Regresion analysis with multicollinear predictor variables: definition, derection, and effects

Abstract

For over fifty years researchers have encountered difficulties with least squares estimators when predictor variables in a regression analysis are multicollinear. Extensive research efforts over the last ten to fifteen years have resulted in a clear understanding of many aspects of this problem and have, generated a great deal of controversy over possible solutiors. In this survey the nature and effects of predictor-variable multicollinearities are examined. Emphasis is placed on discussions of the multicollinearity problem itself rather than on classical or Bayesian solutions to the problem.

Keywords:

• Bayesican modeling
• biased estimation
• ill-conditioning
• outliers
• robust estimation