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Robust simulation-based estimation

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  • Genton, Marc G.
  • de Luna, Xavier

Abstract

The simulation-based inferential method called indirect inference was originally proposed for statistical models whose likelihood is difficult or even impossible to compute and/or to maximize. In this paper, indirect estimation is proposed as a device to robustify the estimation for models where this is not possible or difficult with classical techniques such as M-estimators. We derive the influence function of the indirect estimator, and present results about its gross-error sensitivity and asymptotic variance. Two examples from time series are used for illustration.

Suggested Citation

  • Genton, Marc G. & de Luna, Xavier, 2000. "Robust simulation-based estimation," Statistics & Probability Letters, Elsevier, vol. 48(3), pages 253-259, July.
    • Handle: RePEc:eee:stapro:v:48:y:2000:i:3:p:253-259
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    References listed on IDEAS

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    1. 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.
    2. Yanyuan Ma & Marc G. Genton, 2000. "Highly Robust Estimation of the Autocovariance Function," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(6), pages 663-684, November.
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    Cited by:

    1. Gallant, A. Ronald & Tauchen, George, 2002. "Simulated Score Methods and Indirect Inference for Continuous-time Models," Working Papers 02-09, Duke University, Department of Economics.
    2. Vicky Fasen‐Hartmann & Sebastian Kimmig, 2020. "Robust estimation of stationary continuous‐time arma models via indirect inference," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(5), pages 620-651, September.
    3. Xu, Yihuan & Iglewicz, Boris & Chervoneva, Inna, 2014. "Robust estimation of the parameters of g-and-h distributions, with applications to outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 66-80.

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