Author : Joseph G. Altonji
Publisher :
ISBN 13 :
Total Pages : 64 pages
Book Rating : 4.:/5 (318 download)
Book Synopsis Small Sample Bias in GMM Estimation of Covariance Structures by : Joseph G. Altonji
Download or read book Small Sample Bias in GMM Estimation of Covariance Structures written by Joseph G. Altonji and published by . This book was released on 1994 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: We examine the small sample properties of the GMM estimator for models of covariance structures, where the technique is often referred to as the optimal minimum distance (OMD) estimator. We present a variety of Monte Carlo experiments based on simulated data and on the data used by Abowd and Card (1987, 1990) in an examination of the covariance structure of hours and earnings changes. Our main finding is that OMD is seriously biased in small samples for many distributions and in relatively large samples for poorly behaved distributions. The bias is almost always downward in absolute value. It arises because sampling errors in the second moments are correlated with sampling errors in the weighting matrix used by OMD. Furthermore, OMD usually has a larger root mean square error and median absolute error than equally weighted minimum distance (EWMD). We also propose and investigate an alternative estimator, which we call independently weighted optimal minimum distance (IWOMD). IWOMD is a split sample estimator using separate groups of observations to estimate the moments and the weights. IWOMD has identical large sample properties to the OMD estimator but is unbiased regardless of sample size. However, the Monte Carlo evidence indicates that IWOMD is usually dominated by EWMD.