| Abstract: | This paper considers a locally optimal procedure for testing for first order moving average disturbances in the linear regression model. For this hypothesis testing problem, the Durbin-Watson test is shown to be approximately locally best invariant while the new test is most powerful invariant in a given neighbourhood of the alternative hypothesis. Two versions of the test procedure are recommended for general use; one for problems involving positively correlated disturbances and one for negatively correlated disturbances. An empirical comparison of powers shows the clear superiority of the recommended tests over the Durbin-Watson test. Selected bounds for the tests' significance points are tabulated. |
