Machine-learning Growth at Risk
General Economics
2025-06-03 v1 Economics
Abstract
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.
Keywords
Cite
@article{arxiv.2506.00572,
title = {Machine-learning Growth at Risk},
author = {Tobias Adrian and Hongqi Chen and Max-Sebastian Dovì and Ji Hyung Lee},
journal= {arXiv preprint arXiv:2506.00572},
year = {2025}
}