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Temporal Consistency of Forecasts and Data Releases

Author(s)
Bürgi, Constantin  
Ortiz, Julio L.  
Uri
http://hdl.handle.net/10197/12780
Date Issued
2022-02-11
Date Available
2022-02-28T16:58:26Z
Abstract
We provide key insights on expectation formation based on the Bloomberg eco- nomic survey: around two thirds of professional forecasters provide GDP forecasts that are temporally consistent, meaning that quarterly forecasts add up to the annual. Temporally consistent forecasts are not more accurate than the inconsistent ones, but inconsistent ones might drive estimates of information frictions in some cases. For the overwhelming majority of consistent forecasts, annual GDP predictions almost imme- diately reflect the monthly GDP releases. These findings suggest that most forecasters make at least minor forecast updates after each data release. Indeed, the inattention rate is found to be between 3% and 6% at the quarterly frequency.
Type of Material
Working Paper
Publisher
University College Dublin. School of Economics
Start Page
1
End Page
14
Series
UCD Centre for Economic Research Working Paper Series
WP2022/07
Copyright (Published Version)
2022 the Authors
Subjects

Temporal forecast rec...

Temporal aggregation

Bloomberg survey

GDP

Forecast accuracy

Classification
E37
Language
English
Status of Item
Not peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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WP22_07.pdf

Size

378.07 KB

Format

Adobe PDF

Checksum (MD5)

14b4c767cd25faaaf44648bcab740523

Owning collection
Economics Working Papers & Policy Papers

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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