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Large language models: a primer for economists

Author

Listed:
  • Byeungchun Kwon
  • Taejin Park
  • Fernando Perez-Cruz
  • Phurichai Rungcharoenkitkul

Abstract

Large language models (LLMs) are powerful tools for analysing textual data, with substantial untapped potential in economic and central banking applications. Vast archives of text, including policy statements, financial reports and news, offer rich opportunities for analysis. This special feature provides an accessible introduction to LLMs aimed at economists and offers applied researchers a practical walkthrough of their use. We provide a step-by-step guide on the use of LLMs covering data organisation, signal extraction, quantitative analysis and output evaluation. As an illustration, we apply the framework to analyse perceived drivers of stock market dynamics based on over 60,000 news articles between 2021 and 2023. While macroeconomic and monetary policy news are important, market sentiment also exerts substantial influence.

Suggested Citation

  • Byeungchun Kwon & Taejin Park & Fernando Perez-Cruz & Phurichai Rungcharoenkitkul, 2024. "Large language models: a primer for economists," BIS Quarterly Review, Bank for International Settlements, December.
    • Handle: RePEc:bis:bisqtr:2412b
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    References listed on IDEAS

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    1. Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
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    Cited by:

    1. Aijie Shu & Wenbin Wu & Gbenga Ibikunle & Fengxiang He, 2026. "DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks," Papers 2602.03981, arXiv.org.
    2. Douglas Araujo & Rafael Schmidt & Olivier Sirello & Bruno Tissot & Ricardo Villarreal, 2025. "Governance and implementation of artificial intelligence in central banks," IFC Reports 18, Bank for International Settlements.
    3. Camille Jehle & Florian Le Gallo, 2025. "Europe in the Headlines: What Two Decades of French News Reveal about EU Sentiment," Working papers 1008, Banque de France.
    4. SEKINE, Toshitaka & WADA, Tetsuro, 2025. "How Did People Tweet against Inflation in Japan?," Discussion paper series HIAS-E-150, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    5. Philippe Goulet Coulombe, 2025. "Ordinary Least Squares as an Attention Mechanism," Papers 2504.09663, arXiv.org, revised Jan 2026.
    6. George Fatouros & Kostas Metaxas & John Soldatos & Manos Karathanassis, 2025. "MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents," Papers 2502.00415, arXiv.org, revised Oct 2025.

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    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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