Further Results on Testing AR (1) Against MA (1) Disturbances in the Linear Regression Model
通过蒙特卡洛实验比较了线性回归模型中检验AR(1)扰动与MA(1)扰动的几种检验方法的小样本性质，发现非嵌套检验的真实显著性水平与名义水平差异大，而拉格朗日乘子检验的显著性水平较准确，点最优检验在适当临界值下功效更高。
This paper examines testing for AR(1) disturbances against MA(1) disturbances in the linear regression model. A Monte Carlo experiment compares the small-sample properties of the Cox test, some linearized Cox tests, and an approximate point optimal test, as well as a Lagrange multiplier test of AR (1) disturbances against ARM A (1, 1) disturbances. The main findings are that the true sizes of the asymptotic non-nested tests can differ considerably from their nominal sizes, the Lagrange multiplier test's sizes are reasonably accurate and the point optimal test is generally more powerful than the other tests when appropriate critical values are used. When sizes are controlled at an arbitrary value of the AR (1) parameter, the relative power of the Cox test is increased substantially.