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Nonparametric estimation of finite measures

Author

Listed:
  • Stéphane Bonhomme

    (Institute for Fiscal Studies and University of Chicago)

  • Koen Jochmans

    (Institute for Fiscal Studies and University of Cambridge)

  • Jean-Marc Robin

    (Institute for Fiscal Studies and Sciences Po and University College London)

Abstract

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.

Suggested Citation

  • Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2014. "Nonparametric estimation of finite measures," CeMMAP working papers CWP11/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:11/14
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    File URL: http://www.cemmap.ac.uk/wps/cwp111414.pdf
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    References listed on IDEAS

    as
    1. Kleibergen, Frank & Paap, Richard, 2006. "Generalized reduced rank tests using the singular value decomposition," Journal of Econometrics, Elsevier, vol. 133(1), pages 97-126, July.
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    3. Martin Browning & Mette Ejrnæs & Javier Alvarez, 2010. "Modelling Income Processes with Lots of Heterogeneity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(4), pages 1353-1381.
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    12. Robert A. Moffitt & Peter Gottschalk, 2012. "Trends in the Transitory Variance of Male Earnings: Methods and Evidence," Journal of Human Resources, University of Wisconsin Press, vol. 47(1), pages 204-236.
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