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The Proper Use of Google Trends in Forecasting Models

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  • Marcelo C. Medeiros
  • Henrique F. Pires

Abstract

It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts' accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, data and location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to overcome this obstacle.

Suggested Citation

  • Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
    • Handle: RePEc:arx:papers:2104.03065
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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. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
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    5. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
    6. 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.
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    8. 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.
    9. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
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    11. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
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    Cited by:

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    3. Cebrián, Eduardo & Domenech, Josep, 2024. "Addressing Google Trends inconsistencies," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    4. Ciner, Cetin & Lucey, Brian & Yarovaya, Larisa, 2022. "Determinants of cryptocurrency returns: A LASSO quantile regression approach," Finance Research Letters, Elsevier, vol. 49(C).
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    6. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    7. Lolić, Ivana & Matošec, Marina & Sorić, Petar, 2024. "DIY google trends indicators in social sciences: A methodological note," Technology in Society, Elsevier, vol. 77(C).
    8. Geri Skenderi & Christian Joppi & Matteo Denitto & Marco Cristani, 2024. "Well googled is half done: Multimodal forecasting of new fashion product sales with image‐based google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1982-1997, September.

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