The application of the durbin-watson test to the dynamic regression model under normal and non-normal errors
通过蒙特卡洛实验，验证了将滞后因变量视为非随机回归变量时，杜宾-沃森检验的临界值在动态回归模型中具有合理准确性，并考察了对非正态误差的稳健性。
Until recently, a difficulty with applying the Durbin-Watson (DW) test to the dynamic linear regression model has been the lack of appropriate critical values. Inder (1986) used a modified small-disturbance distribution (SDD) to find approximate criticl values. Unfortunately, these means are unknown although they could be estimated by the actual variable values. This provides a justification for using the exact critical values of the DW statistic from the regression with the lagged. dependent variables treated as non-stochastic regressors. Extensive Monte Carlo experiments are reported in this paper. They show that this approach leads to reasonably accurate critical values, particularly when two lags of the dependent variable are present. Robustness to non-normality is also investigated.