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

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  • John Zhang
  • Mahmud Ibrahim

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.

Suggested Citation

  • John Zhang & Mahmud Ibrahim, 2005. "A simulation study on SPSS ridge regression and ordinary least squares regression procedures for multicollinearity data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(6), pages 571-588.
    • Handle: RePEc:taf:japsta:v:32:y:2005:i:6:p:571-588
    DOI: 10.1080/02664760500078946
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    Citations

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    Cited by:

    1. José García & Román Salmerón & Catalina García & María del Mar López Martín, 2016. "Standardization of Variables and Collinearity Diagnostic in Ridge Regression," International Statistical Review, International Statistical Institute, vol. 84(2), pages 245-266, August.
    2. Salmerón Gómez, Román & Rodríguez Martínez, Eduardo, 2017. "Métodos cuantitativos para un modelo de regresión lineal con multicolinealidad. Aplicación a rendimientos de letras del tesoro || Quantitative Methods for a Linear Regression Model with Multicollinear," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 24(1), pages 169-189, Diciembre.
    3. C. A. Uzuke & J. I. Mbegbu, 2016. "Graphical Investigation of Ridge Estimators When the Eigenvalues of the Matrix (X'X) are Skewed," International Journal of Sciences, Office ijSciences, vol. 5(03), pages 78-100, March.
    4. Zhi-Sheng Ye & Jian-Guo Li & Mengru Zhang, 2014. "Application of ridge regression and factor analysis in design and production of alloy wheels," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(7), pages 1436-1452, July.

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