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.

Machine learning the macroeconomic effects of financial shocks

Marcellino, Massimiliano
2025

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.
2025
2025
Hauzenberger, Niko; Huber, Florian; Klieber, Karin; Marcellino, Massimiliano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4082019
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