Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/342030 
Year of Publication: 
2025
Citation: 
[Journal:] AStA Advances in Statistical Analysis [ISSN:] 1863-818X [Volume:] 110 [Issue:] 2 [Publisher:] Springer Berlin Heidelberg [Place:] Berlin/Heidelberg [Year:] 2025 [Pages:] 371-406
Publisher: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Abstract: 
This article proposes a new method for estimating regime-switching models when some slope parameters are constrained to be non-switching. The constrained parameters are simply found by an appropriate weighted average of the unconstrained parameters. The advantage of this approach is twofold. First, the constrained estimates are obtained by an unconstrained estimation procedure such as the EM algorithm, and hence, the procedure is relatively straightforward to implement. Secondly, as both the constrained and unconstrained estimators are available, testing based on the likelihood ratio and model selection by means of likelihood-based information criteria is particularly simple. The procedure is applied to a three-state Markov-switching variance model.
Subjects: 
Constrained estimation
Regime-switching model
EM algorithm
ML-based tests
ML-based information criteria
Persistent Identifier of the first edition: 
Additional Information: 
C12;C13;C24
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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