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ChatMacro: Evaluating Inflation Forecasts of Generative AI

Author

Listed:
  • M.Jahangir Alam

  • Shane Boyle

  • Huiyu Li
  • Tatevik Sekhposyan

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

  • M.Jahangir Alam & Shane Boyle & Huiyu Li & Tatevik Sekhposyan, 2026. "ChatMacro: Evaluating Inflation Forecasts of Generative AI," Working Paper Series 2026-04, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfwp:102407
    DOI: 10.24148/wp2026-04
    Note: PDF date: January 27, 2006.
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    Keywords

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    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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