Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323767 
Year of Publication: 
2025
Citation: 
[Journal:] Oxford Bulletin of Economics and Statistics [ISSN:] 1468-0084 [Volume:] 87 [Issue:] 4 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2025 [Pages:] 850-865
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
In this paper, we propose a two‐step procedure based on the group LASSO estimator in combination with a backward elimination algorithm to detect multiple structural breaks in linear regressions with multivariate responses. Applying the two‐step estimator, we jointly detect the number and location of structural breaks and provide consistent estimates of the coefficients. Our framework is flexible enough to allow for a mix of integrated and stationary regressors, as well as deterministic terms. Using simulation experiments, we show that the proposed two‐step estimator performs competitively against the likelihood‐based approach in finite samples. However, the two‐step estimator is computationally much more efficient. An economic application to the identification of structural breaks in the term structure of interest rates illustrates this methodology.
Subjects: 
cointegration
LASSO
model selection
multivariate
shrinkage
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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