报告人：刘婧媛 教授
报告题目：LLM-Powered Deep Panel Modeling
报告时间：2026年5月8日（周五）10：30-11：30
报告地点：云龙校区6号楼318会议室
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
刘婧媛，厦门大学经济学院统计学与数据科学系南强特聘教授、博士生导师，国家级高层次人才（教育部），厦门大学南强卓越教学名师、南强青年拔尖人才（A类）、厦门大学“我最喜爱的十位教师”。美国宾夕法尼亚州立大学统计学博士。科研方面主要从事高维及复杂数据的统计方法、因果中介效应分析、大模型辅助统计建模、多数据源整合等领域的工作，在JASA、JMLR、JOE等国际权威学术期刊发表论文40余篇，担任JASA、JBES和AOAS编委，入选福建省杰出青年科研人才计划。
报告摘要：
Panel modeling for economic dynamics is crucial for timely and effective policymaking. However, it typically relies only on low-frequency, high-cost surveys and macroeconomic variables, thus often fails to capture rapid market fluctuations and leads to inaccurate predictions. In this paper, we propose a new framework that integrates large language model (LLM) analyses and social media narratives to enhance the prediction power of dynamic panel modeling. Through narrative corpus constructed from social media data, we introduce a prompt-based GPT model and a series of fine-tuned BERT models to generate high-frequency LLM-induced surrogates for the economic indices of interest. A novel joint modeling strategy is then advocated to transfer the information from these surrogates to enhance the prediction power for the targeted economic indices. To solve the joint objectives, we further develop a new deep panel learning procedure with region-wise homogeneity pursuit, which has its own significance in panel data analysis literature. In addition, conformal-based panel prediction intervals are provided to quantify the uncertainty of the LLM-powered prediction. Empirical and theoretical analyses demonstrate that our approach significantly reduces short-term forecasting errors and more effectively captures abrupt inflationary shifts compared to traditional econometric models.