【专家简介】：袁超凤，黑龙江大学数学科学学院讲师，硕士研究生导师。 2018年于东北师范大学获得统计学博士学位，师从何旭铭教授和郭建华教授。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。其研究成果主要发表在JRSSB、TEST等统计学权威期刊。
【报告摘要】：In this article, we introduce a two-way dynamic factor model (2w-DFM) for high-dimensional matrix-valued time series and study some of the basic theoretical properties in terms of identifiability and estimation accuracy. The proposed model aims to capture separable and low-dimensional effects of row and column attributes and their correlations across rows, columns, and time points. Complementary to other dynamic factor models for high-dimensional data, the 2w-DFM inherits the dimension-reduction feature of factor models but assumes additive row and column factors for easier interpretability. We provide conditions to ensure model identifiability and consider a quasi-likelihood based two-step method for parameter estimation. Under an asymptotic regime where the size of the data matrices as well as the length of the time series increase, we establish that the estimators achieve the optimal rate of convergence and are
asymptotically normal. The asymptotic properties are reaffirmed empirically through simulation studies. An application to air quality data in Chinese cities is given to illustrate the merit of the 2w-DFM.
时间：2023年10月16日 15:00
地点：腾讯会议：871-619-007
