【报告摘要】Matrix time-series data have become increasingly prevalent across diverse fields, including economics, finance, computer science, engineering, and signal processing. This study introduces a novel multi-matrix autoregressive (MMAR) model designed to jointly model matrix time series with varying structures. In particular, the matrix-valued autoregressive model and the three-order tensor autoregressive model are special cases of the proposed model. We present three distinct estimation methods for the MMAR model, investigate their statistical properties, and provide numerical simulations to corroborate them. Furthermore, we integrate the MMAR model with connectedness network analysis to propose a multi-modal connectedness approach and concurrently model the macroeconomic matrix time series of China's 31 provinces and a vector time series comprising the economic policy uncertainty, trade policy uncertainty, and geopolitical risk. We delve into the intricate interrelationships between China's regional economy and macroeconomic regulation. The findings of this study provide valuable information for further research and policy making in the relevant domains.

【报告人简介】叶仕奇，中国科学院数学与系统科学研究院预测科学研究中心助理研究员。主要研究方向为时间序列计量经济学、应用宏观计量经济学、金融计量经济学等。获得首批国家自然科学基金青年学生基础研究项目（博士研究生）资助。相关成果发表于《经济研究》《中国工业经济》《管理科学学报》、Journal of Economic Dynamics and Control, Economic Letters, Energy Economics等经济学领域国内外权威期刊。曾获得澳大利亚新西兰计量经济学研究组（ANZESG）青年计量经济学家奖（最佳论文报告奖），是该年会唯一来自中国高校的获奖人员。