What if Durbin-Watson test is inconclusive?
If DW is in between the lower and upper critical values: The test is inconclusive. If 4-DW > Upper critical value: There is no statistical evidence that the data is negatively correlated. If 4-DW is in between the lower and upper critical values: The test is inconclusive.
Can a hypothesis be inconclusive?
If the probability value is lower then you reject the null hypothesis. However, if your probability value is higher than the conventional α level of 0.05, most scientists will consider your findings inconclusive. Failure to reject the null hypothesis does not constitute support for the null hypothesis.
What does it mean when a study is inconclusive?
If something’s inconclusive, that means it doesn’t lead to a conclusion or a resolution. Inconclusive often describes scientific results. If your data about a flu outbreak is inconclusive, then your results don’t prove anything.
Why is the Durbin-Watson test not inconclusive?
As pointed out before in this and other threads: (1) The Durbin-Watson test is not inconclusive. Only the boundaries suggested initially by Durbin and Watson were because the precise distribution depends on the observed regressor matrix. However, this is easy enough to address in statistical/econometric software by now.
What are the lower bounds of Durbin and Watson?
Durbin & Watson calculated lower bounds for the test statistic under which the test for positive autocorrelation must reject, at given significance levels, for any design matrix, & upper bounds over which the test must fail to reject for any design matrix.
Can a statistic lie in an inconclusive region?
The Durbin-Watson test statistic can lie in an inconclusive region, where it is not possible either to reject or fail to reject the null hypothesis (in this case, of zero autocorrelation). What other statistical tests can produce “inconclusive” results?
Can you use Durbin Watson to test residual autocorrelation?
One important drawback of the Durbin-Watson test is that it must not be applied to models that already contain autoregressive effects. Thus, you cannot test for remaining residual autocorrelation after partially capturing it in an autoregressive model.