 Least squares estimation and tests of breaks in mean and variance under misspecificationEconometrics Journal, 2004, vol. 7, issue 1, 32-54 Abstract: In this paper we investigate the consequences of misspecification on the large sample properties of change-point estimators and the validity of tests of the null hypothesis of linearity versus the alternative of a structural break. Specifically this paper concentrates on the interaction of structural breaks in the mean and variance of a time series when either of the two is omitted from the estimation and inference procedures. Our analysis considers the case of a break in mean under omitted-regime-dependent heteroscedasticity and that of a break in variance under an omitted mean shift. The large and finite sample properties of the resulting least-squares-based estimators are investigated and the impact of the two types of misspecification on inferences about the presence or absence of a structural break subsequently analysed. Copyright Royal Economic Socciety 2004 Date: 2004 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ect:emjrnl:v:7:y:2004:i:1:p:32-54 Ordering information: This journal article can be ordered from 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. |
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