A measure that tests for autocorrelation between error terms at time *t *and those at *t *+ 1. Values of this statistic range from 0 to 4. If no autocorrelation is present, the expected value is 2. Small values (less than 2, approaching 0) indicate positive autocorrelation; larger values (greater than 2, approaching 4) indicate negative autocorrelation. Is autocorrelation important to forecasting? It can tell you when to be suspicious of tests of statistical significance, and this is important when dealing with small samples. However, it is difficult to find empirical evidence showing that knowledge of the Durbin-Watson statistic leads to accurate forecasts or to well- calibrated prediction intervals. Forecasters are fond of reporting the D-W statistic, perhaps because it is provided by the software package. Do not use it for cross-sectional data as they have no natural order.