Narratives to Numbers: Large Language Models and Economic Policy Uncertainty
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摘要
This paper evaluates fine-tuned large language models (LLMs) as probabilistic classifiers for Economic Policy Uncertainty (EPU), showing LLMs substantially outperform traditional keyword (BOW) and SVM approaches at the article level, that these gains materially change index construction through thresholding and aggregation choices, and that the same models enable historical (19th-century U.S.) and multilingual EPU measures—arguing LLMs should be treated as explicit, estimable parts of the data‑generating process [page::0].
