Niko Hauzenberger, Florian Huber, Karin Klieber, Massimiliano Marcellino
arXiv 10 Dec 2024 · Econometrics · publishedEconomics Letters (2025) · 4 citations (OpenAlex)
arXiv:2412.07649 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Barnichon et al (2022) Are the effects of financial market disruptions big or small? | 0.928 | 4 | 3 | 100% |
| 2 | Hauzenberger et al (2024) Bayesian neural networks for macroeconomic analysis | 0.874 | 5 | 2 | 100% |
| 3 | Mumtaz and Piffer (2022) Impulse response estimation via flexible local projections | 0.843 | 3 | 3 | 100% |
| gilchrist2012credit | unmatched citation key gilchrist2012credit | 0.737 | 3 | 2 | 100% |
| 5 | Brunnermeier and Sannikov (2014) A macroeconomic model with a financial sector | 0.644 | 2 | 2 | 100% |
| 6 | Kilian and Lütkepohl (2017) Structural vector autoregressive analysis | 0.511 | 2 | 1 | 100% |
| 7 | Balke (2000) Credit and economic activity: credit regimes and nonlinear propagation of shocks | 0.405 | 1 | 1 | 100% |
| 8 | Barnichon et al (2016) Theory ahead of measurement? assessing the nonlinear effects of financial market disruptions | 0.405 | 1 | 1 | 100% |
| 9 | Bhadra et al (2020) Horseshoe regularisation for machine learning in complex and deep models | 0.405 | 1 | 1 | 100% |
| 10 | Carvalho et al (2009) Handling sparsity via the horseshoe | 0.405 | 1 | 1 | 100% |
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