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Abstract
A number of procedures have been developed for finding biased estimators of regression parameters. One of these procedures is the ridge regression. In this article, a new approach to obtain the ridge parameter K is suggested and then evaluated by Monte Carlo simulations. A number of different models are investigated for different number of observations, the strength of correlation between the explanatory variables, and distribution of the error terms. The mean squared error (MSE) criterion is used to examine the performance of the proposed estimators when compared with other well-known estimators. Under certain conditions, it is shown that at least one of the proposed estimators have a smaller MSE than the ordinary least squared estimator (OLS) and Hoerl and Kennard (Citation1970a) estimator (HK).
Mathematics Subject Classification:
Acknowledgment
The authors wish to thank the anonymous referee for his/her comments which improved the original version.