Forecasting GDP Growth Rates Using Accounting Earnings: A Large Panel Microdata Approach

• 成果类型: Article; Early Access
• 署名作者: Cui, Yumeng; Hong, Yongmiao; Huang, Naijing; Wang, Yicheng
• 署名单位: Central University of Finance & Economics; Chinese Academy of Sciences; Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Peking University Shenzhen Graduate School (PKU Shenzhen); Peking University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2025.01549
• 发表日期: 2026
• 关键词: GDP forecasting; Heterogeneity; firm accounting earnings; Large panel

摘要

Economists and econometricians typically use aggregate economic and financial variables for gross domestic product (GDP) prediction. However, aggregation often results in a loss of valuable information, diminishing key features such as heterogeneity, interactions, nonlinearity, and structural breaks. We propose a novel microforecasting approach, using large panel data of firm accounting earnings from corporate financial reports to forecast GDP. By employing machine learning methods, we can effectively exploit this large microlevel information set to achieve substantially more accurate GDP forecasts. Our findings highlight the advantages and potential of utilizing microlevel data for macroprediction, diverging from the conventional macroforecasting paradigm that relies on aggregate data to forecast macrovariables.