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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yawen Fan & Xiaohui Liu & Yang Cao & Shaochu Liu, 2024. "Jackknife empirical likelihood based diagnostic checking for Ar(p) models," Computational Statistics, Springer, vol. 39(5), pages 2479-2509, July.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:5:d:10.1007_s00180-023-01385-x
    DOI: 10.1007/s00180-023-01385-x
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    References listed on IDEAS

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