Nonparametric estimation of finite mixtures
AbstractThe 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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Bibliographic InfoPaper provided by Sciences Po Departement of Economics in its series Sciences Po Economics Discussion Papers with number 2013-09.
Date of creation: Mar 2013
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finite-mixture model; nonparametric estimation; series expansion; simultaneousdiagonalization system.;
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