Multicollinearity in Regression Analysis: The Problem Revisited

To most economists the single equation least squares regression model, like an old friend, is tried and true.Its properties and limitations have been extensively studied, documented and are, for the most part, well known.Any good text in econometrics can lay out the assumptions on which the model is based and provide a reasonably coherent --perhaps even a lucid -- discussion of problems that arise as particular assumptions are violated.A short bibliography of definitive papers on such classical problems as non -normality, heteroscedasticity, serial correlation, feedback, etc., completes the job.As with most old friends, however, the longer one knows least squares, the more one learns about it.An admiration for its robustness under departures from many assumptions is sure to grow.The admiration must be tempered, however, by an apprecia- tion of the model's sensitivity to certain other conditions.The requirement that independent variables be truly independent of one another is one of these.Proper treatment of the model's classical problems ordinarily involves two separate stages, detection and correction.The Durbin -Watson test for serial correlation, combined with Cochrane and Orcutt's suggested first differencing procedure, is an obvious example.*