Finite sample efficiency of OLS in linear regression models with long-memory disturbances

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DC Field	Value	Language
dc.contributor.author	Kleiber, Christian	de
dc.date.accessioned	2004-12-06T18:42:45Z	-
dc.date.available	2004-12-06T18:42:45Z	-
dc.date.issued	2000	de
dc.identifier.uri	http://hdl.handle.net/2003/5039	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-14544	-
dc.description.abstract	OLS is as efficient as GLS in the linear regression model with long-memory errors as the long-memory parameter approaches the boundary of the stationarity region, provided the model contains a constant term. This generalizes previous results of Samarov & Taqqu (Journal of Time Series Analysis 9, 1988, pp. 191-200) to the regression case and gives a further example of the `high-correlation asymptotics' of Kr?amer & Baltagi (Economics Letters 50, 1996, pp. 13-17).	en
dc.format.extent	131586 bytes	-
dc.format.extent	307585 bytes	-
dc.format.mimetype	application/pdf	-
dc.format.mimetype	application/postscript	-
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	Efficiency of OLS	en
dc.subject	linear regression	en
dc.subject	long memory	en
dc.subject.ddc	310	de
dc.title	Finite sample efficiency of OLS in linear regression models with long-memory disturbances	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 475

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