Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators
AbstractIndirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric model that are based on auxiliary non-parametric maximum likelihood density estimators are shown to be asymptotically normal. If the parametric model is correctly specified, it is furthermore shown that the asymptotic variance-covariance matrix equals the Cramér-Rao bound. These results are based on uniform-in-parameters convergence rates and a uniform-in-parameters Donsker-type theorem for non-parametric maximum likelihood density estimators.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 27512.
Date of creation: 16 Dec 2010
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Indirect inference; simulation-based minimum distance estimation; non-parametric maximum likelihood; density estimation; efficiency;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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- Filippo Altissimo & Antonio Mele, 2009. "Simulated Non-Parametric Estimation of Dynamic Models," Review of Economic Studies, Oxford University Press, vol. 76(2), pages 413-450.
- Gourieroux, C. & Monfort, A. & Renault, E., 1992.
Papers, Toulouse - GREMAQ
92.279, Toulouse - GREMAQ.
- Gourieroux, C & Monfort, A & Renault, E, 1993. "Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 8(S), pages S85-118, Suppl. De.
- Fermanian, Jean-David & Salani , Bernard, 2004. "A Nonparametric Simulated Maximum Likelihood Estimation Method," Econometric Theory, Cambridge University Press, vol. 20(04), pages 701-734, August.
- 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, Elsevier, vol. 140(2), pages 529-573, October.
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