B0422
Title: Identification and estimation of change points in factor models for high-dimensional time series data
Authors: Xialu Liu - San Diego State University (United States) [presenting]
Abstract: The focus is on estimating and identifying a factor model for high-dimensional time series that contains structural breaks in the factor loading space at unknown time points. We first study the case when there is one change point in factor loadings, and propose a consistent estimator for the structural break location. We show that the proposed estimators for change-point location and loading spaces are consistent when the number of factors is correctly estimated or overestimated. The algorithm for multiple change-point detection is also developed. A distinguishing feature of the proposed method is that it is specifically designed for the changes in the factor loading space and the stationarity assumption is not imposed on either the factor or noise process, while most existing methods for change-point detection of high-dimensional time series with/without a factor structure require the data to be stationary or close to a stationary process between two change points, which is rather restrictive. Numerical experiments, including a Monte Carlo simulation and a real data application, are presented to illustrate the proposed estimators perform well.
