Machine Learning the Macroeconomic Effects of Financial Shocks

Autor:	Hauzenberger, Niko, Huber, Florian, Klieber, Karin, Marcellino, Massimiliano
Rok vydání:	2024
Předmět:	Economics - Econometrics
Druh dokumentu:	Working Paper
Popis:	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. Comment: JEL: C11, C30, C45, E3, E44. Keywords: Bayesian neural networks, nonlinear local projections, financial shocks, asymmetric shock transmission
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.07649 Zobrazit plný text záznamu View this record from Arxiv