“Parsing the pulse: Decomposing macroeconomic sentiment with LLMS”

Abstract

Macroeconomic indicators provide quantitative signals that must be pieced to gether and interpreted by economists. We propose a reversed approach of parsing press narratives directly using Large Language Models (LLM) to recover growth and inflation sentiment indices. A key advantage of this LLM-based approach is the ability to decompose aggregate sentiment into its drivers, readily enabling an interpretation of macroeconomic dynamics. Our sentiment indices track hard-data counterparts closely, providing an accurate, near real-time picture of the macroe conomy. Their components–demand, supply, and deeper structural forces–are in tuitive and consistent with prior model-based studies. Incorporating sentiment indices improves the forecasting performance of simple statistical models, pointing to information unspanned by traditional data

About the speaker

Frank Smets is Acting Head of the Monetary and Economic Department and Head of Economic Analysis and Statistics. He is a member of the Bank’s senior management team.