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Artificial Intelligence and Inflation Forecasts
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DOI：10.20955/r.2024.12.png)
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Abstract
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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.
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