Machine-learning Growth at Risk

TL;DR

This paper introduces a machine-learning approach to analyze growth vulnerabilities in the US, identifying key sectors influencing downside risk and how their importance varies over time.

Contribution

It applies a selection-based machine-learning method to decompose and predict downside growth risks across sectors, offering new insights into economic vulnerabilities.

Findings

Financial, labour-market, and housing variables drive downside risk.

Sector-specific indices can predict downside risk independently.

Importance of different sectors changes over time.

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.