Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators
Indirect 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.
|Date of creation:||16 Dec 2010|
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- 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, vol. 140(2), pages 529-573, October.
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- Gourieroux, C. & Monfort, A. & Renault, E., 1992. "Indirect Inference," Papers 92.279, Toulouse - GREMAQ.
- 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. Full references (including those not matched with items on IDEAS)
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