应该是DW检验最权威

DW在有滞后因变量模型和存在二阶以上自相关时可能不太可靠，我不知道eviews为什么把dw值直接在窗口中给出，我觉得这个很误导人，因为在做含滞后因变量时很容易直接看dw，有时候做截面数据时也看dw，实际上dw检验有局限

Unlike the Durbin-Watson statistic for AR(1) errors, the LM test may be used to test for
higher order ARMA errors and is applicable whether or not there are lagged dependent variables.
Therefore, we recommend its use (in preference to the DW statistic) whenever you are
concerned with the possibility that your errors exhibit autocorrelation.
The null hypothesis of the LM test is that there is no serial correlation up to lag order ,
where is a pre-specified integer. The local alternative is ARMA( ) errors, where the
number of lag terms =max( ). Note that this alternative includes both AR( ) and
MA( ) error processes, so that the test may have power against a variety of alternative
autocorrelation structures. See Godfrey (1988), for further discussion.


这是Eviews 帮助里面的一段话。众所周知，DW检验只是在历史上比较重要，因为它只控制了自相关。
当然，可以用修正的DW-h统计量，但是Eviews是不报告这个统计量的（其他一些软件会）。而LM检验是控制了ARMA形式的自相关。Q统计量有两种，如果是小样本的，应该使用Ljung Box修正的，事实上Eviews报告的就是这个。但是LM和Q统计量都存在滞后阶数选择的问题，
如
The Q-statistic is often used as a test of whether the series is white noise. There remains the
practical problem of choosing the order of lag to use for the test. If you choose too small a
lag, the test may not detect serial correlation at high-order lags. However, if you choose too
large a lag, the test may have low power since the significant correlation at one lag may be
diluted by insignificant correlations at other lags. For further discussion, see Ljung and Box
(1979) or Harvey (1990, 1993).

选这个了Breusch-Godfrey Serial Correlation LM Test