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Quasi maximum likelihood estimation for large-dimensional matrix factor models

发布时间:2024-07-02 点击次数:

标题:Quasi maximum likelihood estimation for large-dimensional matrix factor models

报告时间:2024年07月04日(星期四)10:00-11:00

报告地点:人民大街校区数学与统计学院415报告厅

主讲人:袁超凤

主办单位:数学与统计学院

报告内容简介:

  In this study, we introduces a novel approach, called the quasi maximum likelihood estimation (Q-MLE), for estimating large-dimensional matrix factor models. In contrast to the principal components based approach, Q-MLE takes into account heteroskedasticities of the diosyncratic error term, which are simultaneously estimated with other parameters. Theoretical analysis shows that the Q-MLE estimator of the factor loading matrices achieves faster convergence rates than  most existing estimators under similar conditions. We also present the asymptotic distributions of the Q-MLE estimators. Extensive numerical experiments demonstrate that the Q-MLE method performs better empirically, especially when heteroscedasticity exists. Furthermore, two real examples in finance and macroeconomics reveal factor patterns across rows and columns, which coincide with financial, economic, or geographical interpretations.

主讲人简介:

  袁超凤,黑龙江大学数学科学学院,教授、硕士研究生导师。主要研究方向为高维因子分析、面板数据分析及时间序列分析,研究成果主要发表在JRSSB、JBES、TEST等统计学权威期刊。