Prediction, Explanation, Multicollinearity, and Validity Concentration

• In Event: 52.071 - General Linear Model Multiple Linear Regression: Modern Issues and Techniques

Abstract

Recommendations from popular statistics texts regarding avoidance of predictor variable multicollinearity in the use of OLS multiple regression are considered from the perspective of the alternate purposes of explanation and prediction. In the case of relative or absolute prediction accuracy, it is shown that multicollinearity has no effect on prediction. Moreover, from the perspective of prediction accuracy, not only does multicollinearity not disadvantage OLS, but indeed, it is, in most data conditions presented, advantageous to model prediction accuracy, as it allows validity concentration to become large enough for alternative non-OLS methods to exceed OLS.

Authors

• John D. Morris, Florida Atlantic University

• Mary G. Lieberman, Florida Atlantic University