Investigating the Effects of Multicollinearity on the Model Parameters of Ordinary Least Squares Estimator
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Date
2019
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Sretech Journal Publications
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.
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Keywords
Ordinary least squares, Multicollinearity, Mean Square Error, Absolute Bias, Mean Square Error of Prediction