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When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage

Author

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  • Laurent Ferrara
  • Anna Simoni

Abstract

Nowcasting GDP growth is extremely useful for policy-makers to assess macroeconomic conditions in real-time. In this paper, we aim at nowcasting euro area GDP with a large database of Google search data. Our objective is to check whether this specific type of information can be useful to increase GDP nowcasting accuracy, and when, once we control for official variables. In this respect, we estimate shrunk bridge regressions that integrate Google data optimally screened through a targeting method, and we empirically show that this approach provides some gain in pseudo-real-time nowcasting of euro area GDP quarterly growth. Especially, we get that Google data bring useful information for GDP nowcasting for the four first weeks of the quarter when macroeconomic information is lacking. However, as soon as official data become available, their relative nowcasting power vanishes. In addition, a true real-time analysis confirms that Google data constitute a reliable alternative when official data are lacking.

Suggested Citation

  • Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers 717, Banque de France.
  • Handle: RePEc:bfr:banfra:717
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    2. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2023. "Testing big data in a big crisis: Nowcasting under Covid-19," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1548-1563.
    3. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
    4. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    5. Maria Elena Bontempi & Michele Frigeri & Roberto Golinelli & Matteo Squadrani, 2021. "EURQ: A New Web Search‐based Uncertainty Index," Economica, London School of Economics and Political Science, vol. 88(352), pages 969-1015, October.
    6. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    7. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023. "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers 1385, Board of Governors of the Federal Reserve System (U.S.).
    8. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    9. Michael Anthonisz, 2023. "Nowcasting Key Australian Macroeconomic Variables," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 56(3), pages 371-380, September.
    10. Dan Anderberg & Helmut Rainer & Fabian Siuda, 2022. "Quantifying domestic violence in times of crisis: An internet search activity‐based measure for the COVID‐19 pandemic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 498-518, April.
    11. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    12. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
    13. 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.
    14. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    15. Tea Livaic & Ana Perisic, 2019. "What can Google Tell us about Bitcoin Trading Volume in Croatia? Evidence from the Online Marketplace Localbitcoins," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 17(4), pages 707-715.
    16. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    17. Marcelo C. Medeiros & Henrique F. Pires, 2021. "The Proper Use of Google Trends in Forecasting Models," Papers 2104.03065, arXiv.org, revised Apr 2021.
    18. 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.
    19. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.
    20. Jad Beyhum & Jonas Striaukas, 2023. "Sparse plus dense MIDAS regressions and nowcasting during the COVID pandemic," Papers 2306.13362, arXiv.org, revised Dec 2023.
    21. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    22. Valentin BURCA, 2020. "Earnings Quality Versus Accounting Regulation. Empirical Assesment On Accuracy Of Macroeconomic Estimates," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 72-87, November.
    23. 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.
    24. Emilio Blanco & Fiorella Dogliolo & Lorena Garegnani, 2022. "Nowcasting during the Pandemic: Lessons from Argentina," BCRA Working Paper Series 202299, Central Bank of Argentina, Economic Research Department.

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    More about this item

    Keywords

    Nowcasting; Big data; Sure Independence Screening; Ridge Regularization.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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