报告题目：Time-Varying Parameter Dynamic Factor Models: From Novel Estimation to Macroeconomic Monitoring
报告人：郑挺国 厦门大学
报告时间：2024年3月26日(周二) 16:30-18:00
报告地点：中国科学院数学与系统科学研究院南楼N204
腾讯会议ID：662 6149 3785
内容摘要
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 joint quasi-maximum likelihood estimation approach to estimate TVP-DFM. The joint 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. Other two related estimation approaches, namely dual estimation and approximate estimation, are also introduced. In the simulation study, the finite-sample performance and in-sample fitting performance of the proposed estimators are further compared. 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.
主讲人简介
郑挺国，厦门大学邹至庄经济研究院、经济学院统计学与数据科学系和王亚南经济研究院教授，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。主要从事数量经济学的教学与科研工作，研究领域为宏观经济与政策分析、宏观计量经济学、时间序列分析、大数据方法与应用等。先后在《经济研究》（12篇）、《管理世界》、《管理科学学报》、Journal of Econometrics、Journal of Business and Economic Statistics、Journal of Economic Dynamics and Control、International Journal of Forecasting、Journal of Multivariate Analysis等国内外重要学术期刊发表论文88篇。曾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。近五年先后研制“科宽宏观经济与金融市场实时监测系统”和作为课题组组长研发厦门大学宏观经济研究中心“宏观经济实时监测系统”。
