Until this point, the only difficulties with least squares estimation that we have considered have been associated with violations of Gauss-Markov conditions. These conditions only assure us that least squares estimates will be ‘best’ for a given set of independent variables; i.e., for a given X matrix. Unfortunately, the quality of estimates, as measured by their variances, can be seriously and adversely affected if the independent variables are closely related to each other. This situation, which (with a slight abuse of language) is called multicollinearity, is the subject of this chapter and is also the underlying factor that motivates the methods treated in Chapters 11 and 12.
KeywordsCondition Number Variance Inflation Factor Small Eigenvalue Variance Proportion Gross National Product
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