Exact and Stochastic Linear Restrictions
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.
KeywordsUnbiased Estimator Auxiliary Information Linear Restriction Dispersion Matrix Mixed Estimator
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