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Epidemic change-point detection in general causal time series

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  • Diop, Mamadou Lamine
  • Kengne, William

Abstract

We consider an epidemic change-point detection in a large class of causal time series models, including among other processes, AR(∞), ARCH(∞), TARCH(∞), ARMA-GARCH. A test statistic based on the Gaussian quasi-maximum likelihood estimator of the parameter is proposed. It is shown that, under the null hypothesis of no change, the test statistic converges to a distribution obtained from a difference of two Brownian bridge and diverges to infinity under the epidemic alternative. Numerical results for simulation and real data example are provided.

Suggested Citation

  • Diop, Mamadou Lamine & Kengne, William, 2022. "Epidemic change-point detection in general causal time series," Statistics & Probability Letters, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:stapro:v:184:y:2022:i:c:s0167715222000323
    DOI: 10.1016/j.spl.2022.109416
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    References listed on IDEAS

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    5. Kengne, William, 2021. "Strongly consistent model selection for general causal time series," Statistics & Probability Letters, Elsevier, vol. 171(C).
    6. Aston, John A.D. & Kirch, Claudia, 2012. "Detecting and estimating changes in dependent functional data," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 204-220.
    7. Juliana B. de Souza & Valdério A. Reisen & Glaura C. Franco & Márton Ispány & Pascal Bondon & Jane Meri Santos, 2018. "Generalized additive models with principal component analysis: an application to time series of respiratory disease and air pollution data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(2), pages 453-480, February.
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    1. Mamadou Lamine Diop & William Kengne, 2023. "A general procedure for change-point detection in multivariate time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 1-33, March.

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