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).
(This abstract was borrowed from another version of this item.)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
- Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
- Chung, Ching-Fan, 1994. "A note on calculating the autocovariances of the fractionally integrated ARMA models," Economics Letters, Elsevier, vol. 45(3), pages 293-297.
- Kramer, Walter & Baltagi, Badi, 1996. "A general condition for an optimal limiting efficiency of OLS in the general linear regression model," Economics Letters, Elsevier, vol. 50(1), pages 13-17, January.
When requesting a correction, please mention this item's handle: RePEc:eee:ecolet:v:72:y:2001:i:2:p:131-136. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If references are entirely missing, you can add them using this form.