A Note on Collinearity Diagnostics and Centering

ABSTRACT

The usual approach for diagnosing collinearity proceeds by centering and standardizing the regressors. The sample correlation matrix of the predictors is then the basic tool for describing approximate linear combinations that may distort the conclusions of a standard least-square analysis. However, as indicated by several authors, centering may eventually fail to detect the sources of ill-conditioning. In spite of this earlier claim, there does not seem to be in the literature a fully clear explanation of the reasons for this bad potential behavior of the traditional strategy for analyzing collinearity. This note studies this issue in some detail. Results derived are motivated by the analysis of a well-known real dataset. Practical conclusions are illustrated with several examples.

KEYWORDS:

• Condition numbers
• Multiple linear regression
• Normalized linear combinations
• Variance inflation factors.

Acknowledgments

The author is thankful to the editor, and to an anonymous associate editor, and two referees for their careful review of a previous version of this work.

Additional information

Funding

This research is partially supported by Grant ECO2015 - 66593 - P (Spain).