Title: Inflation expectations and consumption with machine learning
Authors: Diana Gabrielyan - University of Tartu (Estonia)
Lenno Uuskula - University of Tartu (Estonia) [presenting]
Abstract: Measures of inflation expectations are extracted from online news to build real interest rates that capture underlying consumer expectations. The new measure is infused into various Euler consumption models. While benchmark models based on traditional risk-free returns rates fail, models built with novel news-driven inflation expectations indices improve upon benchmark models and result in strong instruments. Our positive findings highlight the role played by the media for consumer expectation formation and allow for the use of such novel data sources for other key macroeconomic relationships.