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Googling the present

Author

Listed:
  • Graeme Chamberlin

    (Office for National Statistics)

Abstract

SummaryGoogle Trends data provides weekly reports on the number of search queries made by people in a geographical area and by category. As over three quarters of those who access the Internet regularly are looking for information on goods and services ‐ this information may be a useful indicator of economic activity. For example, the volume of queries may relate to future patterns of spending. This article investigates this use of Google Trends data for various search categories, looking at its correlation with official data on retail sales, property transactions, car registrations and foreign trips.

Suggested Citation

  • Graeme Chamberlin, 2010. "Googling the present," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 4(12), pages 59-95, December.
    • Handle: RePEc:pal:ecolmr:v:4:y:2010:i:12:p:59-95
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    Citations

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    Cited by:

    1. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    2. Torsten Schmidt & Simeon Vosen, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 0382, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    3. Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.
    4. repec:zbw:rwirep:0382 is not listed on IDEAS
    5. Nymand-Andersen, Per & Pantelidis, Emmanouil, 2018. "Google econometrics: nowcasting euro area car sales and big data quality requirements," Statistics Paper Series 30, European Central Bank.
    6. 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.
    7. 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).
    8. Schmidt, Torsten & Vosen, Simeon, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 382, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    9. 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.
    10. 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.
    11. Javier Sebastian, 2016. "Blockchain in financial services: Regulatory landscape and future challenges," Working Papers 16/21, BBVA Bank, Economic Research Department.

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