报告人：段江涛
报告地点：数学与统计学院415室
报告时间：2023年11月18日星期六10:30-11:30
邀请人：高巍
报告摘要：
This paper proposes a quasi-maximum likelihood (QML) estimator of the break point for large-dimensional factor models with multiple structural breaks in the factor loading matrix. We show that the QML estimator is consistent for the true break point when the covariance matrix of the pre- or post-break factor loading (or both) is singular. Consistency here means that the deviation of the estimated break date from the true break date $k_0$ converges to zero as the sample size grows. This is a much stronger result than the break fraction $\hat k/T$ being $T$-consistent (super-consistent) for $k_0/T$. Also, we consider the likelihood ratio (LR) test for the estimated factors. Simulation results confirm the theoretical properties of our estimator, and it significantly outperforms existing estimators for change points in factor models.
主讲人简介：
段江涛，现为西安电子科技大学数学与统计学院统计系菁英副教授。2021年获东北师范大学统计学博士学位，2021-2023年广州大学经济与统计学院统计系博士后。曾赴美国哥伦比亚大学、香港城市大学进行访问与合作研究。研究兴趣为高维因子模型，面板数据，试验设计。主持和参与国家自然科学基金项目和国家社会科学基金项目多项，多篇文章发表于Journal of Econometrics (2篇), Canadian Journal of Statistics, Journal of Statistical Computation and Simulation, Journal of Computational and Applied Mathematics.
