Abstract
IN THEIR PAPER [1] with the above title, Blattberg and Sargent have suggested a method for estimating regression parameters when disturbances are generated by a symmetric stable law. This procedure was originally suggested by John Wise [4]. Wise arrived at this estimator by confining attention to the class of linear unbiased estimators and minimizing the scale parameter. He also suggested that his procedure might be considered as a generalization of the classical least squares procedure which fails when the second moments of the disturbances do not exist. Blattberg and Sargent have compared various estimators by using simulated data. The main purpose of this note is to show that the procedure suggested by Wise and Blattberg and Sargent has an optimal property, namely that it yields an estimator that has minimum mean absolute error among the class of all linear unbiased estimators. The model is the well-known linear regression model given by
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