Abstract
As a starting point, which was also the basis of the standard regression procedures described in the previous chapters, we take T i.i.d. samples of the variables y and X 1,..., X K . If the classical linear regression model y = Xβ+∊ with its assumptions may be assumed to be a realistic picture of the underlying relationship, then the least-squares estimator b = (X′X)−1 X′y is optimal in the sense that it has smallest variability in the class of linear unbiased estimators for β.
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© 1995 Springer Science+Business Media New York
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Rao, C.R., Toutenburg, H. (1995). Exact and Stochastic Linear Restrictions. In: Linear Models. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-0024-1_5
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DOI: https://doi.org/10.1007/978-1-4899-0024-1_5
Publisher Name: Springer, New York, NY
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