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