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 income,and U.S. sectoral employment, all of which possess cross–sectional dependence andnon-stationary temporal dependence. It is worth mentioning that we also contributeto statistical justification for the benchmark paper by Lee and Carter (1992, JASA) in mortalityforecasting.

报告人信息：

Yanrong Yang is an Associate Professor at School of Finance, Actuarial Studies and Finance from The Australian National University. Yanrong’s research interests include high-dimensional statistical inference, large-dimensional random matrix theory, functional time series analysis, large panel data analysis, and responsible data analysis in Finance and Actuarial Science. Yanrong also has publications on leading journals in these diverse areas, in particular includingAnnals of Statistics, Journal of the Royal Statistical Society: Series B, Journal of the American Statistical Association, Journal of Econometrics.

报告时间：2025年6月23日10:00-12:00

报告地点：北衡楼1420