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Shrinkage Regularization for (Non)Linear Serial Dependence Test

Author

Listed:
  • Francesco Giancaterini
  • Alain Hecq
  • Joann Jasiak
  • Aryan Manafi Neyazi

Abstract

This paper introduces a regularized test of the null hypothesis of the absence of linear and nonlinear serial dependence for high-dimensional non-Gaussian time series. Our approach extends the portmanteau test introduced in Jasiak and Neyazi (2023) to the high-dimensional setting.

Suggested Citation

  • Francesco Giancaterini & Alain Hecq & Joann Jasiak & Aryan Manafi Neyazi, 2026. "Shrinkage Regularization for (Non)Linear Serial Dependence Test," Papers 2603.10152, arXiv.org.
    • Handle: RePEc:arx:papers:2603.10152
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    File URL: http://arxiv.org/pdf/2603.10152
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    References listed on IDEAS

    as
    1. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    2. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    3. Christian Gourieroux & Joann Jasiak, 2023. "Generalized Covariance Estimator," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1315-1327, October.
    4. Francesco Giancaterini & Alain Hecq & Joann Jasiak & Aryan Manafi Neyazi, 2025. "Regularized Generalized Covariance (RGCov) Estimator," Papers 2504.18678, arXiv.org.
    5. Kung-Sik Chan & Lop-Hing Ho & Howell Tong, 2006. "A note on time-reversibility of multivariate linear processes," Biometrika, Biometrika Trust, vol. 93(1), pages 221-227, March.
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