A General Framework for Constructing Locally Self-Normalized Multiple-Change-Point Tests

Cheuk Hin Cheng and Kin Wai Chan

Journal of Business & Economic Statistics, 2024, vol. 42, issue 2, 719-731

Abstract: We propose a general framework to construct self-normalized multiple-change-point tests with time series data. The only building block is a user-specified single-change-detecting statistic, which covers a large class of popular methods, including the cumulative sum process, outlier-robust rank statistics, and order statistics. The proposed test statistic does not require robust and consistent estimation of nuisance parameters, selection of bandwidth parameters, nor pre-specification of the number of change points. The finite-sample performance shows that the proposed test is size-accurate, robust against misspecification of the alternative hypothesis, and more powerful than existing methods. Case studies of the Shanghai-Hong Kong Stock Connect turnover are provided.

Date: 2024
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DOI: 10.1080/07350015.2023.2231041

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