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The Durbin-Watson statistic The Durbin-Watson statistic can be used to test for serial autocorrelation as a visual inspection of the residuals may...

The Durbin-Watson statistic


The Durbin-Watson statistic can be used to test for serial autocorrelation as a visual inspection of the residuals may only be indicative, not conclusive. Auto-correlation means that the errors are correlated across time. So, for instance, if there is first-order serial autocorrelation that means that knowing the error in time period n, would give you information about the likely error in time n+1. In 12th order, serial correlation (for instance in monthly data where there is a yearly cycle such as retail sales), means that the errors of time n and time n+12 (or n-12) are correlated. 


This model has 351 observations (n=351) and three independent variables (K=3), four if you include the intercept (alpha) term. At a 5% significance level, the lower and upper boundaries for the Durbin-Watson statistic are approximately 1.8074 and 1.8419, respectively. At a 1% significance level, the lower and upper boundaries for the Durbin-Watson statistic are approximately 1.7353 and 1.7697, respectively. 


What can be said about first-order autocorrelation given the Durbin-Watson value of 1.8941 calculated from the three-factor model residuals? (In other words, interpret the Durbin-Watson test using the critical test values given. More info can be found in Chapter 9 of your Defusco text.)


How does this relate to your answer to part c? (Again, in other words, what feature of the graph is the Durbin-Watson test testing for?)

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