. I find that the usage of Google search terms “recession” and “jobs” could have predicted the recession up to three months in advance of its onset. However, since Google query data are only available from 2004, the time span studied in this Commentary is very short in the context of business cycles. Consequently, our study should be viewed as illustrative of the potential uses of electronic data. I also highlight the benefits and pitfalls that users of Google data may encounter in the context of economic monitoring.

Suggested Citation

  • Greg Tkacz, 2013. "Predicting Recessions in Real-Time: Mining Google Trends and Electronic Payments Data for Clues," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 387, September.
  • Handle: RePEc:cdh:commen:387
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    References listed on IDEAS

    as
    1. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    2. Greg Tkacz, 2010. "An Uncertain Past: Data Revisions and Monetary Policy in Canada," Bank of Canada Review, Bank of Canada, vol. 2010(Spring), pages 41-51.
    3. Estrella, Arturo, 1998. "A New Measure of Fit for Equations with Dichotomous Dependent Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 198-205, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Pete Richardson, 2018. "Nowcasting and the Use of Big Data in Short-Term Macroeconomic Forecasting: A Critical Review," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 65-87.
    2. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    3. Enrique López Enciso, 2019. "Dos tradiciones en la medición del ciclo: historia general y desarrollos en Colombia," Tiempo y Economía, Universidad de Bogotá Jorge Tadeo Lozano, vol. 6(1), pages 77-142, February.
    4. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    5. Diego Bodas & Juan Ramon Garcia & Juan Murillo & Matias Pacce & Tomasa Rodrigo & Juan de Dios Romero & Pep Ruiz & Camilo Ulloa & Heribert Valero, 2018. "Measuring Retail Trade Using Card Transactional Data," Working Papers 18/03, BBVA Bank, Economic Research Department.
    6. Chauvet, Marcelle & Gabriel, Stuart & Lutz, Chandler, 2016. "Mortgage default risk: New evidence from internet search queries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 91-111.
    7. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    8. Enrique A. López-Enciso, 2017. "Dos tradiciones en la medición del ciclo: historia general y desarrollos en Colombia," Borradores de Economia 986, Banco de la Republica de Colombia.

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    More about this item

    Keywords

    Google; search engine; Financial Services; Macroeconomic Indicators;
    All these keywords.

    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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