Alternative Asymptotically Optimal Tests and Their Application to Dynamic Specification
提出一种生成检验统计量的方法，这些统计量与经典统计量具有相同的一阶渐近最优性。通过推广Neyman的工作，该方法利用在备择假设下一致且渐近正态的参数估计量，检验隐函数形式的约束，并应用于单方程动态回归模型中移动平均扰动下的公因子约束检验。
A method is presented for generating test statistics which share the same first order asymptotic optimality properties of the classical statistics. Generalizing Neyman's (1959) work, the linearised classical statistic tests restrictions in implicit function form using a parameter estimator which is consistent and asymptotically normally distributed under the alternative hypothesis. By judicious choice of estimator and form of restrictions at which to evaluate the statistic, a class of asymptotically optimal statistics is obtained among which are numbered some familiar classical statistics. An application is presented for testing common factor restrictions in a single equation dynamic regression model with moving average disturbances.