Machine Learning and the Yield Curve:Tree-Based Macroeconomic Regime Switching

Siyu Bie (), Francis Diebold, Jingyu He () and Junye Li
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Siyu Bie: City University of Hong Kong
Jingyu He: City University of Hong Kong
Junye Li: Fudan University

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: We explore tree-based macroeconomic regime-switching in the context of the dynamic Nelson-Siegel (DNS) yield-curve model. In particular, we customize the treegrowing algorithm to partition macroeconomic variables based on the DNS model’s marginal likelihood, thereby identifying regime-shifting patterns in the yield curve. Compared to traditional Markov-switching models, our model offers clear economic interpretation via macroeconomic linkages and ensures computational simplicity. In an empirical application to U.S. Treasury bond yields, we find (1) important yield curve regime switching, and (2) evidence that macroeconomic variables have predictive power for the yield curve when the short rate is high, but not in other regimes, thereby refining the notion of yield curve “macro-spanning”.

Keywords: Decision Tree; Macro-Finance; Term Structure; Regime Switching; Dynamic Nelson-Siegel Model; Bayesian Estimation (search for similar items in EconPapers)
JEL-codes: C11 E43 G12 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2024-10-08
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp and nep-ets
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Working Paper: Machine Learning and the Yield Curve: Tree-Based Macroeconomic Regime Switching (2024)
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