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Autocorrelation of the error function is something that needs to be addressed in linear regression. If strong autocorrelation exists, then autoregressive models will be more appropriate as opposed to linear regression for the independence assumption of linear regression is severely violated. If no such strong autocorrelation exist, for beginnners, we have no additional reasons to reject linear regression model as a suitable model.

To test for serial autocorrelation in linear regression, one should use the DW option in PROC REG.


It should be noted that the Durbin-Watson statistics are not relevant in certain scenarios where normality assumptions are violated or when variables which are extremely time-dependent (lag variables) are used. In these cases, the more relevant serial correlation test which is the Breusch-Godfrey test will be more relevant.

Submitted by Murphy Choy. Contact me at my Discussion Page.

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