Open AccessA Comparison of OLS and Ridge Regression Methods in the Presence of Multicollinearity Problem in the Data
Author(s) -
N S M Shariff,
Hanan Duzan
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.30.21999
Subject(s) - multicollinearity , variance inflation factor , regression analysis , ordinary least squares , statistics , regression diagnostic , linear regression , regression , mathematics , robust regression , estimator , ridge , econometrics , variables , principal component regression , polynomial regression , geography , cartography
The presence of multicollinearity will significantly lead to inconsistent parameter estimates in regression modeling. The common procedure in regression analysis that is Ordinarily Least Squares (OLS) is not robust to multicollinearity problem and will result in inaccurate model. To solve this problem, a number of methods are developed in the literatures and the most common method is ridge regression. Although there are many studies propose variety method to overcome multicolinearity problem in regression analysis, this study proposes the simplest model of ridge regression which is based on linear combinations of the coefficient of the least squares regression of independent variables to determine the value of k (ridge estimator in ridge regression model). The performance of the proposed method is investigated and compared to OLS and some recent existing methods. Thus, simulation studies based on Monte Carlo simulation study are considered. The result of this study is able to produce similar findings as in existing method and outperform OLS in the existence of multicollinearity in the regression modeling.