Machine learning the macroeconomic effects of financial shocks

C-Tier
Journal: Economics Letters
Year: 2025
Volume: 250
Issue: C

Score contribution per author:

0.251 = (α=2.01 / 4 authors) × 0.5x C-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:eee:ecolet:v:250:y:2025:i:c:s0165176525000977
Journal Field
General
Author Count
4
Added to Database
2026-01-25