Working Paper Revision

Artificial Intelligence and Inflation Forecasts


Abstract: We explore the ability of Large Language Models (LLMs) to produce 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. We argue that this method of generating forecasts is inexpensive and can be applied to other time series.

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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Provider: Federal Reserve Bank of St. Louis

Part of Series: Working Papers

Publication Date: 2023-07-14

Number: 2023-015

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