A General Framework for Constructing Locally Self-Normalized Multiple-Change-Point Tests
提出一个通用框架，利用用户指定的单变点检测统计量构建自归一化多变点检验，无需稳健估计或预设变点数量，在有限样本中表现出更好的检验准确性和稳健性，并应用于沪港通成交量分析。
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.