Jackknife empirical likelihood based diagnostic checking for Ar(p) models

Yawen Fan, Xiaohui Liu (), Yang Cao and Shaochu Liu
Additional contact information
Yawen Fan: Jiangxi University of Finance and Economics
Xiaohui Liu: Jiangxi University of Finance and Economics
Yang Cao: Jiangxi University of Finance and Economics
Shaochu Liu: Jiangxi University of Finance and Economics

Computational Statistics, 2024, vol. 39, issue 5, No 4, 2479-2509

Abstract: Abstract Diagnostic checking is an important predefined step before using autoregressive models. Although many portmanteau tests were proposed for diagnostic checking, they still struggle with the issue of significant size distortion. In this paper, we develop new diagnostic checking methods based on jackknife empirical likelihood. It is demonstrated that the suggested testing statistics asymptotically have a typical chi-squared distribution. To verify the performance of the finite sample, some simulations are constructed. Additionally, a real example of five agricultural futures is provided to illustrate the merits of our diagnostic checking procedure.

Keywords: Autoregressive model; Diagnostic checking; Jackknife empirical likelihood; Data splitting (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-023-01385-x 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:compst:v:39:y:2024:i:5:d:10.1007_s00180-023-01385-x

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-023-01385-x

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().