where ( hoDurbinWatson test is constructed as:
J. Durbin & G.S. Watson (1951),Testing for Serial Correlation in Least Squares Regression II.Biometrika 38, 159–178.
DurbinWatson Statistic = 1.65025
Independence of Residuals  DurbinWatson Statistic. The next assumption is that the residuals are not correlated serially from one observation to the next.
Under the assumption of normally distributed disturbances, the null distribution of the DurbinWatson statistic is the distribution of a linearcombination of chisquared variables. The pvalue is computed using aFortran version of the Applied Statistics Algorithm AS 153 by Farebrother(1980, 1984). This algorithm is called "pan" or "gradsol". For large samplesizes the algorithm might fail to compute the p value; in that case a warningis printed and an approximate p value will be given; this p value is computedusing a normal approximation with mean and variance of the DurbinWatson teststatistic.
using the Durbin–Watson statistic and the estimated variance
R.W. Farebrother (1980),Pan's Procedure for the Tail Probabilities of theDurbinWatson Statistic (Corr: 81V30 p189; AS R52: 84V33 p363 366; ASR53: 84V33 p366 369).Applied Statistics 29, 224–227.
Autocorrelation, also known as serial correlation or crossautocorrelation, is the crosscorrelation of a signal with itself at different points in time that is what.
Brief description: Calculate durbin watson critical values
1 Durbin Watson Test The DurbinWatson statistic is a simple numerical method for checking serial dependence. Let rk be the residuals sorted into time order.
Econometrica, vol. 34, no. 1 january, 1966 use of the durbinwatson statistic in inappropriate situations by marc nerlove and kenneth f. Wallis.
DurbinWatson Table  Real Statistics Using Excel

DurbinWatson Table for values of alpha = .01 and .05
J. Durbin & G.S. Watson (1971),Testing for Serial Correlation in Least Squares Regression III.Biometrika 58, 1–19.

DurbinWatson Statistic (Test)  Financial Exam Help 123
Description of the use of the DurbinWatson statistic for testing autocorrelation in regression models.

Durbin Watson test  AnalystForum
Recall that if we have a firstorder autocorrelation with the errors, then the errors are modeled as:
Hypothesis testing for Durbin Watson test  AnalystForum
The DurbinWatson test has the null hypothesis that the autocorrelationof the disturbances is 0. It is possible to test against the alternative that it isgreater than, not equal to, or less than 0, respectively. This can be specifiedby the argument.
Wikipedia  DurbinWatson statistic
DurbinWatson statistic is related autocorrelation. After we run a regression, the error term should have no correlation, with a mean zero. DurbinWatson statistic is defined as:
R: DurbinWatson Test  Mathematics  Furman University
Here, is the error term at time , is the total number of error term. The DurbinWatson statistic tests the null hypothesis that the residuals from an ordinary leastsquares regression are not autocorrelated against the alternative that the residuals follow an AR1 process. The DurbinWatson statistic ranges in value from 0 to 4. A value near 2 indicates nonautocorrelation; a value toward 0 indicates positive autocorrelation; a value toward 4 indicates ...
DurbinWatson Test Statistic & Pvalues?  Yahoo …
To illustrate how the test works for k=1, consider the Blaisdell Company example from above. If we store the residuals from a simple linear regression model with response and predictor and then find the autocorrelation function for the residuals (select Stat > Time Series > Autocorrelation), we obtain the following output:
values for the DurbinWatson Test Statistic and P ..
When autocorrelated error terms are found to be present, then one of the first remedial measures should be to investigate the omission of a key predictor variable. If such a predictor does not aid in reducing/eliminating autocorrelation of the error terms, then certain transformations on the variables can be performed. We discuss three transformations which are designed for AR(1) errors. Methods for dealing with errors from an AR(k) process do exist in the literature, but are much more technical in nature.
I run the durbinwatson test over my variables using ..
One thing to note about the CochraneOrcutt approach is that it does not always work properly. This occurs primarily because if the errors are positively autocorrelated, then r tends to underestimate ( ho). When this bias is serious, then it can seriously reduce the effectiveness of the CochraneOrcutt procedure.