Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks

• URL: http://arxiv.org/abs/2506.00856v1
• Date: Sun, 01 Jun 2025 06:34:42 GMT
• Title: Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks
• Authors: Qiang Chen, Tianyang Han, Jin Li, Ye Luo, Yuxiao Wu, Xiaowei Zhang, Tuo Zhou,
• Abstract summary: We develop an Econometrics AI Agent'' built on the open-source MetaGPT framework.<n>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.
• Score: 9.52446148818128
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Can AI effectively perform complex econometric analysis traditionally requiring human expertise? This paper evaluates an agentic AI's capability to master econometrics, focusing on empirical analysis performance. We develop 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 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 expertise. Furthermore, our agent enhances research reproducibility and offers promising pedagogical applications for econometrics teaching.

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