Tracking Inattention

Nathan Goldstein

Journal of the European Economic Association, 2023, vol. 21, issue 6, 2682-2725

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:oup:jeurec:v:21:y:2023:i:6:p:2682-2725.

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