Revisiting the Collinear Data Problem: An Assessment of Estimator 'Ill-Conditioning' in Linear Regression
DOI
https://doi.org/10.7275/c8aa-bj67
Abstract
Linear regression has gained widespread popularity in the social sciences. However, many applications of linear regression have been in situations in which the model data are collinear or ‘ill-conditioned.’ Collinearity renders regression estimates with inflated standard errors. In this paper, we present a method for precisely identifying coefficient estimates that are ill-conditioned, as well as those that are not involved, or only marginally involved in a linear dependency. Diagnostic tools are presented for a hypothetical regression model with ordinary least squares (OLS). It is hoped that practicing researchers will more readily incorporate these diagnostics into their analyses.Accessed 17,081 times on https://pareonline.net from June 18, 2008 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.
Creative Commons License
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Recommended Citation
Callaghan, Karen J. and Chen, Jie
(2019)
"Revisiting the Collinear Data Problem: An Assessment of Estimator 'Ill-Conditioning' in Linear Regression,"
Practical Assessment, Research, and Evaluation: Vol. 13, Article 5.
DOI: https://doi.org/10.7275/c8aa-bj67
Available at:
https://scholarworks.umass.edu/pare/vol13/iss1/5