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On the Accuracy and Efficiency of GMM Estimators: A Monte Carlo Study

  • A. J. Hughes Hallett
  • Yue Ma
Part of the Advances in Computational Economics book series (AICE, volume 3)

Abstract

GMM estimators are now widely used in econometric and financial analysis. Their asymptotic properties are well known, but we have little knowledge of their small sample properties or their rate of convergence to their limiting distribution. This paper reports small sample Monte Carlo evidence which helps discriminate between the many GMM estimators proposed in the literature. We add a new GMM estimator which delivers better finite sample properties. We also test whether biases in the parameter estimates are either significant or significantly different between estimators. We conclude that they are, with both relative and absolute biases depending on sample size, fitting criterion, non-normality of disturbances, and parameter size.

Keywords

Maximum Likelihood Estimator Beta Distribution Significant Bias Small Sample Property Small Sample Bias 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1994

Authors and Affiliations

  • A. J. Hughes Hallett
  • Yue Ma

There are no affiliations available

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