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Detection of multiple change-points in high-dimensional panel data with cross-sectional and temporal dependence

Author

Listed:
  • Marie-Christine Düker

    (Cornell University)

  • Seok-Oh Jeong

    (Hankuk University of Foreign Studies)

  • Taewook Lee

    (Hankuk University of Foreign Studies)

  • Changryong Baek

    (Sungkyunkwan University)

Abstract

We consider the detection of multiple change-points in a high-dimensional time series exhibiting both cross-sectional and temporal dependence. Several test statistics based on the celebrated CUSUM statistic are used and discussed. In particular, we propose a novel block wild bootstrap method to address the presence of cross-sectional and temporal dependence. Furthermore, binary segmentation and the moving sum algorithm are considered to detect and locate multiple change-points. We also give some theoretical justifications for the moving sum method. An extensive numerical study provides insights on the performance of the proposed methods. Finally, our proposed procedures are used to analyze financial stock data in Korea.

Suggested Citation

  • Marie-Christine Düker & Seok-Oh Jeong & Taewook Lee & Changryong Baek, 2024. "Detection of multiple change-points in high-dimensional panel data with cross-sectional and temporal dependence," Statistical Papers, Springer, vol. 65(4), pages 2327-2359, June.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:4:d:10.1007_s00362-023-01484-3
    DOI: 10.1007/s00362-023-01484-3
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    References listed on IDEAS

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    1. Jinyuan Chang & Qiwei Yao & Wen Zhou, 2017. "Testing for high-dimensional white noise using maximum cross-correlations," Biometrika, Biometrika Trust, vol. 104(1), pages 111-127.
    2. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    3. Hsu, Chih-Chiang, 2007. "The MOSUM of squares test for monitoring variance changes," Finance Research Letters, Elsevier, vol. 4(4), pages 254-260, December.
    4. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    5. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," Papers 1212.6906, arXiv.org, revised Jan 2018.
    6. Chang, Jinyuan & Yao, Qiwei & Zhou, Wen, 2017. "Testing for high-dimensional white noise using maximum cross-correlations," LSE Research Online Documents on Economics 68531, London School of Economics and Political Science, LSE Library.
    7. Lee, Taewook & Baek, Changryong, 2020. "Block wild bootstrap-based CUSUM tests robust to high persistence and misspecification," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    8. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
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