Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.00856 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915758259306496 |
|---|---|
| author | Chen, Qiang Han, Tianyang Li, Jin Luo, Ye Wang, Zigan Wu, Yuxiao Zhang, Xiaowei Zhou, Tuo |
| author_facet | Chen, Qiang Han, Tianyang Li, Jin Luo, Ye Wang, Zigan Wu, Yuxiao Zhang, Xiaowei Zhou, Tuo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00856 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks Chen, Qiang Han, Tianyang Li, Jin Luo, Ye Wang, Zigan Wu, Yuxiao Zhang, Xiaowei Zhou, Tuo Econometrics Artificial Intelligence 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. |
| title | Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks |
| topic | Econometrics Artificial Intelligence |
| url | https://arxiv.org/abs/2506.00856 |