The DurbinWatson statistic tests for autocorrelation in the residuals from "summary" of Introduction to Econometrics by Christopher Dougherty
The Durbin-Watson statistic is a test that helps us determine whether there is autocorrelation present in the residuals of a regression model. Autocorrelation occurs when the error terms in a regression model are correlated with each other. If there is autocorrelation in the residuals, it means that the error terms are not independent of each other, which violates one of the assumptions of classical linear regression analysis. This can lead to biased and inefficient estimates of the regression coefficients, as well as invalid hypothesis tests. The Durbin-Watson statistic is a measure of the correlation between adjacent residuals in a regression model. It takes on values between 0 and 4, with a value of 2 indicating no autocorrelation. Values of the statistic that are close to 0 indicate positive autocorrelation, while values that are close to 4 indicate negative autocorrelation. To test for autocorrelation using the Durbin-Watson statistic, we compare the computed value of the statistic to critical values from a table. If the computed value falls outside the specified range of critical values, we reject the null hypothesis of no autocorrelation and conclude that autocorrelation is present in the residuals. In practice, researchers often use statistical software to calculate the Durbin-Watson statistic and test for autocorrelation. If autocorrelation is detected, various remedial measures can be taken, such as transforming the data or using a different estimation technique that accounts for autocorrelation.
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