Working Paper Revision
Artificial Intelligence and Inflation Forecasts
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;
JEL Classification: E31; E37; C45; C53;
https://doi.org/10.20955/wp.2023.015
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Bibliographic Information
Provider: Federal Reserve Bank of St. Louis
Part of Series: Working Papers
Publication Date: 2024-02-26
Number: 2023-015
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