Volume 33, Number 1 (Winter) 1998: Symposium on Microeconomic Methods

Horowitz, Joel. 1998. "Bootstrap Methods for Covariance Structures." Journal of Human Resources 33(1):39-61.

The optimal minimum distance (OMD) estimator for models of covariance structures is asymptotically efficient but has much worse finite-sample properties than does the equally weighted minimum distance (EWMD) estimator. 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 the bootstrap often essentially eliminates the bias of the OMD estimator. The finite-sample estimation efficiency of the bias-corrected OMD estimator often exceeds that of the EWMD estimator. Moreover, the true coverage probabilities of confidence intervals based on the OMD estimator with bootstrap-critical values are very close to the nominal coverage probabilities.

Joel L Horowitz is Henry B. Tipple research professor of economics at the University of Iowa. The author thanks Joseph Altonji for attracting his attention to the problem addressed in this paper and for making his data available. Richard Blundell and Gene Savin provided comments on earlier drafts. This research was supported in part by NSF grants SBR-9307677 and SBR-9617925. The data used in this article are available from May 1998 through February 2001 from the author, Department of Economics, University of Iowa, Iowa City, IA, 52242.