报告题目：Two-way Homogeneity Pursuit for Quantile Network Vector Autoregression
报 告 人：朱雪宁 副教授 复旦大学
报告时间：2024年6月19日 9:00-10:00
报告地点：#腾讯会议：642-857-311
校内联系人：朱复康 fzhu@jlu.edu.cn
报告摘要：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.
报告人简介：朱雪宁，复旦大学大数据学院副教授，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，2022年获得国家自然科学基金优秀青年基金项目资助。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。
