Automation of Text-Based Economic Indicator Construction: A Pilot Exploration on Economic Policy Uncertainty Index
Year: 2024
Type: conference-paper
Abstract: 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, a representative keyword-based economic indicator utilized across numerous countries. Our findings affirm the utility of LLMs in facilitating the automation of text-based EPU index construction. Furthermore, we delve into the potential of LLMs in enhancing the indicator construction process.
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4403577902