An Approximation to the Null Distribution of the Durbin-Watson Statistic in Models Containing Lagged Dependent Variables
针对含滞后因变量的线性回归模型，用小扰动渐近法近似德宾-沃森统计量的零分布，得到临界值，并通过蒙特卡洛模拟比较了德宾-沃森检验与德宾h、t检验的功效，发现前者更优。
We consider testing for autoregressive disturbances in the linear regression model with a lagged dependent variable. An approximation to the null distribution of the Durbin—Watson statistic is developed using small-disturbance asymptotics, and is used to obtain test critical values. We also obtain nonsimilar critical values for the Durbin—Watson and Durbin's h and t tests. Monte Carlo results are reported comparing the performances of the tests under the null and alternative hypotheses. The Durbin–Watson test is found to be more powerful and to perform more consistently than either of Durbin's tests under H o .