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Asymptotic efficiency of the OLSE for polynomial regression models with spatially correlated errors

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  • Shin, Dong Wan
  • Song, Seuck Heun

Abstract

For polynomial regression models with spatially correlated errors, the covariance matrix of the ordinary least-squares estimator (OLSE) is shown to have the same limiting value as that of the generalized least-squares estimator (GLSE) under the same normalization. This implies that the OLSE is asymptotically efficient.

Suggested Citation

  • Shin, Dong Wan & Song, Seuck Heun, 2000. "Asymptotic efficiency of the OLSE for polynomial regression models with spatially correlated errors," Statistics & Probability Letters, Elsevier, vol. 47(1), pages 1-10, March.
    • Handle: RePEc:eee:stapro:v:47:y:2000:i:1:p:1-10
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    References listed on IDEAS

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    1. Baltagi, Badi H., 1989. "Applications of a necessary and sufficient condition for OLS to be BLUE," Statistics & Probability Letters, Elsevier, vol. 8(5), pages 457-461, October.
    2. K. Maekawa & J. L. Knight & H. Hisamatsu, 1998. "Finite sample comparisons of the distributions of the ols and gls estimators in regression with an integrated regsorad correlated errors," Econometric Reviews, Taylor & Francis Journals, vol. 17(4), pages 387-413.
    3. Kramer, Walter & Hassler, Uwe, 1998. "Limiting efficiency of OLS vs. GLS when regressors are fractionally integrated," Economics Letters, Elsevier, vol. 60(3), pages 285-290, September.
    4. Baksalary, Jerzy K. & Trenkler, Götz, 1989. "The Efficiency of OLS in a Seemingly Unrelated Regressions Model," Econometric Theory, Cambridge University Press, vol. 5(03), pages 463-465, December.
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    Cited by:

    1. Dong Shin & Dai-Gyoung Kim & Han Kim, 2002. "Efficiency of the OLSE for regressions on two-dimensional grids with sinusoidal regressors and spatially correlated errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 56(3), pages 247-258, December.

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