Time-Varying Dictionary and the Predictive Power of FED Minutes

Abstract

This paper develops a novel method to extract the most predictive information from FED minutes. Instead of considering a dictionary (set of words) with a fixed content, we construct a dictionary whose content is allowed to change over time. Specifically, we utilize machine learning to identify the most predictive words (the most predictive content) of a given minute and use them to derive new predictors. We show that the new predictors improve real time forecasts of output growth by a statistically significant margin, suggesting that the combination of supervised machine learning and text regression can be interpreted as a powerful device for out-of-sample macroeconomic forecasting.

Keywords: Text Regression, Supervised Machine Learning, Elastic Net, Central Bank Communication, Forecasting, Real Time

JEL Classification: 53, C55, E37, E47