Nonparametric estimation of finite measures
Stéphane Bonhomme (),
Koen Jochmans and
Jean-Marc Robin ()
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Stéphane Bonhomme: Institute for Fiscal Studies and University of Chicago
No CWP11/14, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
The aim of this paper is to provide simple nonparametric methods to estimate finite mixture 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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