Abstract: A feature of GMM estimationthe use of a consistent estimate
of the optimal weighting matrix rather than the joint estimation of
the model parameters and the weighting matrixcan lead to the
sensitivity of GMM estimation to the choice of parameter normalization.
In many applications, including Euler equation estimation, a model parameter multiplies the equation error in some, but not all, normalizations. But, conventional GMM estimators that either hold the estimate of the weighting matrix fixed or allow some limited iteration on the weighting matrix fail to account for the dependence of the weighting matrix on the parameter vector
implied by the multiplication of the error by the parameter. In finite
samples, GMM effectively minimizes the square of the parameter times the
objective function that obtains from an alternative normalization
where no parameter multiplies the equation error, resulting in
estimates that are smaller (in absolute value) than those from the
alternative normalization. Of course, normalization is irrelevant
asymptotically.
Keywords: GMM, finitesample, normalization, eulerequation, adjustmentcostsemployment
Full paper (738 KB PDF)
 Full paper (1089 KB Postscript)
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Last update: September 23, 1997
