报告题目：Estimating Time-Varying Networks for High-Dimensional Time Series
主讲嘉宾：陈佳教授于2008年取得浙江大学理学博士学位，后于2008-2011在澳大利亚阿德莱德大学和蒙纳士大学从事博士后研究。现为约克大学经济系正教授。陈佳教授在非参数和半参数统计，面板数据建模和统计推断，高维统计和计量经济学等领域取得了一系列国际领先的研究成果，并同时担任Journal of Nonparametric Statistics,Economic Modelling以及Australian and New Zealand Journal of Statistics的副主编。陈佳教授已有20余篇科研论文发表于国际知名统计学和计量经济学期刊。其中包括Annals of Statistics，Journal of the American Statistical Association，Journal of Econometrics，Journal of Business and Economic Statistics，Econometric Theory，Econometrics Journal.
报告摘要：We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over time. Two types of time-varying graphs are investigated: one containing directed edges of Granger causality linkages, and the other containing undirected edges of partial correlation linkages. Under the sparse structural assumption, we propose a penalised local linear method with time-varying weighted group LASSO to jointly estimate the transition matrices and identify their significant entries, and a time-varying CLIME method to estimate the precision matrices. The estimated transition and precision matrices are then used to determine the time-varying network structures. Under some mild conditions, we derive the theoretical properties of the proposed estimates including the consistency and oracle properties. In addition, the developed methodology and theory are extended to highly correlated large-scale time series, for which the sparsity assumption becomes invalid and factor-adjusted time-varying networks are estimated. Extensive simulation studies and an empirical application to a large U.S. macroeconomic dataset are provided to illustrate the finite-sample performance of the developed methods.
报告时间：2023年1月3日(周二)，16:30-18:00
线上地点：腾讯会议 ID：375 8612 5504
