Affiliation:
1. Peking University HSBC Business School Shenzhen China
2. Department of Economics Korea University Seoul Republic of Korea
3. School of Economics Sogang University Seoul Republic of Korea
Abstract
AbstractThe literature on using yield curves to forecast recessions customarily uses 10‐year–3‐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–3‐month spread.
Funder
National Research Foundation of Korea
Subject
Management Science and Operations Research,Statistics, Probability and Uncertainty,Strategy and Management,Computer Science Applications,Modeling and Simulation,Economics and Econometrics
Reference30 articles.
1. Bauer M. D. &Mertens T. M.(2018a).Economic Forecasts with the Yield Curve. (2018‐07): Federal Reserve Bank of San Franciscohttps://www.frbsf.org/economic-research/publications/economic-letter/2018/march/economic-forecasts-with-yield-curve/
2. Bauer M. D. &Mertens T. M.(2018b).Information in the Yield Curve about Future Recessions. (2018‐20): Federal Reserve Bank of San Francisco https://www.frbsf.org/economic‐research/publications/economic‐letter/2018/august/information‐in‐yield‐curve‐about‐future‐recessions/
3. Evaluating the Classification of Economic Activity into Recessions and Expansions
4. The use of the area under the ROC curve in the evaluation of machine learning algorithms
5. Optimal Versus Naive Diversification: How Inefficient is the 1/NPortfolio Strategy?
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