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题目：Eigen-decomposition based least squares estimation procedure for high-dimensional spatial dynamic panel data models

报告人：金百锁（中国科学技术大学统计与金融系教授）

摘要：Spatiotemporal modeling of networks is of great practical importance, with modern applications in epidemiology and social network analysis. Despite rapid methodological advances, how to effectively and efficiently estimate the parameters of spatial dynamic panel models remains a challenging problem. To tackle this problem, we construct consistent complex least-squares estimators by the eigen-decomposition of a spatial weight matrix method originally proposed for undirected networks. We no longer require all eigenvalues and eigenvectors to be real, which is a remarkable achievement as it implies that the proposed method is now applicable to spatiotemporal data modeling of directed networks. Under mild, interpretable conditions, we show that the proposed parameter estimators are consistent and asymptotically normally distributed. We also present a complex orthogonal greedy algorithm for variable selection and rigorously investigate its convergence properties. Moreover, we incorporate fixed effects into the spatial dynamic panel models and provide a model transformation so that the proposed method can also be applied to the transformed model. Extensive simulation studies and data examples demonstrate the effectiveness of the proposed method.

报告人简介：金百锁，中国科学技术大学统计与金融系教授。主持过安徽省自然科学基金杰出青年基金项目，国家自然科学基金委青年项目和面上项目。研究方向：空间统计，变结构模型，随机矩阵。在PNAS，Annals of Statistic，Biometrika，Journal of Econometrics等国内外期刊已发表60多篇论文，出版1本英文专著。现任Journal of systems science and complexity 编委，全国工业统计学教学研究会常务理事，中国现场统计研究会旅游大数据分会副理事长，中国现场统计研究会教育统计与管理分会秘书长。

报告时间：2025年6月11日（周三）下午 16:00-17：00

报告地点：#腾讯会议：634-857-651

联系人：胡涛