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

Author

Listed:
  • Laurent Ferrara

    (Banque de France)

  • Anna Simoni

    (CREST; CNRS.)

Abstract

Nowcasting GDP growth is extremely useful for policy-makers to assess macroe-conomic 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 anal-ysis 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 2019-04, Center for Research in Economics and Statistics.
  • Handle: RePEc:crs:wpaper:2019-04
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    More about this item

    Keywords

    Nowcasting; Big data; Google search 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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