Testing for News and Noise in Non-Stationary Time Series Subject to Multiple Historical Revisions
Alain Hecq (),
Jan Jacobs () and
Michalis P. Stamatogiannis
CIRANO Working Papers from CIRANO
Before being considered definitive, data currently produced by statistical agencies undergo a recurrent revision process resulting in different releases of the same phenomenon. The collection of all these vintages is referred to as a real-time data set. Economists and econometricians have realized the importance of this type of information for economic modeling and forecasting. This paper focuses on testing non-stationary data for forecastability, i.e., whether revisions reduce noise or are news. To deal with historical revisions which affect the whole vintage of time series due to redefinitions, methodological innovations etc., we employ the recently developed impulse indicator saturation approach, which involves potentially adding an indicator dummy for each observation to the model. We illustrate our procedures with the U.S. Real Gross National Product series from ALFRED and find that revisions to this series neither reduce noise nor can be considered as news.
Keywords: Data revision; Non-Stationary Data; News-Noise Tests; Structural Breaks (search for similar items in EconPapers)
JEL-codes: C32 C82 E01 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Journal Article: Testing for news and noise in non-stationary time series subject to multiple historical revisions (2019)
Working Paper: Testing for news and noise in non-stationary time series subject to multiple historical revisions (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cir:cirwor:2016s-01
Access Statistics for this paper
More papers in CIRANO Working Papers from CIRANO Contact information at EDIRC.
Bibliographic data for series maintained by Webmaster ().