報告題目：Two-way Homogeneity Pursuit for Quantile Network Vector Autoregression
報 告 人：朱雪甯 副教授 複旦大學
報告時間：2024年6月19日 9:00-10:00
報告地點：#騰訊會議：642-857-311
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：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.
報告人簡介：朱雪甯，複旦大學大數據學院副教授，博士生導師。2017年獲得北京大學光華管理學院商務統計與經濟計量系博士學位，2017-2018在美國賓夕法尼亞州立大學從事博士後研究工作。入選2019年度上海市青年科技英才揚帆計劃，2022年獲得國家自然科學基金優秀青年基金項目資助。主要研究領域為網絡數據分析、空間計量模型、高維數據建模等，研究成果發表于Journal of Econometrics, Journal of the American Statistical Association, Annals of Statistics, 中國科學等國内外經濟計量與統計學期刊，著有教材2本。
