Automation of Text-Based Economic Indicator Construction: A Pilot Exploration on Economic Policy Uncertainty Index
Published in In Proceedings of The 33rd ACM International Conference on Information and Knowledge Management, 2024
The growing popularity of text-as-data in various domain-specific applications and research has often relied on manually selected keywords or annotations. Although labor-intensive, expensive and time-consuming, the effectiveness of these efforts is not always guaranteed, especially in the early stages of research. This predicament raises the question of the extent to which large language models (LLMs) can aid in verifying the potential of a nascent research idea. This paper seeks to explore the reliability of LLM-suggested keywords in the automatic construction of the Economic Policy Uncertainty (EPU) index. Our findings confirm that LLMs can effectively automate the construction of EPU index. Furthermore, we delve into the potential of LLMs in enhancing the indicator construction process.
Recommended citation: Hsiu-Hsuan Yeh, Yu-Lieh Huang, Ziho Park, and Chung-Chi Chen. 2024. Automation of Text-Based Economic Indicator Construction: A Pilot Exploration on Economic Policy Uncertainty Index. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24).
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