Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/296334 
Year of Publication: 
2023
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 14 [Issue:] 2 [Year:] 2023 [Pages:] 609-650
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
Expectations affect economic decisions, and inaccurate expectations are costly. Expectations can be wrong due to either bias (systematic mistakes) or noise (unsystematic mistakes). We develop a framework for quantifying the level of noise in survey expectations. The method is based on the insight that theoretical models of expectation formation predict a factor structure for individual expectations. Using data from professional forecasters, we find that the magnitude of noise is large (10%-30% of forecast MSE) and comparable to bias. We illustrate how our estimates can be applied to calibrate models with incomplete information and bound the effects of measurement error.
Subjects: 
Expectation formation
factor models
measurement error
noise
panel data
subjective expectations
JEL: 
C53
D83
E70
G40
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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