Nonparametric estimation of finite mixtures

Abstract : The aim of this paper is to provide simple nonparametric methods to estimate finitemixture models from data with repeated measurements. Three measurements suffice for the mixture to be fully identified and so our approach can be used even with very short panel data. We provide distribution theory for estimators of the mixing proportions and the mixture distributions, and various functionals thereof. We also discuss inference on the number of components. These estimators are found to perform well in a series of Monte Carlo exercises. We apply our techniques to document heterogeneity in log annual earnings using PSID data spanning the period 1969-1998.
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Pré-publication, Document de travail
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Soumis le : jeudi 3 avril 2014 - 20:07:52
Dernière modification le : mardi 18 juin 2019 - 01:11:27
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Stéphane Bonhomme, Koen Jochmans, Jean-Marc Robin. Nonparametric estimation of finite mixtures. 2013. ⟨hal-00972868⟩



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