A class of generalized ridge estimators. Issue 7 (9th August 2017) - Bhat, Satish; Raju, Vidya
ABSTRACT: Presence of collinearity among the explanatory variables results in larger standard errors of parameters estimated. When multicollinearity is present among the explanatory variables, the ordinary least-square (OLS) estimators tend to be unstable due to larger variance of the estimators of the regression coefficients. As alternatives to OLS estimators few ridge estimators are available in the literature. This article presents some of the popular ridge estimators and attempts to provide (i) a generalized class of ridge estimators and (ii) a modified ridge estimator. The performance of the proposed estimators is investigated with the help of Monte Carlo simulation technique. Simulation results indicate that the suggested estimators perform better than the ordinary least-square (OLS) estimators and other estimators considered in this article.