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Machine-learning Growth at Risk

Author

Listed:
  • Tobias Adrian
  • Hongqi Chen
  • Max-Sebastian Dov`i
  • Ji Hyung Lee

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.

Suggested Citation

  • Tobias Adrian & Hongqi Chen & Max-Sebastian Dov`i & Ji Hyung Lee, 2025. "Machine-learning Growth at Risk," Papers 2506.00572, arXiv.org.
    • Handle: RePEc:arx:papers:2506.00572
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    File URL: http://arxiv.org/pdf/2506.00572
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