REDUCED BIAS AUTOCORRELATION ESTIMATION - 3 JACKKNIFE METHODS

The effectiveness of jackknife methods in reducing bias in the estimat ion of the lag-1 autocorrelation parameter rho1 was evaluated. A Monte Carlo investigation was carried out to study the empirical bias, mean -square error, and variance properties of three jackknife estimators u sing sample sizes that ranged from 6 through 500. The results demonstr ated that these estimators are far less biased in the small sample cas e than are many other estimators that have been recently investigated. Results on the mean-squared error revealed that the advantage of grea tly reduced bias associated with the jackknife estimators does not ove rcome the disadvantage of increased error variance. Three previously i nvestigated estimators yield smaller mean-squared error than do the ja ckknife estimators or the conventional estimator at most sample sizes.