报告题目：Estimation of Grouped Time-Varying Network Vector Autoregression Models
报告人：李德柜
时 间：2025年4月3日（星期四）上午11:30-12:30
地 点：西安交通大学创新港涵英楼经济金融研究院8121会议室
报告人简介：
李德柜，现为澳门大学工商管理学院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及社会科学学院经济系兼职特聘教授，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, 曾受ARC、BA/Leverhulme Trust及Heilbronn Institute等机构的科研资助，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。
摘要：
This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with nonparametrically estimated distance matrix is combined with a ratio criterion to consistently estimate the latent group number and membership. A post-grouping local linear smoothing method is proposed to estimate the group-specific time-varying momentum and network effects, substantially improving the convergence rates of the preliminary estimates which ignore the latent structure. We further modify the methodology and theory to allow for structural breaks in either the group membership, group number or group-specific coefficient functions. Numerical studies including Monte-Carlo simulation and an empirical application are presented to examine the finite-sample performance of the developed model and methodology.
经济与金融学院
2025年3月26日
