Lag length and mean break in stationary VAR models
Minxian Yang
Econometrics Journal, 2002, vol. 5, issue 2, 374-387
Abstract:
We consider three approaches to determine the lag length of a stationary vector autoregression model and the presence of a mean break. The first approach, commonly used in practice, uses a break test as a specification check after the lag length is selected by an information criterion. The second performs the break test prior to estimating the lag length. The third simultaneously selects both the lag length and the break by some information criterion. While the latter two approaches are consistent for the true lag order, we justify the validity of the first approach by showing that the lag length estimator based on specific information criteria is at worst biased upwards asymptotically when the mean break is ignored. Thus, conditional on the estimated lag length, the break test retains its asymptotic power properties. Finite-sample simulation results show that the second approach tends to have the most stable performance. The results also indicate that the best strategy for short-run forecasting does not necessarily coincide with the best strategy for finding the correct model. Copyright Royal Economic Society, 2002
Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (6)
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
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:ect:emjrnl:v:5:y:2002:i:2:p:374-387
Ordering information: This journal article can be ordered from
http://www.ectj.org
Access Statistics for this article
Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms
More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and Christopher F. Baum ().