学术沙龙主题:Estimation and inference of structural break in high-dimensional factor models using QML

报告人:段江涛 广州大学（博士后）

报告时间:2022年4月26日(周二)；上午10:30—12:00

报告地点：腾讯会议ID：203613815

报告人简介：段江涛，2021年获东北师范大学统计学博士，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。主持国家自然科学基金青年基金项目，以第一作者身份在《Journal of Econometrics》、《Canadian Journal of Statistics》、《Journal of Statistical Computation and Simulation》、《Journal of Computational and Applied Mathematics》期刊发表科研论文4篇。

报告摘要:This paper proposes a quasi-maximum likelihood (QML) estimator of the break point for large-dimensional factor models with a single structural break 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 k0 converges to zero as the sample size grows. This is a much stronger result than the break fraction k/T being T -consistent (super-consistent) for k0/T . Also, singularity occurs for most types of structural changes, except for a rotational change. Even for a rotational change, the QML estimator is still T -consistent in terms of the break fraction. Simulation results confirm the theoretical properties of our estimator, and it significantly outperforms existing estimators for change points in factor models.