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

Author

Listed:
  • Laurent Ferrara

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Anna Simoni

Abstract

We analyse whether, and when, a large set of Google search data can be useful to increase GDP nowcasting accuracy once we control for information contained in official variables. We put forward a new approach that combines variable pre-selection and Ridge regularization and we provide theoretical results on the asymptotic behaviour of the estimator. Empirical results on the euro area show that Google data convey useful information for pseudo-real-time nowcasting of GDP growth during 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, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers hal-04159714, HAL.
    • Handle: RePEc:hal:wpaper:hal-04159714
    Note: View the original document on HAL open archive server: https://hal.science/hal-04159714
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    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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