On modified maximum likelihood estimators of the autocorrelation coeffcient in linear models

Abstract

SUMMARY Two approximations to the maximum likelihood estimator of the autocorrelation coefficient in first-order Markov disturbances in a linear model have been previously suggested by Durbin & Watson. A Monte Carlo study shows that one of these estimators is less absolutely biased and has smaller variance than the exact maximum likelihood estimator. Further, this estimator exhibits greater power in tests of autocorrelation when used in place of the standard Durbin-Watson statistic. The second estimator, while having very small variance, is severely biased and of little use in power considerations.