Yield Spread Selection in Predicting Recession Probabilities: A Machine Learning Approach

Jaehyuk Choi, Desheng Ge, Kyu Ho Kang and Sungbin Sohn

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Abstract: The literature on using yield curves to forecast recessions customarily uses 10-year--three-month Treasury yield spread without verification on the pair selection. This study investigates whether the predictive ability of spread can be improved by letting a machine learning algorithm identify the best maturity pair and coefficients. Our comprehensive analysis shows that, despite the likelihood gain, the machine learning approach does not significantly improve prediction, owing to the estimation error. This is robust to the forecasting horizon, control variable, sample period, and oversampling of the recession observations. Our finding supports the use of the 10-year--three-month spread.

Date: 2021-01, Revised 2022-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mac and nep-mon
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Published in Journal of Forecasting, 42(7): 1772-1785, 2023

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2101.09394

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