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arXiv · 2506.00572

Machine-learning Growth at Risk

Tobias Adrian·Hongqi Chen·Max-Sebastian Dovì·Ji Hyung Lee
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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.

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Tobias Adrian, Hongqi Chen, Max-Sebastian Dovì, Ji Hyung Lee. 2025-05-31. Machine-learning Growth at Risk. https://arxiv.org/abs/2506.00572

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