RAUI: Uncertainty Indicators Built With Artificial Intelligence

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.