Machine learning the macroeconomic effects of financial shocks

Niko Hauzenberger, Florian Huber, Karin Klieber and Massimiliano Marcellino

Economics Letters, 2025, vol. 250, issue C

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.

Keywords: Bayesian neural networks; Nonlinear local projections; Financial shocks; Asymmetric shock transmission (search for similar items in EconPapers)
JEL-codes: C11 C30 C45 E3 E44 (search for similar items in EconPapers)
Date: 2025
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Related works:
Working Paper: Machine Learning the Macroeconomic Effects of Financial Shocks (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:250:y:2025:i:c:s0165176525000977

DOI: 10.1016/j.econlet.2025.112260

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