Year: 1994
Type: report
Abstract: We examine the small-sample properties of the generalized method of moments estimator applied to models of covariance structures, in which case it is commonly known as the optimal minimum distance (OMD) estimator. We find that OMD is almost always biased downward in absolute value. The bias arises because sampling errors in the second moments are correlated with sampling errors in the weighting matrix used by OMD. Furthermore, OMD is usually dominated by equally weighted minimum distance (EWMD). We also propose an alternative estimator that is unbiased and asymptotically equivalent to OMD. The Monte Carlo evidence indicates, however, that it is usually dominated by EWMD.
DOI: 10.3386/t0156
Authors: Joseph Altonji, Lewis Segal
Institutions: Northwestern University, Federal Reserve Bank of Chicago
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3121917322