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Fast indirect robust generalized method of moments

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  • Loisel, Sébastien
  • Takane, Marina

Abstract

The Robust Generalized Methods of Moments (RGMM) and the Indirect Robust GMM (IRGMM) are algorithms for estimating parameter values in statistical models, such as diffusion models for interest rates, in a robust way. The long computation time is one of the main challenges facing these methods. In this paper, we introduce accelerated variants of RGMM and IRGMM. The fixed point iteration in RGMM is accelerated using minimal polynomial extrapolation, and the simulation of pseudo-observations in IRGMM is sped up by using a higher order stochastic Runge-Kutta method. We illustrate the fast performance of these algorithms for an interest rate diffusion model on four datasets.

Suggested Citation

  • Loisel, Sébastien & Takane, Marina, 2009. "Fast indirect robust generalized method of moments," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3571-3579, August.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:10:p:3571-3579
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    References listed on IDEAS

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Ronchetti, Elvezio & Trojani, Fabio, 2001. "Robust inference with GMM estimators," Journal of Econometrics, Elsevier, vol. 101(1), pages 37-69, March.
    3. Czellar, Veronika & Karolyi, G. Andrew & Ronchetti, Elvezio, 2007. "Indirect robust estimation of the short-term interest rate process," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 546-563, September.
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    7. Veronika Czellar & G. Andrew Karolyi & Elvezio Ronchetti, 2007. "Indirect Robust Estimation of the Short-Term Interest Rate Process," Post-Print hal-02313232, HAL.
    8. Dell'Aquila, Rosario & Ronchetti, Elvezio & Trojani, Fabio, 2003. "Robust GMM analysis of models for the short rate process," Journal of Empirical Finance, Elsevier, vol. 10(3), pages 373-397, May.
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    Cited by:

    1. Somayeh Kokabisaghi & Eric J. Pauwels & Katrien Van Meulder & André B. Dorsman, 2018. "Are These Shocks for Real? Sensitivity Analysis of the Significance of the Wavelet Response to Some CKLS Processes," IJFS, MDPI, vol. 6(3), pages 1-12, September.
    2. Yoshio Takane & Kwanghee Jung & Heungsun Hwang, 2010. "An acceleration method for Ten Berge et al.’s algorithm for orthogonal INDSCAL," Computational Statistics, Springer, vol. 25(3), pages 409-428, September.
    3. Sébastien Loisel & Yoshio Takane, 2011. "Generalized GIPSCAL re-revisited: a fast convergent algorithm with acceleration by the minimal polynomial extrapolation," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(1), pages 57-75, April.
    4. Khan, Jafar A. & Van Aelst, Stefan & Zamar, Ruben H., 2010. "Fast robust estimation of prediction error based on resampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3121-3130, December.

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