IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v38y2019i2p81-91.html
   My bibliography  Save this article

Forecasting private consumption with Google Trends data

Author

Listed:
  • Jaemin Woo
  • Ann L. Owen

Abstract

This paper examines the predictive relationship of consumption‐related and news‐related Google Trends data to changes in private consumption in the USA. The results suggest that (1) Google Trends‐augmented models provide additional information about consumption over and above survey‐based consumer sentiment indicators, (2) consumption‐related Google Trends data provide information about pre‐consumption research trends, (3) news‐related Google Trends data provide information about changes in durable goods consumption, and (4) the combination of news and consumption‐related data significantly improves forecasting models. We demonstrate that applying these insights improves forecasts of private consumption growth over forecasts that do not utilize Google Trends data and over forecasts that use Google Trends data, but do not take into account the specific ways in which it informs forecasts.

Suggested Citation

  • 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.
  • Handle: RePEc:wly:jforec:v:38:y:2019:i:2:p:81-91
    DOI: 10.1002/for.2559
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2559
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.2559?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. 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.
    2. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    3. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    4. Jiam Song & Kwangmin Jung & Jonghun Kam, 2023. "Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    5. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    6. 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.
    7. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    8. Karaman Örsal, Deniz Dilan, 2021. "Onlinedaten und Konsumentscheidungen: Voraussagen anhand von Daten aus Social Media und Suchmaschinen," Edition HWWI: Chapters, in: Straubhaar, Thomas (ed.), Neuvermessung der Datenökonomie, volume 6, pages 157-172, Hamburg Institute of International Economics (HWWI).
    9. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    10. Fajar, Muhammad & Prasetyo, Octavia Rizky & Nonalisa, Septiarida & Wahyudi, Wahyudi, 2020. "Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia)," MPRA Paper 105042, University Library of Munich, Germany, revised 30 Nov 2020.
    11. Diaz-Balteiro, L. & Alfranca, O. & Voces, R. & Soliño, M., 2023. "Using google search patterns to explain the demand for wild edible mushrooms," Forest Policy and Economics, Elsevier, vol. 152(C).
    12. Rik Chakraborti & Gavin Roberts, 2020. "Anti-Gouging Laws, Shortages, and COVID-19: Insights from Consumer Searches," Journal of Private Enterprise, The Association of Private Enterprise Education, vol. 35(Winter 20), pages 1-20.
    13. Di Wu & Zhenning Xu & Seung Bach, 2023. "Using Google Trends to predict and forecast avocado sales," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 629-641, December.

    More about this item

    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:wly:jforec:v:38:y:2019:i:2:p:81-91. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.