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.
Authors
![Jean-Marc Robin](/sites/default/files/styles/square_desktop/public/2022-07/Jean-Marc%20Robin.jpg?itok=umZw1Ufv)
Research Fellow Sciences Po and University College London
Jean-Marc is a Research Fellow of the IFS and a Professor of Economics at Sciences Po, Paris, and University College London.
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
University of Cambridge
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
Professor of Economics University of Chicago
Working Paper details
- DOI
- 10.1920/wp.cem.2014.1114
- Publisher
- Institute for Fiscal Studies
Suggested citation
S, Bonhomme and K, Jochmans and J, Robin. (2014). Nonparametric estimation of finite measures. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/nonparametric-estimation-finite-measures (accessed: 2 July 2024).
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