Serial correlation durbin watson

To determine if a Durbin-Watson test statistic is significantly significant at a certain alpha level, you can refer to this table of critical values. If the absolute value of the Durbin-Watson test statistic is greater than the value found in the table, then you can reject the null hypothesis of the test and conclude that autocorrelation is present. If you reject the null hypothesis of the Durbin-Watson test and conclude that autocorrelation is present in the residuals, then you have a few different options to correct this problem if you deem it to be serious enough:.

These strategies are typically sufficient to remove the problem of autocorrelation. For step-by-step examples of Durbin-Watson tests, refer to these tutorials that explain how to perform the test using different statistical software:. Your email address will not be published. Skip to content Menu. Posted on January 21, April 2, by Zach.

For negative serial correlation, check to make sure that none of your variables are overdifferenced. For seasonal correlation, consider adding seasonal dummy variables to the model. The formula for the Durbin Watson statistic is rather complex but involves the residuals from an ordinary least squares OLS regression on a set of data.

The following example illustrates how to calculate this statistic. Assume the following x,y data points:. Using the methods of a least squares regression to find the " line of best fit ," the equation for the best fit line of this data is:. This first step in calculating the Durbin Watson statistic is to calculate the expected "y" values using the line of best fit equation.

For this data set, the expected "y" values are:. Next, the differences of the actual "y" values versus the expected "y" values, the errors, are calculated:. Next these errors must be squared and summed :. Next, the value of the error minus the previous error are calculated and squared:.

Finally, the Durbin Watson statistic is the quotient of the squared values:. Note: Tenths place may be off due to rounding errors in the squaring. Fundamental Analysis. Fixed Income Essentials.

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List of Partners vendors. Your Money. Personal Finance. Your Practice. Popular Courses. Financial Analysis How to Value a Company. What Is the Durbin Watson Statistic? Key Takeaways The Durbin Watson statistic is a test for autocorrelation in a regression model's output. The DW statistic ranges from zero to four, with a value of 2. Values below 2. Autocorrelation can be useful in technical analysis, which is most concerned with the trends of security prices using charting techniques in lieu of a company's financial health or management.

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