Artificial Intelligence and Inflation Forecasts

Miguel Faria-e-Castro and Fernando Leibovici

No 2023-015, Working Papers from Federal Reserve Bank of St. Louis

Abstract: We explore the ability of Large Language Models (LLMs) to produce in-sample conditional inflation forecasts during the 2019-2023 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% inflation anchor.

Keywords: inflation forecasts; large language models; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C45 C53 E31 E37 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2023-07-14, Revised 2024-02-26
New Economics Papers: this item is included in nep-ain, nep-ban, nep-big, nep-cmp and nep-mon
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Citations: View citations in EconPapers (1)

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Journal Article: Artificial Intelligence and Inflation Forecasts (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedlwp:96478

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DOI: 10.20955/wp.2023.015

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