Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics

Vegard H. Larsen and Leif Anders Thorsrud

No 12454, CESifo Working Paper Series from CESifo

Abstract: Building on recent advances in Natural Language Processing and modeling of sequences, we study how a multimodal Transformer-based deep learning architecture can be used for measurement and structural narrative attribution in macroeconomics. The framework we propose combines (news) text and (macroeconomic) time series information using cross-attention mechanisms, easily incorporates differences in data frequencies and reporting delays, and can be used together with Reinforcement Learning to produce structurally coherent summaries of high-frequency news flows. Applied and tested on both simulated and real-world data out-of-sample, the results we obtain are encouraging.

Keywords: multimodal transformer; structural decomposition; text analytics; macroeconomic nowcasting (search for similar items in EconPapers)
JEL-codes: C45 C55 E32 E37 (search for similar items in EconPapers)
Date: 2026
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