时间：2025-04-15 13:30

地点：理四323

报名截止时间：2025-04-14 17:00

李宇宁，英国约克大学（University of York）商业与社会学院助理教授。浙江大学数学与应用数学学士、统计学博士，英国约克大学经济学博士。主要从事时间序列和计量经济学的理论研究，在国际学术刊物上发表多篇论文，其中包括在Journal of Econometrics, Journal of Business and Economic Statistics, Econometric Theory等国际计量经济学顶级期刊上的论文。

单位：英国约克大学

This paper studies the estimation of dynamic precision matrices with multiple conditioning variables for high-dimensional time series. We assume that the high-dimensional time series has an approximate factor structure plus an idiosyncratic error term, allowing the time series to have a non-sparse dynamic precision matrix and hence, enhancing the applicability of our method. Using the Sherman-Morrison-Woodbury formula, the estimation of the dynamic precision matrix for the time series boils down to the estimation of a low-rank factor structure and the precision matrix of the idiosyncratic error term. For the latter, we introduce an easy-to-implement semiparametric method to estimate the entries of the corresponding dynamic covariance matrix via the Model Averaging MArginal Regression (MAMAR) before applying the constrained L1 minimisation for inverse matrix estimation (CLIME) method to obtain the dynamic precision matrix. Under some regularity conditions, we derive the uniform consistency for the proposed estimators. We provide a simulation study that illustrates the finite-sample performance of the developed methodology and an application in construction of minimum-variance portfolios using daily returns of S&P 500 constituents from 2000 to 2024.