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The 2007–2008 U.S. Recession: What Did The Real-Time Google Trends Data Tell The United States?

Author

Listed:
  • Tao Chen
  • Erin Pik Ki So
  • Liang Wu
  • Isabel Kit Ming Yan

Abstract

type="main" xml:id="coep12074-abs-0001"> In the extant literature of business cycle predictions, the signals for business cycle turning points are generally issued with a lag of at least 5 months. In this paper, we make use of a novel and timely indicator—the Google search volume data—to help to improve the timeliness of business cycle turning point identification. We identify multiple query terms to capture the real-time public concern on the aggregate economy, the credit market, and the labor market condition. We incorporate the query indices in a Markov-switching framework and successfully “nowcast” the peak date within a month that the turning occurred. (JEL E37, G17)

Suggested Citation

  • Tao Chen & Erin Pik Ki So & Liang Wu & Isabel Kit Ming Yan, 2015. "The 2007–2008 U.S. Recession: What Did The Real-Time Google Trends Data Tell The United States?," Contemporary Economic Policy, Western Economic Association International, vol. 33(2), pages 395-403, April.
  • Handle: RePEc:bla:coecpo:v:33:y:2015:i:2:p:395-403
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    File URL: http://hdl.handle.net/10.1111/coep.2015.33.issue-2
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    Citations

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    Cited by:

    1. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 2-12, April.
    2. Karolien Lenaerts & Miroslav Beblavý & Brian Fabo, 2016. "Prospects for utilisation of non-vacancy Internet data in labour market analysis—an overview," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-18, December.
    3. Fabo, B., 2017. "Towards an understanding of job matching using web data," Other publications TiSEM b8b877f2-ae6a-495f-b6cc-9, Tilburg University, School of Economics and Management.
    4. Stephen L. France & Yuying Shi, 2017. "Aggregating Google Trends: Multivariate Testing and Analysis," Papers 1712.03152, arXiv.org, revised Mar 2018.
    5. Maas, Benedikt, 2019. "Short-term forecasting of the US unemployment rate," MPRA Paper 94066, University Library of Munich, Germany.
    6. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).

    More about this item

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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