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Constructing Consumer Sentiment Index for U.S. Using Google Searches

Author

Listed:
  • Della Penna, Nicolas

    (Unknown)

  • Huang, Haifang

    (University of Alberta, Department of Economics)

Abstract

We construct a consumer sentiment index for the U.S. using the popularity trends of selected Google searches. The final index consists of four components and is highly correlated with the Index of Consumer Sentiment from the University of Michigan and the Consumer Confidence Index from the Conference Board. Among the three sentiment indices, the Google search-based index (SBI) leads in time and predicts other indices. In terms of forecasting consumer spending, the SBI outperforms both the ICS and the CCI and provides independent information. For robustness, we use multiple measures of consumer spending and a range of statistical specifications. The finding is robust.

Suggested Citation

  • Della Penna, Nicolas & Huang, Haifang, 2009. "Constructing Consumer Sentiment Index for U.S. Using Google Searches," Working Papers 2009-26, University of Alberta, Department of Economics, revised 01 Feb 2010.
    • Handle: RePEc:ris:albaec:2009_026
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    File URL: https://sites.ualberta.ca/~econwps/2009/wp2009-26.pdf
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    Citations

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    Cited by:

    1. Joaquín Artés & Ana Melissa Botello Mainieri & A. Jesús Sánchez-Fuentes, 2019. "Tax reforms and Google searches: the case of Spanish VAT reforms during the great recession," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(3), pages 321-336, November.
    2. Yan Carrière‐Swallow & Felipe Labbé, 2013. "Nowcasting with Google Trends in an Emerging Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 289-298, July.
    3. Chiu, Peng-Chia & Teoh, Siew Hong & Zhang, Yinglei & Huang, Xuan, 2023. "Using Google searches of firm products to detect revenue management," Accounting, Organizations and Society, Elsevier, vol. 109(C).
    4. Briggs, Justin Thomas & Tabarrok, Alexander, 2014. "Firearms and suicides in US states," International Review of Law and Economics, Elsevier, vol. 37(C), pages 180-188.
    5. Siliverstovs, Boriss & Wochner, Daniel S., 2018. "Google Trends and reality: Do the proportions match?," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 1-23.
    6. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    7. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
    8. Paul Gift, 2020. "Moving the Needle in MMA: On the Marginal Revenue Product of UFC Fighters," Journal of Sports Economics, , vol. 21(2), pages 176-209, February.
    9. Florian Schaffner, 2015. "Predicting US bank failures with internet search volume data," ECON - Working Papers 214, Department of Economics - University of Zurich.
    10. Gutiérrez, Antonio, 2022. "Movilidad urbana y datos de alta frecuencia [Urban mobility and high frequency data]," MPRA Paper 114854, University Library of Munich, Germany.

    More about this item

    Keywords

    consumer sentiment; consumer confidence; leading economic indicators;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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