Tracking Inattention

Nathan Goldstein

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)2682-2725
Number of pages44
JournalJournal of the European Economic Association
Volume21
Issue number6
DOIs
StatePublished - 1 Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of European Economic Association.

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