mlsanityData to 14 Oct 2026

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

arXiv 2506.00856 ↗First posted 1 Jun 2025econ.EM cs.AIcited by 0

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 54 references that are in the map. First posted on arXiv 1 Jun 2025.

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14 of 54 references.

References · 14

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  3. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,729
  4. 2023BloombergGPT: A Large Language Model for Financecited by 939
  5. 2024Data Interpreter: An LLM Agent For Data Sciencecited by 225
  6. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167
  7. 2024Does Prompt Formatting Have Any Impact on LLM Performance?cited by 141
  8. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 135
  9. 2024Understanding the Performance and Estimating the Cost of LLM Fine-Tuningcited by 48
  10. 2024Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalabilitycited by 40
  11. 2023Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendationscited by 16
  12. 2024Integrating Chemistry Knowledge in Large Language Models via Prompt Engineeringcited by 10
2 more
  1. 2024Mining Causality: AI-Assisted Search for Instrumental Variablescited by 7
  2. 2023Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honestycited by 3

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