ChatMacro: Evaluating Inflation Forecasts of Generative AI

M. Jahangir Alam, Shane Boyle, Huiyu Li and Tatevik Sekhposyan

No 21057, CEPR Discussion Papers from Centre for Economic Policy Research

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.

Date: 2026-01
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