The Durbin-Watson test checks if there is autocorrelation among the residuals of a linear regression. It is available in Excel using the XLSTAT software.
Durbin Watson test definition
Developed by J.Durbin and G.Watson (1950,1951), the Durbin-Watson test is used to detect the autocorrelation in the residuals from a linear regression.
In practice, the errors are often autocorrelated, it leads to undesirable consequences such as sub-optimal least-squares estimates.
Assume that the error terms (epsilon) are stationary and normally distributed with mean 0. The null and alternative hypotheses of the Durbin-Watson test are:
H0: The errors are uncorrelated
H1: The errors are AR(1)
And the test statistic D is:
In the context of the Durbin-Watson test, the main problem is the evaluation of the p-values. In XLSTAT the Imhof procedure (1961) is used to solve this problem.
Results of the Durbin-Watson test in XLSTAT
In XLSTAT, the results of the Durbin-Watson test are the following:
The tables of descriptive statistics show the simple statistics for the residuals. The number of observations, missing values, the number of non-missing values, the mean and the standard deviation (unbiased) are displayed.
The value of the statistic D and the p-value of the Durbin-Watson test. A short interpretation is also displayed.