More Efficient Estimation Strategy for (k-d) Class Estimator in Existence of Multicollinearity and Heteroscedasticity: Some Monte Carlo Simulation Evidence

Abstract

The typical linear regression model does this to have some sort of heteroscedasticity in the error terms and linear correlation in the regressors. The ordinary least squares estimates are significantly impacted by each of these issues. When these assumptions violated in any multiple linear regression model then ordinary least square estimator happen to unstable and no longer remain best linear unbiased estimator. Therefore, in attempt to tackle the issue of Multicollinearity the rigid, Liu and (k-d) regression exist and easily accessible in literature. The adaptive estimator was recommended to obtain an efficient estimator in comparison to the conventional least square estimator to address the problem of heteroscedasticity. This current work suggests the improved method of adaptation for (kd) class estimator to get more efficient results when dealing with multicollinearity and heteroscedasticity occur at same time. All the numerical work is done by using simulation scheme Monte Carlo, with different degrees of collinearity, severity (existence) of heteroscedasticity, and sample size to assess the performance of the suggested estimator. The simulation results provide best performance of adaptive (k-d) class estimator which is our proposed estimator.