主题：Factor Models of Matrix-Valued Time Series: Nonstationarity and Cointegration
主讲人：严雅毅 上海财经大学
主持人：朱海斌 暨南大学
时间：2025年12月19日（周五）下午16:00-17:00
地点：暨南大学石牌校区经济学院大楼（中惠楼）102室
摘要
In this paper, we consider the nonstationary matrix-valued time series with common stochastic trends. Unlike the traditional factor analysis which flattens matrix observations into vectors, we adopt a matrix factor model in order to fully explore the intrinsic matrix structure in the data, allowing interaction between the row and column stochastic trends, and subsequently improving the estimation convergence. It also reduces the computation complexity in estimation. The main estimation methodology is built on the eigenanalysis of sample row and column covariance matrices when the nonstationary matrix factors are of full rank and the idiosyncratic components are temporally stationary, and is further extended to tackle a more flexible setting when the matrix factors are cointegrated and the idiosyncratic components may be nonstationary. Under some mild conditions which allow the existence of weak factors, we derive the convergence theory for the estimated factor loading matrices and nonstationary factor matrices. In particular, the developed methodology and theory are applicable to the general case of heterogeneous strengths over weak factors. An easy-to-implement ratio criterion is adopted to consistently estimate the size of latent factor matrix. Both simulation and empirical studies are conducted to examine the numerical performance of the developed model and methodology in finite samples.
主讲人简介
严雅毅，上海财经大学统计与数据科学学院副教授，2022年获莫纳什大学计量经济学博士学位。主要研究领域为计量经济学理论、实证资产定价等，已在Journal of the American Statistical Association、Journal of Econometrics、Journal of Business & Economic Statistics、Econometric Theory等期刊发表10余篇论文。获国自然青年基金资助，并入选2023年上海市领军人才（海外）青年项目。
欢迎感兴趣的师生参加！
校对|朱海斌
责编| 彭毅
初审| 姜云卢
终审发布| 何凌云
（来源：暨南大学经济学院微信公众号）
