报告题目：Two-way Homogeneity Pursuit for Quantile Network Vector Autoregression

报告时间：2024年6月25日 上午10:00

会议链接：https://meeting.tencent.com/dm/C8v92W9L9CN8

会议 ID：643-760-777

主办单位：数学与统计学院/科研处

报告人：朱雪宁

报告人简介：复旦大学大数据学院教授，博士生导师。2017年获得北京大学光华管理学院商务统计与经济计量系博士学位，2017-2018在美国宾夕法尼亚州立大学从事博士后研究工作。入选2019年度上海市青年科技英才扬帆计划，获得国家级人才称号。主要研究领域为网络数据分析、空间计量模型、高维数据建模等，研究成果发表于Journal of Econometrics, Journal of the American Statistical Association, Annals of Statistics, 中国科学等国内外经济计量与统计学期刊，著有教材2本。

摘要：While the Vector Autoregression (VAR) model has received extensive attention for modelling complex time series, quantile VAR analysis remains relatively underexplored for high-dimensional time series data. To address this disparity, we introduce a two-way grouped network quantile (TGNQ) autoregression model for time series collected on large-scale networks, known for their significant heterogeneous and directional interactions among nodes. Our proposed model simultaneously conducts node clustering and model estimation to balance complexity and interpretability. To account for the directional influence among network nodes, each network node is assigned two latent group memberships that can be consistently estimated using our proposed estimation procedure. Theoretical analysis demonstrates the consistency of membership and parameter estimators even with an overspecified number of groups. With the correct group specification, estimated parameters are proven to be asymptotically normal, enabling valid statistical inferences. Moreover, we propose a quantile information criterion for consistently selecting the number of groups. Simulation studies show promising finite sample performance, and we apply the methodology to analyze connectedness and risk spillover effects among Chinese A-share stocks.