Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/97364
Authors: 
Bonhomme, Stéphane
Jochmans, Koen
Robin, Jean-Marc
Year of Publication: 
2014
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP11/14
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.
Subjects: 
finite-mixture model
nonparametric estimation
series expansion
simultaneousdiagonalization system
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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