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ChatMacro: Evaluating Inflation Forecasts of Generative AI

Author

Listed:
  • Alam, M. Jahangir
  • Boyle, Shane
  • Li, Huiyu
  • Sekhposyan, Tatevik

Abstract

Recent research suggests that generic large language models (LLMs) can match the accuracy of traditional methods when forecasting macroeconomic variables in pseudo out-of-sample settings generated via prompts. This paper assesses the out-of-sample forecasting accuracy of LLMs by eliciting real-time forecasts of U.S. inflation from ChatGPT. We find that out-of-sample predictions are largely inaccurate and stale, even though forecasts generated in pseudo out-of-sample environments are comparable to existing benchmarks. Our results underscore the importance of out-of-sample benchmarking for LLM predictions.

Suggested Citation

  • Alam, M. Jahangir & Boyle, Shane & Li, Huiyu & Sekhposyan, Tatevik, 2026. "ChatMacro: Evaluating Inflation Forecasts of Generative AI," CEPR Discussion Papers 21057, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:21057
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    Cited by:

    1. Dalibor Stevanovic, 2026. "Who Saw It Coming? Historical Experience and the 2021 Inflation Forecast Failure," Papers 2604.14467, arXiv.org.

    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • 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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