Reduced Bias Autocorrelation Estimation:Three Jackknife Methods

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Abstract
The effectiveness of jackknife methods in reducing bias in the estimation of the lag-I autocorrelation parameter Pi was evaluated. A Monte Carlo investigation was carried out to study the empirical bias, mean-square error, and variance properties of three jackknife estimators using sample sizes that ranged from 6 through 500. The results demonstrated that these estimators are far less biased in the small sample case than are many other estimators that have been recently investigated. Results on the mean-squared error revealed that the advantage of greatly reduced bias associated with the jackknife estimators does not overcome the disadvantage of increased error variance. Three previously investigated estimators yield smaller mean-squared error than do the jackknife estimators or the conventional estimator at most sample sizes.

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