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On a principal component two-parameter estimator in linear model with autocorrelated errors

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  • Jiewu Huang
  • Hu Yang

Abstract

This paper is concerned with autocorrelation in errors and multicollinearity among the regressors in linear regression model. To reduce these effects of autocorrelation and multicollinearity, we generalize a principal component two-parameter (PCTP) estimator in the linear regression model with correlated or heteroscedastic errors. Then we give detailed comparisons between those estimators that can be derived from the PCTP estimator such as the generalized least squares estimator, the principal components regression estimator, the $$r-k$$ r - k estimator and the $$r-d$$ r - d estimator by the mean squared error (MSE) matrix criterion. Also, we obtain the conditions for the superiority of one estimator over the other. Furthermore, we conduct a Monte Carlo simulation study to compare these estimators under the MSE criterion. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Jiewu Huang & Hu Yang, 2015. "On a principal component two-parameter estimator in linear model with autocorrelated errors," Statistical Papers, Springer, vol. 56(1), pages 217-230, February.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:1:p:217-230
    DOI: 10.1007/s00362-013-0576-0
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    References listed on IDEAS

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    1. Xinfeng Chang & Hu Yang, 2012. "Combining two-parameter and principal component regression estimators," Statistical Papers, Springer, vol. 53(3), pages 549-562, August.
    2. 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.
    3. Özkale, M. Revan, 2009. "A stochastic restricted ridge regression estimator," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1706-1716, September.
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