Abstract:

To combat autocorrelation in errors and multicollinearity among the regressors in linear regression model, we proposed a new estimator by combining the principal components regression (PCR) estimator and the s-K estimator. Then necessary and sufficient conditions for the superiority of the new estimator over the GLS, the PCR, the r-k and the s-K estimators were derived by the mean squared error matrix criterion. Finally, a Monte
Carlo simulation study was carried out to investigate the performance of the proposed estimator.

Key words: autocorrelation, multicollinearity, principal components regression estimator, s-K estimator, mean squared error matrix