Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators

Gach, Florian and Pötscher, Benedikt M. (2010): Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators.

Abstract

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Indirect inference, simulation-based minimum distance estimation, non-parametric maximum likelihood, density estimation, efficiency
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General

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