RAUI: Uncertainty Indicators Built With Artificial Intelligence

Morteza Ghomi and Samuel Hurtado
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Morteza Ghomi: BANCO DE ESPAÑA

No 2609, Working Papers from Banco de España

Abstract: We present a methodology for generating uncertainty indicators for user-defined topics based on newspaper data. The approach is based on Retrieval-Augmented Generation (RAG) systems commonly used in artificial intelligence applications, which we adapt to construct topic-specific uncertainty measures, referred to as Retrieval-Augmented Uncertainty Indicators (RAUI). The method employs semantic search with an embedding model to select news articles relevant to a given topic, and a large language model (LLM) to quantify the level of uncertainty contained in each of those articles. We construct uncertainty indicators for ten topics using Spanish newspaper data and an aggregate measure that also highlights how each topic contributes to overall uncertainty. We present two practical applications of these indicators: a VAR analysis that shows how different sources of uncertainty have different effects on the Spanish economy, and an estimation that generates time-varying fan charts around the Banco de España GDP growth projections.

Keywords: uncertainty; artificial intelligence; natural language processing; newspapers (search for similar items in EconPapers)
JEL-codes: C81 E32 (search for similar items in EconPapers)
Pages: 76 pages
Date: 2026-03
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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:2609

DOI: 10.53479/42605

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