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Explores the statistical properties of a class of estimators, known as either ridge analysis or ridge regression, proposed as an alternative to ordinary least squares (OLS) regression in analyzing sample data that are collinear. Using Monte Carlo techniques, various ridge estimation procedures were evaluated. All the ridge estimators did worse than OLS for at least some choices of the true regression coefficient in the models considered. Further, the failures were numerous. It thus appears that the ridge estimators proposed to date are not a viable alternative to OLS. However, the results show that it might be possible to define a ridge estimator that would be better than OLS. Until the properties of such an estimator are rigorously derived, the authors caution against using ridge analysis to estimate regression coefficients.

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