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

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 1998 pp, 191 – 200) to the regression case and gives a further example of the ‘high_correlation asymptotics of Krämer & Baltagi (Economics Letters 50, 1996, pp. 13 – 17).

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Year of publication:	2000
Authors:	Kleiber, Christian
Publisher:	Dortmund : Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Subject:	Regression \| Theorie \| Methode der kleinsten Quadrate \| Efficiency of OLS \| linear regression \| long memory

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Series:	Technical Report ; 2000,34
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	766143449 [GVK] hdl:10419/77175 [Handle] RePEc:zbw:sfb475:200034 [RePEc]
Classification:	C13 - Estimation ; C22 - Time-Series Models
Source:	EconStor

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010316518