讲座题目：From Micro to Macro: Learning Real-Time Economic Signals from Firm-Level Accounting Data
内容摘要：How can we learn real-time information about aggregate economic activity from firm-level accounting data? This paper proposes a micro-to-macro nowcasting framework that uses machine learning algorithms to directly exploit accounting information from 21,061 publicly listed U.S. firms to nowcast U.S. aggregate output, thereby preserving the rich information embedded in firm-level heterogeneity and cross-firm interactions that is often lost in aggregated data. The empirical results show that the proposed approach significantly improves nowcasting accuracy, reducing the root mean squared error by more than 70% relative to a random walk benchmark and substantially outperforming models based on aggregated accounting data. Firm-level corporate accounting data also improve nowcasting performance by about 17.35% relative to models using mixed-frequency aggregate macroeconomic and financial predictors. Overall, the results underscore the value of directly exploiting firm-level accounting data for nowcasting aggregate economic activity.
主讲人简介：黄乃静，中央财经大学经济学院教授，博士生导师，国民经济系主任，美国波士顿学院经济学博士。以宏观大数据分析，宏观金融风险预测为主要研究方向，先后在Management Science、《管理科学学报》、《经济学动态》、《中国软科学》、《光明日报》（理论版）等国内外学术刊物发表多篇论文。主持国家自然科学基金青年项目，面上项目，参与多项国家社会科学基金重大课题。
报告时间：2026年3月31日(周二)，16:30-18:00
线下地点：厦门大学经济楼C108
线上地点：腾讯会议 ID：275 181 086
