Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/339977 
Year of Publication: 
2026
Citation: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 67 [Issue:] 2 [Article No.:] 40 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2026
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We consider a threshold regression model in a long-memory setting. A method for estimating the threshold parameter is proposed. Asymptotic results are derived. The asymptotic rate of convergence turns out to be slower than under weak dependence, but approaches the usual fast rate of Op(n-1)when the long-memory parameter of the residuals converges to zero. Furthermore, asymptotic inference for the difference of conditional means below and above an estimated threshold is considered. A statistic is defined to construct confidence intervals. Surprisingly, the rate of convergence of the statistic improves when nuisance parameters are estimated. An algorithm for constructing data driven confidence intervals is proposed. The results are illustrated by a small simulation study and an application to CBOE volumes and volatilities for S&P 500 index options.
Subjects: 
Threshold regression
Threshold estimation
Fast rate of convergence
Long memory
Nuisance parameters
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by 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.