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

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Listed:
  • Qiang Chen
  • Tianyang Han
  • Jin Li
  • Ye Luo
  • Yuxiao Wu
  • Xiaowei Zhang
  • Tuo Zhou

Abstract

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 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.

Suggested Citation

  • Qiang Chen & Tianyang Han & Jin Li & Ye Luo & Yuxiao Wu & Xiaowei Zhang & Tuo Zhou, 2025. "Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks," Papers 2506.00856, arXiv.org, revised Jun 2025.
    • Handle: RePEc:arx:papers:2506.00856
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    References listed on IDEAS

    as
    1. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2025. "Generative AI at Work," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 140(2), pages 889-942.
    2. Margarita Leib & Nils Köbis & Rainer Michael Rilke & Marloes Hagens & Bernd Irlenbusch, 2023. "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty," ECONtribute Discussion Papers Series 251, University of Bonn and University of Cologne, Germany.
    3. Douglas Almond & Kenneth Y. Chay & David S. Lee, 2005. "The Costs of Low Birth Weight," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(3), pages 1031-1083.
    4. Gorodnichenko, Yuriy & Pham, Tho & Talavera, Oleksandr, 2025. "Central bank communication on social media: What, to whom, and how?," Journal of Econometrics, Elsevier, vol. 249(PC).
    5. Ahrens, Maximilian & Erdemlioglu, Deniz & McMahon, Michael & Neely, Christopher J. & Yang, Xiye, 2025. "Mind your language: Market responses to central bank speeches," Journal of Econometrics, Elsevier, vol. 249(PC).
    6. Margarita Leib & Nils Kobis & Rainer Michael Rilke & Marloes Hagens & Bernd Irlenbusch, 2023. "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty," Papers 2301.01954, arXiv.org.
    7. Card, David & Sullivan, Daniel G, 1988. "Measuring the Effect of Subsidized Training Programs on Movements in and out of Employment," Econometrica, Econometric Society, vol. 56(3), pages 497-530, May.
    8. Leib, Margarita & Köbis, Nils & Rilke, Rainer Michael & Hagens, Marloes & Irlenbusch, Bernd, 2023. "Corrupted by Algorithms? How AI-Generated and Human-Written Advice Shape (Dis)Honesty," IZA Discussion Papers 16293, Institute of Labor Economics (IZA).
    9. Cattaneo, Matias D., 2010. "Efficient semiparametric estimation of multi-valued treatment effects under ignorability," Journal of Econometrics, Elsevier, vol. 155(2), pages 138-154, April.
    10. Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
    11. Noailly, Joëlle & Nowzohour, Laura & van den Heuvel, Matthias & Pla, Ireneu, 2024. "Heard the news? Environmental policy and clean investments," Journal of Public Economics, Elsevier, vol. 238(C).
    12. Niu, Yimeng & Wu, Jing & Jiang, Shenyang & Jiang, Zhibin, 2025. "The bullwhip effect in servitized manufacturers," Other publications TiSEM 638f661c-295a-4ada-8c33-a, Tilburg University, School of Economics and Management.
    13. Ali Goli & Amandeep Singh, 2024. "Frontiers: Can Large Language Models Capture Human Preferences?," Marketing Science, INFORMS, vol. 43(4), pages 709-722, July.
    14. Thomas Graeber & Christopher Roth & Florian Zimmermann, 2024. "Stories, Statistics, and Memory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(4), pages 2181-2225.
    15. Peiyao Li & Noah Castelo & Zsolt Katona & Miklos Sarvary, 2024. "Frontiers: Determining the Validity of Large Language Models for Automated Perceptual Analysis," Marketing Science, INFORMS, vol. 43(2), pages 254-266, March.
    16. Anderson, Michael, 2008. "Safety for whom? The effects of light trucks on traffic fatalities," Journal of Health Economics, Elsevier, vol. 27(4), pages 973-989, July.
    17. Yang Chen & Samuel N. Kirshner & Anton Ovchinnikov & Meena Andiappan & Tracy Jenkin, 2025. "A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?," Manufacturing & Service Operations Management, INFORMS, vol. 27(2), pages 354-368, March.
    18. repec:cdl:agrebk:qt1mb4m7qz is not listed on IDEAS
    19. Ishita Chakraborty & Khai Chiong & Howard Dover & K. Sudhir, 2025. "Can AI and AI-Hybrids Detect Persuasion Skills? Salesforce Hiring with Conversational Video Interviews," Marketing Science, INFORMS, vol. 44(1), pages 30-53, January.
    20. Gmyrek, Pawel, & Lutz, Christoph, & Newlands, Gemma,, 2024. "A Technological Construction of Society comparing GPT-4 and human respondents for occupational evaluation in the UK," ILO Working Papers 995347793502676, International Labour Organization.
    21. Bryan K. Stroube & David M. Waguespack, 2024. "Status and consensus: Heterogeneity in audience evaluations of female‐ versus male‐lead films," Strategic Management Journal, Wiley Blackwell, vol. 45(5), pages 994-1024, May.
    22. Matias D. Cattaneo, 2010. "multi-valued treatment effects," The New Palgrave Dictionary of Economics,, Palgrave Macmillan.
    23. Yimeng Niu & Jing Wu & Shenyang Jiang & Zhibin Jiang, 2025. "The Bullwhip Effect in Servitized Manufacturers," Management Science, INFORMS, vol. 71(1), pages 1-20, January.
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