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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.09839 |
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Table of Contents:
- This study develops the E-Rule, a novel composite recession indicator that integrates financial market and labor market signals to improve the precision of recession forecasting. Combining the yield curve and the Sahm rule, the E-Rule provides a holistic and early-warning measure of economic downturns. Using historical data from 1976 onward, we empirically evaluate the E-Rule's predictive power relative to traditional indicators. The analysis employs machine learning techniques, including logistic regression, support vector machines, gradient boosting, and random forests, to assess predictive accuracy. Our findings demonstrate that the E-Rule offers a superior lead time in forecasting recessions and improves stability over existing methods.