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Obtaining consistent time series from Google Trends

Author

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  • Vera Z. Eichenauer
  • Ronald Indergand
  • Isabel Z. Martínez
  • Christoph Sax

Abstract

Google Trends data are a popular data source for research, but raw data are frequency‐inconsistent: daily data fail to capture long‐run trends. This issue has gone unnoticed in the literature. In addition, sampling noise can be substantial. We develop a procedure (available in an R‐package), which solves both issues at once. We apply this procedure to construct long‐run, frequency‐consistent daily economic indices for three German‐speaking countries. The resulting indices are significantly correlated with traditional leading economic indicators while being available in real time. We discuss potential applications across disciplines and spanning well beyond business cycle analysis.

Suggested Citation

  • Vera Z. Eichenauer & Ronald Indergand & Isabel Z. Martínez & Christoph Sax, 2022. "Obtaining consistent time series from Google Trends," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 694-705, April.
    • Handle: RePEc:bla:ecinqu:v:60:y:2022:i:2:p:694-705
    DOI: 10.1111/ecin.13049
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    References listed on IDEAS

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    1. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    2. Castelnuovo, Efrem & Tran, Trung Duc, 2017. "Google It Up! A Google Trends-based Uncertainty index for the United States and Australia," Economics Letters, Elsevier, vol. 161(C), pages 149-153.
    3. C. Douglas Swearingen & Joseph T. Ripberger, 2014. "Google Insights and U.S. Senate Elections: Does Search Traffic Provide a Valid Measure of Public Attention to Political Candidates?," Social Science Quarterly, Southwestern Social Science Association, vol. 95(3), pages 882-893, September.
    4. Lourenço, Nuno & Rua, António, 2021. "The Daily Economic Indicator: tracking economic activity daily during the lockdown," Economic Modelling, Elsevier, vol. 100(C).
    5. 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.
    6. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    8. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    9. 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.
    10. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    11. Pierre Magontier, 2020. "Does media coverage affect governments preparation for natural disasters?," Diskussionsschriften credresearchpaper29, Universitaet Bern, Departement Volkswirtschaft - CRED.
    12. Thomas Eisensee & David Strömberg, 2007. "News Droughts, News Floods, and U. S. Disaster Relief," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(2), pages 693-728.
    13. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    14. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    15. Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    16. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    17. Mr. Futoshi Narita & Rujun Yin, 2018. "In Search of Information: Use of Google Trends’ Data to Narrow Information Gaps for Low-income Developing Countries," IMF Working Papers 2018/286, International Monetary Fund.
    18. Jaemin Woo & Ann L. Owen, 2019. "Forecasting private consumption with Google Trends data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(2), pages 81-91, March.
    19. Jun, Seung-Pyo & Yoo, Hyoung Sun & Choi, San, 2018. "Ten years of research change using Google Trends: From the perspective of big data utilizations and applications," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 69-87.
    20. Klaus Abberger & Michael Graff & Boriss Siliverstovs & Jan-Egbert Sturm, 2014. "The KOF Economic Barometer, Version 2014," KOF Working papers 14-353, KOF Swiss Economic Institute, ETH Zurich.
    21. Klaus Neusser, 2016. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-32862-1, June.
    22. Kostopoulos, Dimitrios & Meyer, Steffen & Uhr, Charline, 2020. "Google search volume and individual investor trading," Journal of Financial Markets, Elsevier, vol. 49(C).
    23. Paul Smith, 2016. "Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 263-284, April.
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    4. Laine, Olli-Matti & Nelimarkka, Jaakko, 2023. "Assessing targeted longer-term refinancing operations: Identification through search intensity," Bank of Finland Research Discussion Papers 13/2023, Bank of Finland.
    5. Jung, Alexander, 2023. "Are monetary policy shocks causal to bank health? Evidence from the euro area," Journal of Macroeconomics, Elsevier, vol. 75(C).

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