報告人：劉婧媛 教授
報告題目：LLM-Powered Deep Panel Modeling
報告時間：2026年5月8日（周五）10：30-11：30
報告地點：云龍校區6號樓318會議室
主辦單位：數學與統計學院、數學研究院、科學技術研究院
報告人簡介：
劉婧媛，廈門大學經濟學院統計學與數據科學系南強特聘教授、博士生導師，國家級高層次人才（教育部），廈門大學南強卓越教學名師、南強青年拔尖人才（A類）、廈門大學“我最喜愛的十位教師”。美國賓夕法尼亞州立大學統計學博士。科研方面主要從事高維及復雜數據的統計方法、因果中介效應分析、大模型輔助統計建模、多數據源整合等領域的工作，在JASA、JMLR、JOE等國際權威學術期刊發表論文40余篇，擔任JASA、JBES和AOAS編委，入選福建省杰出青年科研人才計劃。
報告摘要：
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.