BOOTSTRAP METHODS FOR COVARIANCE-STRUCTURES

The optimal minimum distance (OMD) estimator for models of covariance structures is asymptotically efficient but has much worse finite-sampl e properties than does the equally weighted minimum distance (EWMD) es timator. This paper shows how the bootstrap can be used to improve the finite-sample performance of the OMD estimator The theory underlying the bootstrap's ability to reduce the bias of estimators and errors in the coverage probabilities of confidence intervals is summarized The results of numerical experiments and an empirical example show that th e bootstrap often essentially eliminates the bias of the OMD estimator . The finite-sample estimation efficiency of the bias-corrected OMD es timator often exceeds that of the EWMD estimator. Moreover, the true c overage probabilities of confidence intervals based on the OMD estimat or with bootstrap-critical values are very close to the nominal covera ge probabilities.