One of the assumptions for ordinary least squares regression is that the error terms are independent. However, with time series data, much of the time, the error terms are correlated. This is also known as autocorrelation. The regression tests performed up until now don't test for autocorrelation. If autocorrelation is present in the model, then the parameter estimates may not be accurate, and the standard error estimates will be biased.
While the AUTOREG procedure should ideally be used for regressing time series data, we can still try to evaluate the model for autocorrelation by using PROC REG, which was used earlier. The statistic that is going to help us explore the autocorrelation is the Durbin-Watson (DW) statistic. ...
