Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks
Jari Hännikäinen ()
Journal of Econometric Methods, 2017, vol. 6, issue 1, 22
In this paper, we analyze the forecasting performance of a set of widely used window selection methods in the presence of data revisions and recent structural breaks. Our Monte Carlo and empirical results for U.S. real GDP and inflation show that the expanding window estimator often yields the most accurate forecasts after a recent break. It performs well regardless of whether the revisions are news or noise, or whether we forecast first-release or final values. We find that the differences in the forecasting accuracy are large in practice, especially when we forecast inflation after the break of the early 1980s.
Keywords: choice of estimation window; forecasting; real-time data; recent structural break (search for similar items in EconPapers)
JEL-codes: C22 C53 C82 (search for similar items in EconPapers)
References: Add references at CitEc
Citations Track citations by RSS feed
Downloads: (external link)
https://www.degruyter.com/view/j/jem.2017.6.issue- ... -0021.xml?format=INT (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
Working Paper: Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks (2016)
Working Paper: Selection of an estimation window in the presence of data revisions and recent structural breaks (2015)
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:bpj:jecome:v:6:y:2017:i:1:p:22:n:9
Ordering information: This journal article can be ordered from
Access Statistics for this article
More articles in Journal of Econometric Methods from De Gruyter
Bibliographic data for series maintained by Peter Golla ().