Machine Learning the Macroeconomic Effects of Financial Shocks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929622789128192 |
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| author | Hauzenberger, Niko Huber, Florian Klieber, Karin Marcellino, Massimiliano |
| author_facet | Hauzenberger, Niko Huber, Florian Klieber, Karin Marcellino, Massimiliano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07649 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning the Macroeconomic Effects of Financial Shocks Hauzenberger, Niko Huber, Florian Klieber, Karin Marcellino, Massimiliano Econometrics 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. |
| title | Machine Learning the Macroeconomic Effects of Financial Shocks |
| topic | Econometrics |
| url | https://arxiv.org/abs/2412.07649 |