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. This is a joint work with Y. Yan and Q. Yao.

 

报告人简介：李德柜，现为澳门大学工商管理学院副院长、商业经济学特聘教授、亚太经济与管理研究所金融计量经济首席研究员，此前曾在英国约克大学及澳大利亚阿德莱德大学、莫纳什大学工作。主要研究领域包括时间序列分析、面板数据建模、函数型数据分析、网格数据建模、金融计量经济学、非参数计量经济学、高维计量经济学，并有数十篇论文发表于国际知名计量经济学和统计学刊物如AoS、JASA、JoE、JBES、ET、JMLR等。 2011年获澳大利亚科研委员会DECRA奖，2023年获英国Leverhulme Research Fellowship, 2025获国家自然科学基金青年基金A类项目，现担任理论计量经济学顶级期刊《Econometric Theory》联合主编及《Journal of Time Series Analysis》等国际学术刊物的副主编。