Samuel Modée, Yushu Li, Sjur Westgaard, Stein Andreas Bethuelsen
arXiv 14 May 2026 · Statistics — Methodology
arXiv:2605.14976 · PDF · DOI · OpenAlex · Extracted main text
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, 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 $(σ^2,A)$. 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bazzi, Marco and Blasques, Francisco and Koopman, Siem Jan and Lucas… (2017) Time-varying transition probabilities for Markov regime switching models | 1.000 | 17 | 4 | 100% |
| 2 | Creal, Drew and Koopman, Siem Jan and Lucas, André (2013) Generalized autoregressive score models with applications | 1.000 | 6 | 3 | 100% |
| 3 | Diebold, Francis X and Lee, Joon-Haeng and Weinbach, Gretchen C (1994) Regime switching with time-varying transition probabilities | 0.928 | 4 | 3 | 100% |
| 4 | Hamilton, James D (1989) A new approach to the economic analysis of nonstationary time series and the business cycle | 0.737 | 3 | 2 | 100% |
| 5 | Hamilton, James D (1994) Time Series Analysis | 0.644 | 2 | 2 | 100% |
| 6 | Liu, Yan and Wu, Jing Cynthia (2021) Reconstructing the yield curve | 0.644 | 2 | 2 | 100% |
| 7 | Filardo, Andrew J (1994) Business-cycle phases and their transitional dynamics | 0.585 | 3 | 1 | 100% |
| 8 | Jushan Bai and Peng Wang (2011) Conditional Markov chain and its application in economic time series analysis | 0.405 | 1 | 1 | 100% |
| 9 | G. D. Berentsen and J. Bulla and A. Maruotti and Bård Støve (2022) Modelling clusters of corporate defaults: Regime‐switching models significantly reduce the contagion source | 0.405 | 1 | 1 | 100% |
| 10 | Kevin Berk and Andreas Hoffmann and A. Müller (2018) Probabilistic forecasting of industrial electricity load with regime switching behavior | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 23 scored citations.