Artificial Intelligence and Inflation Forecasts

Miguel Faria-e-Castro and Fernando Leibovici

Review, 2024, vol. 106, issue 12, 14 pages

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.

Keywords: large language models (LLMs); artificial intelligence (AI); inflation forecasts; Survey of Professional Forecasters (SPF) (search for similar items in EconPapers)
JEL-codes: C45 C53 E31 E37 (search for similar items in EconPapers)
Date: 2024
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Working Paper: Artificial Intelligence and Inflation Forecasts (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedlrv:99416

DOI: 10.20955/r.2024.12

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