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Computing Generalized Method of Moments and Generalized Empirical Likelihood with R

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  • Pierre Chaussé

Abstract

This paper shows how to estimate models by the generalized method of moments and the generalized empirical likelihood using the R package gmm. A brief discussion is offered on the theoretical aspects of both methods and the functionality of the package is presented through several examples in economics and finance.

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File URL: http://www.jstatsoft.org/v34/i11/paper
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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 34 ()
Issue (Month): i11 ()
Pages:

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Handle: RePEc:jss:jstsof:34:i11

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Web page: http://www.jstatsoft.org/

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  1. Marine Carrasco & Jean-Pierre Florens, 2000. "Efficient GMM Estimation Using the Empirical Characteristic Function," Working Papers 2000-33, Centre de Recherche en Economie et Statistique.
  2. Susanne M. Schennach, 2007. "Point estimation with exponentially tilted empirical likelihood," Papers 0708.1874, arXiv.org.
  3. GARCIA, René & RENAULT, Eric & VEREDAS, David, 2006. "Estimation of stable distributions by indirect inference," CORE Discussion Papers 2006112, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Yves Croissant & Giovanni Millo, . "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, American Statistical Association, vol. 27(i02).
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Cited by:
  1. Paul S. Clarke & Tom M. Palmer & Frank Windmeijer, 2011. "Estimating Structural Mean Models with Multiple Instrumental Variables using the Generalised Method of Moments," The Centre for Market and Public Organisation 11/266, Department of Economics, University of Bristol, UK.
  2. Pierre Chausse & Dinghai Xu, 2012. "GMM Estimation of a Stochastic Volatility Model with Realized Volatility: A Monte Carlo Study," Working Papers 1203, University of Waterloo, Department of Economics, revised May 2012.

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