IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i10p3571-3579.html
   My bibliography  Save this article

Fast indirect robust generalized method of moments

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00128-5
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    4. Genton M.G. & Ronchetti E., 2003. "Robust Indirect Inference," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 67-76, January.
    5. Chan, K C, et al, 1992. "An Empirical Comparison of Alternative Models of the Short-Term Interest Rate," Journal of Finance, American Finance Association, vol. 47(3), pages 1209-1227, July.
    6. Gourieroux, C & Monfort, A & Renault, E, 1993. "Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 85-118, Suppl. De.
    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.
    9. Brennan, Michael J. & Schwartz, Eduardo S., 1977. "Savings bonds, retractable bonds and callable bonds," Journal of Financial Economics, Elsevier, vol. 5(1), pages 67-88, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Cizek, P., 2009. "Generalized Methods of Trimmed Moments," Discussion Paper 2009-25, Tilburg University, Center for Economic Research.
    3. Zuzana Buckova & Beata Stehlikova & Daniel Sevcovic, 2016. "Numerical and analytical methods for bond pricing in short rate convergence models of interest rates," Papers 1607.04968, arXiv.org.
    4. Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2013. "Indirect Inference in fractional short-term interest rate diffusions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 109-126.
    5. Christiansen, Charlotte, 2008. "Level-ARCH short rate models with regime switching: Bivariate modeling of US and European short rates," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 925-948, December.
    6. Emma M. Iglesias & Garry D. A. Phillips, 2020. "Further Results on Pseudo‐Maximum Likelihood Estimation and Testing in the Constant Elasticity of Variance Continuous Time Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 357-364, March.
    7. Hou, Ai Jun & Suardi, Sandy, 2011. "Modelling and forecasting short-term interest rate volatility: A semiparametric approach," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 692-710, September.
    8. Al-Zoubi, Haitham A., 2019. "Bond and option prices with permanent shocks," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 272-290.
    9. Hong, Yongmiao & Lin, Hai & Wang, Shouyang, 2010. "Modeling the dynamics of Chinese spot interest rates," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 1047-1061, May.
    10. Calvet, Laurent E. & Czellar, Veronika, 2015. "Through the looking glass: Indirect inference via simple equilibria," Journal of Econometrics, Elsevier, vol. 185(2), pages 343-358.
    11. Gagliardini, Patrick & Trojani, Fabio & Urga, Giovanni, 2005. "Robust GMM tests for structural breaks," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 139-182.
    12. Calvet, Laurent-Emmanuel & Czellar , Veronika, 2011. "state-observation sampling and the econometrics of learning models," HEC Research Papers Series 947, HEC Paris.
    13. repec:wyi:journl:002108 is not listed on IDEAS
    14. Andersen, Torben G. & Lund, Jesper, 1997. "Estimating continuous-time stochastic volatility models of the short-term interest rate," Journal of Econometrics, Elsevier, vol. 77(2), pages 343-377, April.
    15. Broze, Laurence & Scaillet, Olivier & Zakoian, Jean-Michel, 1995. "Testing for continuous-time models of the short-term interest rate," Journal of Empirical Finance, Elsevier, vol. 2(3), pages 199-223, September.
    16. Tauchen, George E., 1995. "New Minimum Chi-Square Methods in Empirical Finance," Working Papers 95-42, Duke University, Department of Economics.
    17. Anna Gottard & Giorgio Calzolari, 2014. "Alternative estimating procedures for multiple membership logit models with mixed effects: indirect inference and data cloning," Econometrics Working Papers Archive 2014_07, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    18. Ait-Sahalia, Yacine, 1996. "Nonparametric Pricing of Interest Rate Derivative Securities," Econometrica, Econometric Society, vol. 64(3), pages 527-560, May.
    19. Zongwu Cai & Yongmiao Hong, 2013. "Some Recent Developments in Nonparametric Finance," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    20. Brandt, Michael W. & Santa-Clara, Pedro, 2002. "Simulated likelihood estimation of diffusions with an application to exchange rate dynamics in incomplete markets," Journal of Financial Economics, Elsevier, vol. 63(2), pages 161-210, February.
    21. Otunuga Olusegun M. & Ladde Gangaram S. & Ladde Nathan G., 2019. "Local Lagged Adapted Generalized Method of Moments: An Innovative Estimation and Forecasting Approach and its Applications," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-72, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:10:p:3571-3579. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.