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Econometrics of machine learning methods in economic forecasting

In: Handbook of Research Methods and Applications in Macroeconomic Forecasting

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  • Andrii Babii
  • Eric Ghysels
  • Jonas Striaukas

Abstract

Bringing together the recent advances and innovative methods in macroeconomic forecasting, this erudite Handbook outlines how to forecast, including following world events such as the Covid-19 pandemic and the global financial crisis. With contributions from global experts, chapters explore the use of machine-learning techniques, the value of social media data, and climate change forecasting. This title contains one or more Open Access chapters.

Suggested Citation

  • Andrii Babii & Eric Ghysels & Jonas Striaukas, 2024. "Econometrics of machine learning methods in economic forecasting," Chapters, in: Michael P. Clements & Ana Beatriz Galvão (ed.), Handbook of Research Methods and Applications in Macroeconomic Forecasting, chapter 10, pages 246-273, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:22222_10
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    References listed on IDEAS

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    11. Lahiri, Kajal & Yang, Cheng, 2022. "Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York," International Journal of Forecasting, Elsevier, vol. 38(2), pages 545-566.
    12. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
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    Economics and Finance; Research Methods;

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