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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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Suggested Citation

  • Kleiber, Christian, 2001. "Finite sample efficiency of OLS in linear regression models with long-memory disturbances," Economics Letters, Elsevier, vol. 72(2), pages 131-136, August.
  • Handle: RePEc:eee:ecolet:v:72:y:2001:i:2:p:131-136

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    References listed on IDEAS

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

    1. Ko, Kyungduk & Lee, Jaechoul & Lund, Robert, 2008. "Confidence intervals for long memory regressions," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1894-1902, September.
    2. Krämer Walter, 2002. "Statistische Besonderheiten von Finanzzeitreihen / Statistical Properties of Financial Time Series," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 222(2), pages 210-229, April.
    3. Kleiber, Christian & Krämer, Walter, 2004. "Finite sample of the Durbin-Watson test against fractionally integrated disturbances," Technical Reports 2004,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Martellosio, Federico, 2011. "Efficiency of the OLS estimator in the vicinity of a spatial unit root," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1285-1291, August.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes


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