International Journal of Advances in Scientific Research and Engineering-IJASRE

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

Article Category: Applied Mathematics

DOI: 10.31695/IJASRE.2019.33217

Pages: 116-121

Author: Nusirat Funmilayo Gatta,Banjoko Alabi Waheed

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.

Keyword: Ordinary least squares, Multicollinearity, Mean Square Error, Absolute Bias, Mean Square Error of Prediction

This work is licensed under a Creative Commons Attribution 4.0 (International) Licence. (CC BY-NC 4.0)
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