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A Note on the Performance of Biased Estimators with Autocorrelated Errors

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  • Gargi Tyagi
  • Shalini Chandra

Abstract

It is a well-established fact in regression analysis that multicollinearity and autocorrelated errors have adverse effects on the properties of the least squares estimator. Huang and Yang (2015) and Chandra and Tyagi (2016) studied the PCTP estimator and the class estimator, respectively, to deal with both problems simultaneously and compared their performances with the estimators obtained as their special cases. However, to the best of our knowledge, the performance of both estimators has not been compared so far. Hence, this paper is intended to compare the performance of these two estimators under mean squared error (MSE) matrix criterion. Further, a simulation study is conducted to evaluate superiority of the class estimator over the PCTP estimator by means of percentage relative efficiency. Furthermore, two numerical examples have been given to illustrate the performance of the estimators.

Suggested Citation

  • Gargi Tyagi & Shalini Chandra, 2017. "A Note on the Performance of Biased Estimators with Autocorrelated Errors," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2017, pages 1-12, January.
    • Handle: RePEc:hin:jijmms:2045653
    DOI: 10.1155/2017/2045653
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    References listed on IDEAS

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    1. Deniz Ünal, 2010. "The effects of the proxy information on the iterative Stein-rule estimator of the disturbance variance," Statistical Papers, Springer, vol. 51(2), pages 477-484, June.
    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. Xinfeng Chang & Hu Yang, 2012. "Combining two-parameter and principal component regression estimators," Statistical Papers, Springer, vol. 53(3), pages 549-562, August.
    4. Ohtani, Kazuhiro, 1987. "Inadmissibility of the iterative Stein-rule estimator of the disturbance variance in a linear regression," Economics Letters, Elsevier, vol. 24(1), pages 51-55.
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