Time: 2023/03/31 (Fri.), 10:30 – 12:00(Beijing Time)

Title:  Matrix-valued Network Autoregression Model with Latent Group Structure

Venue: Zhonghui Building 106

About the speaker:

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，2023年获得国家自然科学基金优秀青年基金项目资助。参与国家自然科学基金重大项目一项。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。

Abstract:

Group number selection is a key question for group panel data modelling. In this work, we develop a cross validation method to tackle this problem. Specifically, we split the panel data into a training dataset and a testing dataset on the time span. We first use the training dataset to estimate the parameters and group memberships. Then we apply the fitted model to the testing dataset and then the group number is estimated by minimizing certain loss function values on the testing dataset. We design the loss functions for panel data models either with or without fixed effects. The proposed method has two advantages. First, the method is totally data-driven thus no further tuning parameters are involved. Second, the method can be flexibly applied to a wide range of panel data models. Theoretically, we establish the estimation consistency by taking advantage of the optimization property of the estimation algorithm. Experiments on a variety of synthetic and empirical datasets are carried out to further illustrate the advantages of the proposed method.