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- W4360602011 abstract "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." @default.
- W4360602011 created "2023-03-24" @default.
- W4360602011 creator A5003261736 @default.
- W4360602011 date "2023-03-23" @default.
- W4360602011 modified "2023-10-01" @default.
- W4360602011 title "Tracking Inattention" @default.
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- W4360602011 doi "https://doi.org/10.1093/jeea/jvad022" @default.
- W4360602011 hasPublicationYear "2023" @default.
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