Tracking Innatention

This study proposes a real-time estimate of inattention, based on micro-level data. I show that a simple specification that estimates the persistence of a forecaster’s deviation from the mean provides a direct estimate of parameters of information frictions according to prominent models of expectations. The new estimate can also be interpreted as a hybrid measure of both information frictions and behavioral frictions. Using the new specification, I revise several key findings documented in the previous literature. I find higher levels of inattention and document new forms of variations over time and across variables, horizons, individuals, and types of agents. I also report new results from long-run forecasts and document an unprecedented response to COVID-19. (JEL: E31; E37; E47; D83; D84)

Keywords: Expectations, Rational Inattention, Information Frictions, Behavioral Frictions, COVID- 19.