AI能像专家一样做复杂计量分析，且表现远超普通大模型。
Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks
- 基于MetaGPT构建专用计量经济AI代理，可自主规划、写代码并反思纠错。
- 在真实课程与论文数据集上，性能显著优于主流大模型和通用AI代理。
- 适合需要快速完成计量分析的研究者或教学场景，降低技术门槛。
AI能否有效执行传统上需人类专家才能完成的复杂计量经济分析？本文评估了AI代理在实证分析任务中的能力，开发了基于开源MetaGPT框架的「MetricsAI」计量经济AI代理。该代理在策略性规划、代码生成与执行、基于错误的反思优化以及多轮迭代改进方面表现出色。我们从学术课程材料和已发表研究论文中构建了两个数据集，用于评估其在真实挑战下的表现。对比测试显示，该领域专用AI代理显著优于基准大语言模型（LLMs）和通用型AI代理。本研究建立了社会科学研究中AI影响的评测基准，实现低成本引入专业领域知识，使编码能力有限的用户也能使用高级计量方法。此外，该代理提升了研究可复现性，并具备良好的计量经济学教学应用前景。
原文摘要 · Abstract (English)
Can AI effectively perform complex econometric analysis traditionally requiring human expertise? This paper evaluates AI agents' capability to master econometrics, focusing on empirical analysis performance. We develop ``MetricsAI'', an Econometrics AI Agent built on the open-source MetaGPT framework. This agent exhibits outstanding performance in: (1) planning econometric tasks strategically, (2) generating and executing code, (3) employing error-based reflection for improved robustness, and (4) allowing iterative refinement through multi-round conversations. We construct two datasets from academic coursework materials and published research papers to evaluate performance against real-world challenges. Comparative testing shows our domain-specialized AI agent significantly outperforms both benchmark large language models (LLMs) and general-purpose AI agents. This work establishes a testbed for exploring AI's impact on social science research and enables cost-effective integration of domain expertise, making advanced econometric methods accessible to users with minimal coding skills. Furthermore, our AI agent enhances research reproducibility and offers promising pedagogical applications for econometrics teaching.
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