摘要：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.
個人簡介：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，2023年獲得國家自然科學基金優秀青年基金項目資助。參與國家自然科學基金重大項目一項。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。
騰訊會議号：308-164-485
時間：4月6日（周四）19:00