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Nowcasting of the U.S. unemployment rate using Google Trends

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  • Nagao, Shintaro
  • Takeda, Fumiko
  • Tanaka, Riku

Abstract

This study examines whether and how the search intensity data obtained from Google Trends contributes to nowcasting of the U.S. unemployment rate compared to the conventional AR model. Our assessment is motivated by two issues that may affect the validity of the forecast model using the Google search intensity. The first issue is the change in Google Trends specification that limits the period during which the search intensity data can be retrieved on weekly basis. The second issue is the potential change in the endpoint value of seasonally-adjusted series based on the timing of seasonal adjustment, which may generate a problem when running a real-time forecast. Our results show that the usage of Google Trends doesn't necessarily contribute to improving the accuracy of forecasts under some preconditions, suggesting that there is a limit to the method of adding the search intensity of single keyword to the forecast model.

Suggested Citation

  • Nagao, Shintaro & Takeda, Fumiko & Tanaka, Riku, 2019. "Nowcasting of the U.S. unemployment rate using Google Trends," Finance Research Letters, Elsevier, vol. 30(C), pages 103-109.
    • Handle: RePEc:eee:finlet:v:30:y:2019:i:c:p:103-109
    DOI: 10.1016/j.frl.2019.04.005
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    Cited by:

    1. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
    2. 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.
    3. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    4. Aldo Mascareño & Pablo A. Henríquez & Marco Billi & Gonzalo A. Ruz, 2020. "A Twitter-Lived Red Tide Crisis on Chiloé Island, Chile: What Can Be Obtained for Social-Ecological Research through Social Media Analysis?," Sustainability, MDPI, vol. 12(20), pages 1-38, October.
    5. Fernando Díaz & Pablo A Henríquez, 2021. "Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-29, July.
    6. Daekook Kang, 2021. "Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model," Electronic Commerce Research, Springer, vol. 21(1), pages 41-72, March.
    7. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
    8. Ramona ORĂȘTEAN & Silvia Cristina MĂRGINEAN & Raluca SAVA, 2024. "Exploring The Relationship Between Google Trends And Cryptocurrency Metrics," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 19(1), pages 368-379, April.
    9. Khaskheli, Asadullah & Zhang, Hongyu & Raza, Syed Ali & Khan, Komal Akram, 2022. "Assessing the influence of news indicator on volatility of precious metals prices through GARCH-MIDAS model: A comparative study of pre and during COVID-19 period," Resources Policy, Elsevier, vol. 79(C).
    10. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.
    11. Takumi Ito & Fumiko Takeda, 2022. "Do sentiment indices always improve the prediction accuracy of exchange rates?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 840-852, July.
    12. Başyiğit, Mikail, 2021. "Can Google Trends improve the marble demand model: A case study of USA's marble demand from Turkey," Resources Policy, Elsevier, vol. 72(C).
    13. Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Liviu Adrian Stoica, 2021. "Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    14. Muneeb Ahmad & Yousaf Ali Khan & Chonghui Jiang & Syed Jawad Haider Kazmi & Syed Zaheer Abbas, 2023. "The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 528-543, January.
    15. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Adewuyi, Adeolu, 2020. "Google trends and the predictability of precious metals," Resources Policy, Elsevier, vol. 65(C).
    16. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.

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