REGDIAG gives the value of the Durbin-Watson statistics for testing the first order autocorrelation of residuals. To get its P-value enter a specification DWN=<integer> where <integer> must be at least 10000. Then the P-value will be computed by simulation using <integer> replicates. Also higher-order autocorrelations can be tested by the same method. By MAXLAG=k the order 1,2,...,k autocorrelations of residuals will be tested by using the generalized DW statistics sum [res(i)-res(i-j)]^2 D(j) = ----------------------- sum [res(i)]^2 where res(1),...,res(n) are the residuals of the estimated linear regression model Y = X*beta + eps where X is the n*m design matrix and eps is N(0,sigma^2*I). Then we have res=M*eps where M=I-X*inv(X'X)X'. By making the SVD decomposition X=U*D*V' where U is a n*m matrix the residuals can be computed by 2*m*n multiplications by res=(I-U*U')*eps. In randomized tests DWN replicates of D(j), j=1,2,...,k are computed and the P values are obtained as the proportion of replicates having a lower value than the original one. By using RN specification instead of DWN the randomized tests will be based on autocorrelations instead of DW values. E = Estimating regression models with autocorrelated disturbances R = More about REGDIAG

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