A Partial Review on Testing for Change Points in Autoregressive Time Series Models

Mohamed Salah Eddine Arrouch (), Echarif Elharfaoui (), Mohamed-Amine Elaafani () and Sara Nejjam ()
Additional contact information
Mohamed Salah Eddine Arrouch: Chouaib Doukkali University
Echarif Elharfaoui: Chouaib Doukkali University
Mohamed-Amine Elaafani: Chouaib Doukkali University
Sara Nejjam: Chouaib Doukkali University

Methodology and Computing in Applied Probability, 2025, vol. 27, issue 3, 1-35

Abstract: Abstract This review discusses the detection of a single change-point in autoregressive models of order p. It begins by outlining parameter estimation. Subsequently, two common robust testing methods are considered: the Efficient Score Test (EST) and the Likelihood Ratio Test (LRT). The limiting distributions of the test statistics under the null hypothesis of no change, along with methods for pinpointing the location of the change-point, are presented. Both methods are backed by theoretical justifications. To illustrate the performance, a summary of a comprehensive simulation experiment under various change scenarios is included, confirming the convergence and performance of the discussed methods. Finally, an application of these techniques to real-world data, specifically analyzing changes in volatility is described. These findings are placed in context with recent algorithms in the literature, highlighting their comparative efficacy and reliability.

Keywords: Time series; Change-point; Score vector; Gaussian likelihood ratio; Mixing processes; 62F03; 62G10; 62F10; 62M10; 60G10; 60F17 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11009-025-10185-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:27:y:2025:i:3:d:10.1007_s11009-025-10185-3

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-025-10185-3

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().