讲座主题：Fast Estimation of a Large TVP-VAR Model with Score-Driven Volatilities

主讲嘉宾：郑挺国，厦门大学3308维多利亚优惠大厅和王亚南经济研究院教授

讲座时间：2022年12月08日 14:00-16:00

讲座地点：腾讯会议ID 736268185

嘉宾简介：郑挺国，厦门大学3308维多利亚优惠大厅和王亚南经济研究院教授、博士生导师，厦门大学特聘教授，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。近年来在Journal of Econometrics、Journal of Business & Economic Statistics、Journal of Multivariate Analysis、Journal of Time Series Analysis等国际学术期刊发表论文近20篇，在《经济研究》、《经济学季刊》、《世界经济》、《金融研究》等国内学术期刊上发表论文50余篇。曾主持国家自然科学基金项目三项，获全国优秀博士学位论文提名奖，入选国家级高层次人才、国家万人计划青年拔尖人才、福建省哲学社会科学领军人才、教育部新世纪优秀人才等。

内容摘要：This paper proposes a fast approach for estimating a large time-varying parameter structural vector autoregressive (TVP-SVAR) model. 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 forgetting factors, the filtering estimation of time-varying parameters is equivalent to an equation-by-equation estimator, which can significantly reduce the dimension of state space and thus is a very fast estimation. Moreover, an extremely fast smoothing estimation can be derived straightforwardly, overcoming the inverse of the supra-high dimensional state equation covariance matrix. We provide dynamic model averaging (selection) and maximum likelihood estimates for the needs of forecasting and inference, respectively. Our simulation study shows that the proposed method is more accurate than the existing popular methods and illustrates the tremendous computational gain from the equation-by-equation estimator. Finally, we conduct an empirical study on the dynamic connectedness of global stock markets, demonstrating our method's advantages in real-time and ex-post analysis.