Small-Sample Bias in GMM Estimation of Covariance Structures.
The authors examine the small sample properties of the generalized method of moments estimator applied to models of covariance structures, where it is commonly known as the optimal minimum distance (OMD) estimator. They 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). The authors also propose an alternative estimator that is unbiased and asymptotically equivalent to OMD. However, the Monte Carlo evidence indicates that it is usually dominated by EWMD.
Year of publication: |
1996
|
---|---|
Authors: | Altonji, Joseph G ; Segal, Lewis M |
Published in: |
Journal of Business & Economic Statistics. - American Statistical Association. - Vol. 14.1996, 3, p. 353-66
|
Publisher: |
American Statistical Association |
Saved in:
Saved in favorites
Similar items by person
-
The Marginal Propensity to Spend on Adult Children
Altonji, Joseph G, (2007)
-
Testing the Response of Consumption to Income Changes with (Noisy) Panel Data.
Altonji, Joseph G, (1987)
-
Labor Supply Preferences, Hours Constraints, and Hours-Wage Trade-Offs.
Altonji, Joseph G, (1988)
- More ...