COMPARATIVE ANALYSIS OF RIDGE AND PRINCIPAL COMPONENT REGRESSION IN ADDRESSING MULTICOLLINEARITY

Abstract

Multicollinearity arises when two or more regressors are correlated in multiple linear regression model (MLRM) and in most cases, one regressor variable can be predicted from another. Multicollinearity majorly results in inefficient regression model estimates and poor performance of the regression model. However, multicollinearity problem can easily be handled using various methods such as ridge regression, lasso regression, principal components regression, etc. This study compared the effectiveness of two estimators in handling multicollinearity problem in a given dataset. The estimators being compared are ridge estimator (RE) and principal components estimator (PCE). This research uses secondary data obtained from World Bank database, International Monetary Fund (IMF) database, and the Nigerian Debt Management Office to compare the two approaches of handling multicollinearity problem in MLRM. The presence of multicollinearity in the dataset was established using the correlation matrix of predictors and the Variance Inflation Factors (VIF's). Then ridge regression and principal components regression methods were used to fit models to the dataset respectively and their mean squared errors (MSE) were obtained. The MSE was used as performance evaluation measure for the regression models. Both methods addressed the problem multicollinearity in the datasets but the ridge estimator performed better than PCE by having the smallest mean squared error.