Nonparametric estimation of finite mixtures - Stéphane Bonhomme; Koen Jochmans; Jean-Marc Robin (Centre for Microdata Methods and Practice (cemmap), 2014)
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: 330; finite-mixture model; nonparametric estimation; series expansion; simultaneousdiagonalization system