[The preliminary test estimator (or predictor) is studied in the context of linear normal regression models. The estimator (or predictor) obtained after the preliminary test is known to be inadmissible with respect to squared error loss under certain assumptions. Nevertheless, it is still widely used in practical application of regression analysis, particularly in econometrics. The object of the present paper is to tabulate the optimal significance points in the preliminary test for practical use. The optimality is based on the minimax regret principle. It is shown that if, as is usual, we take the significance point equal to the 5 per cent point or 10 per cent point, the risk is extremely large for some parameter value. The optimal significance point of the preliminary t test decreases slightly as the degrees of freedom increase,but it is nearly constant; i.e., it lies between 1.370 to 1.380 if d.f. is greater than 6.]

[The limited information maximum likelihood and two-stage least squares estimates have the same asymptotic normal distribution; the ordinary least squares estimate has another asymptotic normal distribution. This paper considers more accurate approximations to the distributions of the so-called "k-class" estimates. An asymptotic expansion of the distribution of such an estimate is given in terms of an Edgeworth or Gram-Charlier series (of which the leading term is the normal distribution). The development also permits expression of the exact distribution in several forms. The distributions of the two-stage least squares and ordinary least squares estimates are transformed to doubly-noncentral F distributions. Numerical comparisons are made between the approximate distributions and exact distributions calculated by the second author.]