Ridge regression estimators with the problem of multicollinearity

Abstract

Abstract The study aims to illustrate the negative effect of the Multicollinearity problem upon the specimen, identify the way of Ridge Regression as a way to deal with the Multicollinearity problem, focus on some of the estimators of Ridge regression as (James and Stein, Bhattacharya, Heuristic) and identify which estimator from the previously mentioned estimators is highly preferable to be used, to estimate the parameters of a model which faces the Multicollinearity problem. Minimum mean-square error (MSE) has been used as the best measure for estimator. Application has been done on specific data for return on total assets of a bank after making sure that this data faces the Multicollinearity problem. Also, simulation method was used to generate fabricated data sets, which gave more space in the application. According to the study we can see that James and Stein’s estimator has got the minimum mean square error (MSE). Consequently the study recommends its usage to estimate model parameters which face the Multicollinearity problem