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Generalized empirical likelihood for a continuum of moment conditions


  • Pierre Chaussé

    (Department of Economics, University of Waterloo)


This paper extends the generalized empirical likelihood method to the case in which the moment conditions are defined on a continuum (CGEL). We show, for the iid case, that CGEL is asymptotically equivalent at the first order to the generalized method of moments for a continuum (CGMM) developed by Carrasco and Florens (2000). Because the system of equations that we need to solve becomes singular when the number of moment conditions converges to infinity, we treat CGEL as a nonlinear ill-posed problem and obtain the solution using the regularized Gauss-Newton method. This numerical algorithm is a fast and relatively easy way to compute the regularized Tikhonov solution to nonlinear ill-posed problems in function spaces. In order to compare the properties of CGEL and CGMM, we then perform a numerical study in which we estimate the parameters of a stable distribution using moment conditions based on the characteristic function. The results show that CGEL outperforms CGMM in most cases according to the root mean squared error criterion.

Suggested Citation

  • Pierre Chaussé, 2011. "Generalized empirical likelihood for a continuum of moment conditions," Working Papers 1104, University of Waterloo, Department of Economics, revised Oct 2011.
  • Handle: RePEc:wat:wpaper:1104

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    References listed on IDEAS

    1. Carrasco, Marine & Kotchoni, Rachidi, 2017. "Efficient Estimation Using The Characteristic Function," Econometric Theory, Cambridge University Press, vol. 33(02), pages 479-526, April.
    2. Antoine, Bertille & Bonnal, Helene & Renault, Eric, 2007. "On the efficient use of the informational content of estimating equations: Implied probabilities and Euclidean empirical likelihood," Journal of Econometrics, Elsevier, vol. 138(2), pages 461-487, June.
    3. Carrasco, Marine & Florens, Jean-Pierre, 2000. "Generalization Of Gmm To A Continuum Of Moment Conditions," Econometric Theory, Cambridge University Press, vol. 16(06), pages 797-834, December.
    4. Carrasco, Marine & Florens, Jean-Pierre & Renault, Eric, 2007. "Linear Inverse Problems in Structural Econometrics Estimation Based on Spectral Decomposition and Regularization," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 77 Elsevier.
    5. Garcia, René & Renault, Eric & Veredas, David, 2011. "Estimation of stable distributions by indirect inference," Journal of Econometrics, Elsevier, vol. 161(2), pages 325-337, April.
    6. Carrasco, Marine & Chernov, Mikhail & Florens, Jean-Pierre & Ghysels, Eric, 2007. "Efficient estimation of general dynamic models with a continuum of moment conditions," Journal of Econometrics, Elsevier, vol. 140(2), pages 529-573, October.
    7. Anatolyev, Stanislav & Gospodinov, Nikolay, 2011. "Specification Testing In Models With Many Instruments," Econometric Theory, Cambridge University Press, vol. 27(02), pages 427-441, April.
    8. Yuichi Kitamura & Gautam Tripathi & Hyungtaik Ahn, 2004. "Empirical Likelihood-Based Inference in Conditional Moment Restriction Models," Econometrica, Econometric Society, vol. 72(6), pages 1667-1714, November.
    9. Berkowitz, Jeremy, 2001. "Generalized spectral estimation of the consumption-based asset pricing model," Journal of Econometrics, Elsevier, vol. 104(2), pages 269-288, September.
    10. Donald, Stephen G. & Imbens, Guido W. & Newey, Whitney K., 2003. "Empirical likelihood estimation and consistent tests with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 117(1), pages 55-93, November.
    11. Dagenais, Marcel G., 1983. "Extension of the ridge regression technique to non-linear models with additive errors," Economics Letters, Elsevier, vol. 12(2), pages 169-174.
    12. Susanne M. Schennach, 2007. "Point estimation with exponentially tilted empirical likelihood," Papers 0708.1874,
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    Cited by:

    1. 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.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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