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

  • Kleiber, Christian

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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File URL: http://www.sciencedirect.com/science/article/B6V84-4379GRM-1/2/f738b243697c4669d92d12286bf8aa4f
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Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 72 (2001)
Issue (Month): 2 (August)
Pages: 131-136

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Handle: RePEc:eee:ecolet:v:72:y:2001:i:2:p:131-136
Contact details of provider: Web page: http://www.elsevier.com/locate/ecolet

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  1. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
  2. 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.
  3. 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.
  4. 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.
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