Nonparametric estimation of finite mixtures

Stéphane Bonhomme, Koen Jochmans and Jean-Marc Robin

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.

Keywords: Series expansion; Nonparametric estimation; Finite-mixture model; Simultaneousdiagonalization system (search for similar items in EconPapers)
Date: 2013-03
Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-00972868
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Citations: View citations in EconPapers (8)

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Related works:
Working Paper: Nonparametric estimation of finite mixtures (2013)
Working Paper: Nonparametric estimation of finite mixtures (2013)
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