Using Transformers and Reinforcement Learning as Narrative Filters in Macroeconomics*

Vegard H. Larsen () and Leif Anders Thorsrud ()

No No 02/2026, Working Papers from Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School

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.

Pages: 70 pages
Date: 2026-02
New Economics Papers: this item is included in nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:bny:wpaper:0147

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