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

Marie-Christine Düker (), Seok-Oh Jeong (), Taewook Lee () and Changryong Baek ()
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Marie-Christine Düker: Cornell University
Seok-Oh Jeong: Hankuk University of Foreign Studies
Taewook Lee: Hankuk University of Foreign Studies
Changryong Baek: Sungkyunkwan University

Statistical Papers, 2024, vol. 65, issue 4, No 17, 2327-2359

Abstract: 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.

Keywords: Change point analysis; High-dimensional time series; Block wild bootstrap; CUSUM; Binary segmentation; Moving sum algorithm; Primary: 62M10; 62G10; Secondary: 62H15 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01484-3

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