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Statistical Papers

, Volume 32, Issue 1, pp 71–73 | Cite as

The asymptotic unbiasedness of S2 in the linear regression model with AR(1)-disturbances

  • W. Krämer
Notes

Abstract

The OLS-estimator of the disturbance variance in the Linear Regression Model is shown to be asymptotically unbiased in the context of AR(1)-disturbances, although for any given design, E(s22) tends to zero as correlation increases.

Keywords

Linear Regression Model Relative Bias Full Column Rank Great Root Positive Autocorrelation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. DUFOUR, J.M. (1986):“Bias of s 2 in Linear Regression with Dependent Errors”, The American Statistician 40, 284–285.CrossRefMathSciNetGoogle Scholar
  2. KIVIET, J and KRÄMER, W. (1990): “Bias of S 2 in the Linear Regression Model with Autocorrelated Errors”, Paper given at the 6th World Congress of the Econometric Society, Barcelona.Google Scholar
  3. NEUDECKER, H. (1977): “Bounds for the Bias of the Least Squares Estimator ofσ2 in Case of a First-Order Autoregressive Process (Positive Autocorrelation)”, Econometrica 45, 1258–1262.MathSciNetGoogle Scholar
  4. NEUDECKER, H. (1978): “Bounds for the Bias of the LS Estimator in the Case of a First-Order (positive) Autoregressive Process where the Regression contains a Constant Term”, Econometrica 46, 1223–1226.zbMATHCrossRefGoogle Scholar
  5. SATHE, S.T. and VINOD, H.D. (1974): “Bounds on the Variance of Regression Coefficients due to Heteroscedastic or Autoregressive Errors”, Econometrica 42, 333–340.zbMATHCrossRefGoogle Scholar
  6. WATSON, G.S. (1955): “Serial Correlation in Regression Analysis I”, Biometrika 42, 327–341.zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag 1991

Authors and Affiliations

  • W. Krämer
    • 1
  1. 1.Department of StatisticsUniversity of DortmundDortmund 50

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