Multi-regime Markov-switching models with time-varying transition probabilities: An application to U.S. Treasury yields

TL;DR

This paper extends Markov-switching models with time-varying transition probabilities to multiple regimes, develops an open-source R package, and applies it to U.S. Treasury yields, revealing insights into regime dynamics and model identification issues.

Contribution

It introduces a generalized K-regime MS model with TVTP, provides an open-source R package, and offers empirical insights into U.S. Treasury yield regimes.

Findings

Regime means, variances, and transition probabilities are reliably estimated.

GAS score coefficient is statistically non-identifiable due to likelihood ridge.

One-step forecasts are robust to TVTP misspecification, but regime probabilities are sensitive.

Abstract

This paper studies Markov-switching (MS) models with time-varying transition probabilities (TVTP) under various specifications of the transition probability matrix. Especially, we extend the two-regime common-variance setting of the Generalized Autoregressive Score (GAS) model from (Bazzi et al., 2017) to the general $K$-regime case with regime-specific means and variances. Our study contains comprehensive Monte Carlo simulations and we developed an open-source R package, \texttt{multiregimeTVTP}, for data simulation and parameter estimation. We find that the regime means, variances, and transition probabilities are reliably recovered, whereas the TVTP driving coefficients are harder to identify. Another finding from our paper is that the GAS score coefficient appears to be statistically non-identifiable, due to a ridge in the joint likelihood surface $(\sigma^2,A)$. In addition, we find that one-step point forecasts are remarkably robust to TVTP misspecification, but filtered regime probabilities are not, so correct specification matters most for characterizing regime dynamics rather than short-horizon forecasting. An empirical application to U.S. Treasury zero-coupon yield changes at four maturities (1961-2024) shows that an exogenous specification driven by the lagged yield level dominates the constant and lagged-change models in fit, while the GAS specification fails to converge, with $\hat{A}$ collapsing to zero, reflecting the same identifiability issue observed in simulation.