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Artificial Intelligence and Inflation Forecasts

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Abstract

We explore the ability of large language models (LLMs) to produce in-sample conditional inflation forecasts during the 2019–23 period. We use a leading LLM (Google AI’s PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years and at almost all horizons. LLM forecasts exhibit slower reversion to the 2 percent inflation anchor.

Suggested Citation

  • Miguel Faria-e-Castro & Fernando Leibovici, 2024. "Artificial Intelligence and Inflation Forecasts," Review, Federal Reserve Bank of St. Louis, vol. 106(12), pages 1-14, November.
    • Handle: RePEc:fip:fedlrv:99416
    DOI: 10.20955/r.2024.12
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    Cited by:

    1. Seiler, Pascal, 2025. "Measuring economic sentiment from open-ended survey comments using large language models," Economics Letters, Elsevier, vol. 256(C).
    2. Chen Gao & Xiaochong Lan & Nian Li & Yuan Yuan & Jingtao Ding & Zhilun Zhou & Fengli Xu & Yong Li, 2024. "Large language models empowered agent-based modeling and simulation: a survey and perspectives," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-24, December.
    3. Alexander Eliseev & Sergei Seleznev, 2026. "Fake Date Tests: Can We Trust In-sample Accuracy of LLMs in Macroeconomic Forecasting?," Papers 2601.07992, arXiv.org, revised Mar 2026.
    4. Leland D. Crane & Akhil Karra & Paul E. Soto, 2025. "Total Recall? Evaluating the Macroeconomic Knowledge of Large Language Models," Finance and Economics Discussion Series 2025-044, Board of Governors of the Federal Reserve System (U.S.).
    5. Tom Coupé, 2024. "Revealed Preferences: ChatGPT’s Opinion on Economic Issues and the Economics Profession," Working Papers in Economics 24/13, University of Canterbury, Department of Economics and Finance.
    6. Nikoleta Anesti & Edward Hill & Andreas Joseph, 2025. "Inflation Attitudes of Large Language Models," Papers 2512.14306, arXiv.org.

    More about this item

    Keywords

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    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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