Generalised method of moments

Alastair Hall, Economics.

Video: Alistair Hall

Generalised method of moments by Alisdair Hall

Video: Alistair Hall

What is generalised method of moments? by Alistair Hall

Generalized Method of Moments (GMM) provides a computationally convenient method for estimating the parameters of statistical models based on the information in population moment conditions.

Structure and flexibility

This structure has made it very popular in econometrics because competing economic theories often imply that economic variables satisfy different sets of population moment conditions. The specific form of these population moment conditions depends on the context but the generic form of the GMM estimator is the same in each case.

This flexibility means that GMM has been implemented in very diverse areas spanning macroeconomics, finance, agricultural economics, environmental economics and labour economics.

Use and development

Its widespread use in econometrics has both stimulated and been facilitated by the development of numerous statistical inference techniques based on GMM estimators. These inference techniques allow researchers, inter alia, to test hypotheses about the parameters of the econometric model and also to test whether the population moment conditions are consistent with the data.

In addition, GMM subsumes many other well-known estimators, such as least squares, instrumental variables and maximum likelihood. As a result, GMM provides a convenient framework for considering general aspects of estimation and inference in statistics, and, in many ways, is becoming the common language of econometric dialogue.

Manchester experts

Additional resources

  • Alastair R. Hall, 2005, Generalized Method of Moments, Oxford University Press, Oxford, UK
  • Kostas Kyriakoulis, GMM Toolbox for MATLAB (The help files and examples for this toolbox are linked to Hall, 2005)
  • Generalized Method of Moments Estimation, Laszlo Matyas (ed.), 1999, Cambridge University Press, Cambridge, UK.

This talk was also given 29 November, 2012 as part of the methods@manchester seminar series.

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