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Data Preselection in Machine Learning Methods: An Application to Macroeconomic Nowcasting with Google Search Data

Author

Listed:
  • Agostino Capponi
  • Charles-Albert Lehalle
  • Anna Simoni

    (CNRS - Centre National de la Recherche Scientifique)

  • Laurent Ferrara

    (SKEMA Business School - SKEMA Business School)

Abstract

In this chapter, we present some Machine Learning (ML) econometric methods that allows to conveniently exploit Google search data for macroeconomic nowcast. In particular, we focus on the issue of variables preselection among a large set of Google search categories before entering them into ML approaches in order to nowcast macroeconomic variables. We consider two ML approaches allowing to estimate linear regression models starting from large information sets: a factor extraction and a Ridge regularisation. As an application we consider euro area GDP growth nowcasting using weekly Google Search data. Empirical results tend to suggest that estimating a Ridge regression associated with an ex ante preselection procedure appears as a pertinent strategy in terms of nowcasting accuracy.

Suggested Citation

  • Agostino Capponi & Charles-Albert Lehalle & Anna Simoni & Laurent Ferrara, 2023. "Data Preselection in Machine Learning Methods: An Application to Macroeconomic Nowcasting with Google Search Data," Post-Print hal-04369062, HAL.
    • Handle: RePEc:hal:journl:hal-04369062
    DOI: 10.1017/9781009028943.026
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