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

2023/07/06 by Cheuk Hin Cheng, Kin Wai Chan · 1 citation
Economics, Econometrics and Finance · Mathematics · #Global trade and economics #Monetary Policy and Economic Impact #Statistical Methods and Inference

paper · doi:10.1080/07350015.2023.2231041

openalex publication_date 2023/07/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Estimating and Testing Linear Models with Multiple Structural Changes
• Segmenting Time Series via Self-Normalization

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