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Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference

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  • Nickl, Richard
  • Pötscher, Benedikt M.

Abstract

Given a random sample from a parametric model, we show how indirect inference estimators based on appropriate nonparametric density estimators (i.e., simulation-based minimum distance estimators) can be constructed that, under mild assumptions, are asymptotically normal with variance-covarince matrix equal to the Cramér-Rao bound.

Suggested Citation

  • Nickl, Richard & Pötscher, Benedikt M., 2009. "Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference," MPRA Paper 16608, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:16608
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    References listed on IDEAS

    as
    1. Fermanian, Jean-David & Salanié, Bernard, 2004. "A Nonparametric Simulated Maximum Likelihood Estimation Method," Econometric Theory, Cambridge University Press, vol. 20(4), pages 701-734, August.
    2. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(4), pages 657-681, October.
    3. 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.
    4. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 63-84, Suppl. De.
    5. 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.
    6. Filippo Altissimo & Antonio Mele, 2009. "Simulated Non-Parametric Estimation of Dynamic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(2), pages 413-450.
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    Cited by:

    1. Forneron, Jean-Jacques & Ng, Serena, 2018. "The ABC of simulation estimation with auxiliary statistics," Journal of Econometrics, Elsevier, vol. 205(1), pages 112-139.

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

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