|
|
--- |
|
|
license: apache-2.0 |
|
|
tags: |
|
|
- Econometric |
|
|
- AI |
|
|
--- |
|
|
|
|
|
# Data Card for Econometric AI Agent Testset |
|
|
|
|
|
### Dataset Summary |
|
|
|
|
|
This sample test dataset pertains to the study titled [Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks](https://arxiv.org/abs/2506.00856). |
|
|
Our goal is to establish a standardized benchmark to evaluate the ability of artificial intelligence models and(or) agents in executing econometric tasks. |
|
|
We invite contributions to expand this econometric task repository and to assess the performance of various large language models (LLMs) and AI agents. |
|
|
If you use our dataset, please kindly cite the original source. |
|
|
|
|
|
- **Repository:** https://github.com/HKU-Business-AI-Center/Econometrics-Agent |
|
|
- **Paper:** https://arxiv.org/abs/2506.00856 |
|
|
|
|
|
## Citation Information |
|
|
```bibtex |
|
|
@misc{chen2025aimastereconometricsevidence, |
|
|
title={Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks}, |
|
|
author={Qiang Chen and Tianyang Han and Jin Li and Ye Luo and Yuxiao Wu and Xiaowei Zhang and Tuo Zhou}, |
|
|
year={2025}, |
|
|
eprint={2506.00856}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={econ.EM}, |
|
|
url={https://arxiv.org/abs/2506.00856}, |
|
|
}} |
|
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|