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A general class of biased estimators in the presence of multicollinearity with autocorrelated errors

Shalini Chandra, Gargi Tyagi

Abstract



It is a well known fact in regression analysis that multicollinearity and autocorrelated errors have adverse effects on least squares estimator. In this paper, the r 􀀀 (k; d) class estimator (O¨ zkale, 2012) which was developed to combat the problem of multicollinearity has been generalized to address autocorrelated errors simultaneously. Necessary and sufficient conditions for superiority of the proposed estimator over other competing estimators have been derived under mean squared error (MSE) matrix criterion. A simulation study has been done to evaluate the performance of the estimators.

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