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The GMM parameter normalization puzzle

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  • Charles A. Fleischman

Abstract

A feature of GMM estimation--the use of a consistent estimate of the optimal weighting matrix rather than the joint estimation of the model parameters and the weighting matrix--can 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.

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Bibliographic Info

Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 1997-43.

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Date of creation: 1997
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Handle: RePEc:fip:fedgfe:1997-43

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Related research

Keywords: Econometrics;

References

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  1. Krane, Spencer D & Braun, Stephen N, 1991. "Production Smoothing Evidence from Physical-Product Data," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 558-81, June.
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Cited by:
  1. Joshua Hojvat Gallin, 1999. "Net migration and state labor market dynamics," Finance and Economics Discussion Series 1999-16, Board of Governors of the Federal Reserve System (U.S.).

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