A monte carlo comparison of tests based on the durbin-watson statistic with other autocorrelation tests in dynamic models
通过蒙特卡洛模拟比较了动态线性模型中多种序列相关检验的抽样性质，发现m检验、Hausman检验和Inder检验表现最佳，而基于DW的检验在动态性更强的模型中功效较低且更敏感。
This paper examines the sampling properties of a number of serial correlation tests in dynamic linear models which include one or two lags of the dependent variable. Among the tests considered are the Durbin-Watson (DW) bounds test, modified versions of the DW proposed recently by King and Wu and Inder, Durbin's m test, Inder's point optimal test and a Hausman type test. Sampling designs include models with one or two lags of the dependent variable. The m, Hausman, and Inder's tests have the best performance, while Inder's modified DW test appears to be better than the other DW based tests. Results also suggest that tests are less powerful and more sensitive to design parameters in models with higher dynamics, with the DW-based tests being the most sensitive.