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

Christian Kleiber

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 : SFB 475, Universität Dortmund
Subject:	Efficiency of OLS \| linear regression \| long memory \| Regression (STW) \| Theorie (STW) \| Methode der kleinsten Quadrate (STW) \| Regressionsanalyse \| Regression analysis \| Theorie \| Theory \| Kleinste-Quadrate-Methode \| Least squares method

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Erscheint auch als (Druck-Ausgabe):

Kleiber, Christian *1966-*. Finite sample efficiency of OLS in linear regression models with long-memory disturbances. - Dortmund : Univ., SFB 475, 2000. - 8 S

Persistent link: https://www.econbiz.de/10009783566