Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/284831 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Time Series Analysis [ISSN:] 1467-9892 [Volume:] 43 [Issue:] 1 [Publisher:] John Wiley & Sons, Ltd [Place:] Oxford, UK [Year:] 2021 [Pages:] 83-104
Publisher: 
John Wiley & Sons, Ltd, Oxford, UK
Abstract: 
In this article, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long‐run US money demand function demonstrates the practical importance of this methodology.
Subjects: 
Adaptive group lasso
change‐points
cointegration
model selection
US money demand
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.