A simulation study on SPSS ridge regression and ordinary least squares regression procedures for multicollinearity data

John Zhang and Mahmud Ibrahim

Journal of Applied Statistics, 2005, vol. 32, issue 6, 571-588

Abstract: This study compares the SPSS ordinary least squares (OLS) regression and ridge regression procedures in dealing with multicollinearity data. The LS regression method is one of the most frequently applied statistical procedures in application. It is well documented that the LS method is extremely unreliable in parameter estimation while the independent variables are dependent (multicollinearity problem). The Ridge Regression procedure deals with the multicollinearity problem by introducing a small bias in the parameter estimation. The application of Ridge Regression involves the selection of a bias parameter and it is not clear if it works better in applications. This study uses a Monte Carlo method to compare the results of OLS procedure with the Ridge Regression procedure in SPSS.

Keywords: Ridge regression; least squares regression; eigenvalues; eigenvectors; simulation (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (4)

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DOI: 10.1080/02664760500078946

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