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This research aims to enhance testing methods for Markov switching models, focusing on regime shifts and identification issues.

July 10, 2026Open Access

Monte Carlo Likelihood-Ratio Tests for Markov Switching Models

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Randomized trial examines regime shifts using Monte Carlo likelihood-ratio tests, suggesting improved identification methods.

This research aims to enhance testing methods for Markov switching models, focusing on regime shifts and identification issues.
Developed Monte Carlo likelihood-ratio tests for M0 versus M0 + m regimes for M0 >= 1 and m >= 1.
Applied techniques to nonstationary processes and non-Gaussian errors, accommodating multivariate models.
Introduced the Maximized Monte Carlo likelihood-ratio test (MMC-LRT) for robust identification.
The MMC-LRT demonstrates both finite-sample and asymptotic validity for regime testing.
Proposed tests effectively address issues with non-standard asymptotic distributions and nuisance parameters.
Results indicate that Monte Carlo methods can robustly detect regime shifts in complex models.

Rodriguez‐Rondon et al. (2026) studied this question.

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