Moving Sum Procedure for Multiple Change Point Detection in Large Factor Models
提出一种移动和方法，用于检测高维时间序列因子模型中的多重变点，包括载荷变化和因子出现或消失，并建立了渐近零分布和一致性。
ABSTRACT This paper proposes a moving sum methodology for detecting multiple change points in high‐dimensional time series under a factor model, where changes are attributed to those in loadings as well as emergence or disappearance of factors. We establish the asymptotic null distribution of the proposed test for family‐wise error control and show the consistency of the procedure for multiple change point estimation. Simulation studies and an application to a large dataset of volatilities demonstrate the competitive performance of the proposed method.