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Machine learning the macroeconomic effects of financial shocks

Author

Listed:
  • Hauzenberger, Niko
  • Huber, Florian
  • Klieber, Karin
  • Marcellino, Massimiliano

Abstract

We propose a method to learn the nonlinear impulse responses to structural shocks using neural networks, and apply it to uncover the effects of US financial shocks. The results reveal substantial asymmetries with respect to the sign of the shock. Adverse financial shocks have powerful effects on the US economy, while benign shocks trigger much smaller reactions. Instead, with respect to the size of the shocks, we find no discernible asymmetries.

Suggested Citation

  • Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Machine learning the macroeconomic effects of financial shocks," Economics Letters, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:ecolet:v:250:y:2025:i:c:s0165176525000977
    DOI: 10.1016/j.econlet.2025.112260
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    Keywords

    Bayesian neural networks; Nonlinear local projections; Financial shocks; Asymmetric shock transmission;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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