Investigating the Effects of Multicollinearity on the Model Parameters of Ordinary Least Squares Estimator

Abstract

This study investigated the effects of multicollinearity on the model parameters of the ordinary least squares regression model. The aim was to examine the impacts of multicollinearity on the efficiency of classical Ordinary least squares (OLS). Data were
simulated from a multivariate normal distribution with mean zero and variance-covariance matrix at various sample sizes 25, 50,
100, 200, 500 and 1000. To assess the asymptotic efficiency and consistency of the regression models in the presence of multicollinearity, the evaluation criteria used were the Variance, Absolute bias, Mean Square Error (MSE) and Mean Square Error of Prediction (MSEP). Results from the analysis revealed that the OLS is not efficient given the large MSE, MSEP, and Absolute bias.