Search Results
Working Paper
The Fed's Fine-Tune: Coarse Statements and Predictive Pressers
Central bank communications, particularly FOMC statements and press conferences, play a crucial role in shaping financial market expectations. Using large language models to quantify central bank content, this paper demonstrates how sentiment aligns with traditional market-based monetary policy measures. We show that press conferences correlate with future policy to a greater extent than other communications. While FOMC statements coarsely signal the current stance of policy, press conferences fine-tune the message, which helps market participants revise their expectations about future policy.
Journal Article
Artificial Intelligence and Inflation Forecasts
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.
Discussion Paper
Combining AI and Established Methods for Historical Document Analysis
This paper examines methodological approaches for extracting structured data from large-scale historical document archives, comparing “hyperspecialized” versus “adaptive modular” strategies. Using 56 years of Philadelphia property deeds as a case study, we show the benefits of the adaptive modular approach leveraging optical character recognition (OCR), full-text search, and frontier large language models (LLMs) to identify deeds containing specific restrictive use language— achieving 98% precision and 90–98% recall. Our adaptive modular methodology enables analysis of ...