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Google econometrics: nowcasting euro area car sales and big data quality requirements

Author

Listed:
  • Nymand-Andersen, Per
  • Pantelidis, Emmanouil

Abstract

Big data” is becoming an increasingly important aspect of our daily lives as the digital sources of information and intelligence that it encompasses become more structured and more publicly available. These sources may enable the generation of new datasets providing high-frequency and timely insights into unconscious digital behaviour and the consequent actions of economic agents, which may, in turn, assist in the generation of early indicators of economic and financial trends and activities. This paper examines the usefulness of Google search data in nowcasting euro area car sales, as a leading macroeconomic indicator, and considers the quality requirements for using these new data sources as a toolkit for sound decision and policy making. The paper finds that, while Google data may have predictive capabilities for nowcasting euro area car sales, further quality improvements in the data source are needed in order to move beyond experimental statistics. If these quality requirements can be met, the resulting advances in theory and knowledge around interpreting big data can be expected to significantly re-shape how we think about and explain both behaviour and complex socio-economic phenomena. JEL Classification: C53, C82, E58, E71

Suggested Citation

  • 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.
    • Handle: RePEc:ecb:ecbsps:201830
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    File URL: https://www.ecb.europa.eu//pub/pdf/scpsps/ecb.sps30.en.pdf
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    References listed on IDEAS

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    7. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    8. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
    9. Mohamed Arouri & Amal Aouadi & Philippe Foulquier & Frédéric Teulon, 2013. "Can Information Demand Help to Predict Stock Market Liquidity ? Google it !," Working Papers 2013-24, Department of Research, Ipag Business School.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Giulio Cornelli & Sebastian Doerr & Leonardo Gambacorta & Bruno Tissot, 2022. "Big Data in Asian Central Banks," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(2), pages 255-269, July.
    2. 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.
    3. Alexander Jung & Patrick Kuehl, 2021. "Can central bank communication help to stabilise inflation expectations?," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(3), pages 298-321, July.
    4. Kakuho Furukawa & Ryohei Hisano, 2022. "A Nowcasting Model of Exports Using Maritime Big Data," Bank of Japan Working Paper Series 22-E-19, Bank of Japan.
    5. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    6. Jung, Alexander, 2023. "Are monetary policy shocks causal to bank health? Evidence from the euro area," Journal of Macroeconomics, Elsevier, vol. 75(C).

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    More about this item

    Keywords

    big data; google internet search; modelling; nowcasting; quality; statistics; vector auto regression;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • E71 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy

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