Artificial Intelligence and Inflation Forecasts

RePEc Short ID: pfa225
ORCID iD: 0000-0002-3759-2172

Faria-e-Castro, Miguel; Leibovici, Fernando

doi:10.20955/wp.2023.015

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