Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00572 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916770550382592 |
|---|---|
| author | Adrian, Tobias Chen, Hongqi Dovì, Max-Sebastian Lee, Ji Hyung |
| author_facet | Adrian, Tobias Chen, Hongqi Dovì, Max-Sebastian Lee, Ji Hyung |
| contents | We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk is primarily driven by financial, labour-market, and housing variables, with their importance changing over time. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors, thereby isolating the downside risks emanating from each sector. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00572 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-learning Growth at Risk Adrian, Tobias Chen, Hongqi Dovì, Max-Sebastian Lee, Ji Hyung General Economics Economics We analyse growth vulnerabilities in the US using quantile partial correlation regression, a selection-based machine-learning method that achieves model selection consistency under time series. We find that downside risk is primarily driven by financial, labour-market, and housing variables, with their importance changing over time. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors, thereby isolating the downside risks emanating from each sector. |
| title | Machine-learning Growth at Risk |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2506.00572 |