【学术讲座】Identifying the structure of high-dimensional time series via eigen-analysis

报告题目：Identifying the structure of high-dimensional time series via eigen-analysis

报告人：张博

邀请人：黄磊

讲座时间： 2025年12月4日，周四上午10:00-11:00

讲座地点： 犀浦校区三号教学楼 X30423

报告摘要：Cross-sectional structures and temporal tendency are important features of high-dimensional time series. Based on eigen-analysis on sample covariance matrices, we propose a novel approach to identifying four popular structures of high-dimensional time series, which are grouped in terms of factor structures and stationarity. The proposed three-step method includes: (1) a ratio statistic of empirical eigenvalues; (2) a projected Augmented Dickey-Fuller Test; (3) a new unit-root test based on the largest empirical eigenvalues.We develop asymptotic properties for these three statistics to ensure the feasibility of the whole identifying procedure. Finite sample performances are illustrated via various simulations. We also analyze U.S. mortality data, U.S. house prices and in-come, and U.S. sectoral employment, all of which possess cross–sectional dependence and non-stationary temporal dependence. It is worth mentioning that we also contribute to statistical justification for the benchmark paper by Lee and Carter [32] in mortality forecasting.

主讲人简介：张博，中国科学技术大学统计与金融系副教授，2017年于新加坡南洋理工大学获博士学位，主要研究方向为大维随机矩阵、高维时间序列和复杂网络问题。他的部分研究被AOS, JASA, Biometrika, Statistica Sinica等期刊发表或接收，主持国自然青年及面上项目。