Temporal Consistency of Forecasts and Data Releases

Constantin Bürgi and Julio L. Ortiz

No 202207, Working Papers from School of Economics, University College Dublin

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.

Keywords: Temporal forecast reconciliation; Temporal aggregation; Bloomberg survey; GDP; Forecast accuracy (search for similar items in EconPapers)
JEL-codes: E37 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2022-02
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10197/12780 First version, 2022 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ucn:wpaper:202207

Access Statistics for this paper

More papers in Working Papers from School of Economics, University College Dublin Contact information at EDIRC.
Bibliographic data for series maintained by UCD School of Economics ().