报告时间：2021年7月23日（星期五）9:30-11:30

报告地点：管理学院1号楼1111会议室

报 告 人：郑挺国 教授

工作单位：厦门大学

举办单位：管理学院

报告简介：

This paper introduces a large time-varying parameter structural vector autoregressive (TVP-SVAR) model and then proposes a fast approach to estimate it. Based on the score-driven modeling framework, we firstly assume that the time-varying variances of structural errors in each equation of the TVP-SVAR are score-driven, and then propose the filtering and smoothing procedures for estimating time-varying parameters and time-varying volatilities. We show that under the Minnesota prior and forgetting factors, the filtering estimation of time-varying parameters is equivalent to an equation-by-equation estimator, which can greatly reduce the dimension of state space and thus is a very fast estimation. Moreover, we find that under forgetting factors, the smoothing estimation is also straightforward and extremely fast, which overcomes the inverse of supra-high dimensional state equation covariance matrix. Our simulation study shows that the proposed method in filtering the data is more accuracy than the existing popular method and illustrates the computational gain from the equation-by-equation estimator. Finally, we carry out an empirical study on dynamic connectedness of global stock markets, which demonstrates the advantages of our method in real-time and ex-post analysis.

报告人简介：

郑挺国，厦门大学经济学院和王亚南经济研究院教授、博士生导师，厦门大学特聘教授。先后入选教育部新世纪优秀人才。主要从事宏观经济与政策分析、宏观计量学、金融计量学、时间序列分析等领域的研究。在《经济研究》、《世界经济》、《经济学季刊》、《金融研究》、《管理世界》、Journal of Econometrics、Journal of Business & Economic Statistics、Journal of Multivariate Analysis以及China Economic Review等国内外学术期刊上发表论文近70篇。主持国家自然科学基金项目3项，获全国优秀博士学位论文提名奖以及其他省部级各类奖项多项。目前致力于宏观经济与金融市场监测、宏观与金融大数据分析等方面的专题研究，积极运用经济计量方法对现实经济进行实时监测和预测。