Abstract
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.
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© 1990 Springer-Verlag New York Inc.
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Sen, A., Srivastava, M. (1990). Multicollinearity. In: Regression Analysis. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4470-7_10
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DOI: https://doi.org/10.1007/978-1-4612-4470-7_10
Publisher Name: Springer, New York, NY
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