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r − k Class estimator in the linear regression model with correlated errors

Author

Listed:
  • Gülesen Üstündagˇ Şiray
  • Selahattin Kaçıranlar
  • Sadullah Sakallıoğlu

Abstract

Autocorrelation in errors and multicollinearity among the regressors are serious problems in regression analysis. The aim of this paper is to examine multicollinearity and autocorrelation problems concurrently and to compare the r − k class estimator to the generalized least squares estimator, the principal components regression estimator and the ridge regression estimator by the scalar and matrix mean square error criteria in the linear regression model with correlated errors. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Gülesen Üstündagˇ Şiray & Selahattin Kaçıranlar & Sadullah Sakallıoğlu, 2014. "r − k Class estimator in the linear regression model with correlated errors," Statistical Papers, Springer, vol. 55(2), pages 393-407, May.
    • Handle: RePEc:spr:stpapr:v:55:y:2014:i:2:p:393-407
    DOI: 10.1007/s00362-012-0484-8
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    References listed on IDEAS

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    1. Trenkler, G., 1984. "On the performance of biased estimators in the linear regression model with correlated or heteroscedastic errors," Journal of Econometrics, Elsevier, vol. 25(1-2), pages 179-190.
    2. Sarkar, Nityananda, 1996. "Mean square error matrix comparison of some estimators in linear regressions with multicollinearity," Statistics & Probability Letters, Elsevier, vol. 30(2), pages 133-138, October.
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