A Point Optimal Test for Moving Average Regression Disturbances
改进了King针对线性回归模型中一阶移动平均扰动的局部最优检验方法，推荐了分别针对正相关和负相关扰动的两个检验，并通过模拟比较证明了新检验在正相关情况下的整体优越性。
This paper reconsiders King's [12] locally optimal test procedure for first-order moving average disturbances in the linear regression model. It recommends two tests, one for problems involving positively correlated disturbances and one for negatively correlated disturbances. Both tests are most powerful invariant at a point in the alternative hypothesis parameter space that is determined by a function involving the sample size and the number of regressors. Selected bounds for the tests' significance points are tabulated and an empirical comparison of powers demonstrates the overall superiority of the new test for positively correlated moving average disturbances.