“数字+”与统计数据工程系列讲座（六十九）10月23日厦门大学郑挺国教授来中心讲座预告

讲座题目：Time-Varying Parameter Dynamic Factor Models: From Novel Estimation To Macroeconomic Monitoring

主讲人：郑挺国

讲座时间：2024年10月23日10:00-11:30

讲座地点： 综合楼644会议室

主讲人简介：

郑挺国，厦门大学邹至庄经济研究院、经济学院统计系和王亚南经济研究院教授、博士生导师，厦门大学宏观经济研究中心副主任，厦门大学南强重点岗位特聘教授，中国数量经济学会常务理事，中国统计教育学会常务理事，国家社科基金重大项目首席专家。研究领域为宏观经济与政策分析、宏观计量经济学、金融计量经济学、时间序列分析、大数据方法与应用。先后在《经济研究》（12篇）、《管理世界》、Journal of Econometrics、Journal of Business & Economic Statistics、Journal of Economic Dynamics & Control等国内外主流期刊上共发表论文90余篇。入选教育部青年长江学者、国家万人计划青年拔尖人才、福建省特支双百计划哲学社会科学领军人才、教育部新世纪优秀人才、福建省新世纪优秀人才、福建省高校杰出青年科研人才。主持国家社科基金重大项目1项，教育部人文社会科学重点研究基地重大项目（首席专家）1项，国家自然科学基金项目4项。

报告摘要：    

The time-varying parameter dynamic factor model (TVP-DFM) has received increasing attention in recent years due to its consideration of the changing economic environment and structure. However, its estimation and applications are still very limited. This paper proposes an efficient quasi-maximum likelihood estimation approach to estimate TVP-DFM. The estimation approach represents TVP-DFM as a linear non-Gaussian state space model and then exploits quasi-maximum likelihood estimation to estimate unknown parameters of the model, as well as common factors and time-varying factor loadings. In the simulation study, the finite-sample performance and in-sample fitting performance of the proposed estimator are further validated. Moreover, to adapt to high-dimensional modeling and estimation of large-scale economic variables, we propose a parsimonious TVP-DFM and confirm its applicability and accuracy in high-dimensional scenarios. Finally, we use basic TVP-DFM and parsimonious TVP-DFM to empirically measure China's economic conditions from low and high dimensions, as well as from a real-time data-rich environment.