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IN REGRESSION ANALYSIS most empirical economists use the well-known Durbin-Watson (DW) procedure 1 to test the hypothesis of no autocorrelation among the disturbances of a linear regression model against the hypothesis of a first-order autocorrelation. The use of this procedure is compromised by the fact that it is a bounds text and, hence, cannot discriminate between the two competing hypotheses for a range of intermediate values of the test statistic. This shortcoming can be eliminated by determining the distribution function 5 for the Durbin-Watson test statistic and enumerating it for a given level of significance and a particular regression matrix. The authors have written a FORTRAN IV program for finding the probability that the DW test statistic is less than the observed value if the null hypothesis of no autocorrelation were true. The program enables the investigator to perform the DW test for either positive or negative correlation by comparing the above probability to a specified level of significance. This procedure provides a conclusive test for first-order autocorrelation. The procedure begins with a transformation of the Durbin-Watson test statistic stated as
L’Esperance et al. (1976) studied this question.