A1271
Title: A general framework for constructing locally self-normalized multiple-change-point tests
Authors: Cheuk Hin Cheng - The Chinese University of Hong Kong (Hong Kong) [presenting]
Kin Wai Chan - The Chinese University of Hong Kong (Hong Kong)
Abstract: A general framework is proposed 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, or 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. A case study of the Shanghai-Hong Kong Stock Connect turnover is provided.
