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Inference on Mixtures Under Tail Restrictions

Author

Listed:
  • Marc Henry

    (Départment de sciences économiques)

  • Koen Jochmans

    (Département d'économie)

  • Bernard Salanié

    (Department of Economics (Columbia))

Abstract

Many econometric models can be analyzed as finite mixtures. We focus on two-component mixtures and we show that they are nonparametrically point identified by a combination of an exclusion restriction and tail restrictions. Our identification analysis suggests simple closed-form estimators of the component distributions and mixing proportions. We derive their asymptotic properties using results on tail empirical processes and we present a simulation study that documents their finite-sample performance.

Suggested Citation

  • Marc Henry & Koen Jochmans & Bernard Salanié, 2015. "Inference on Mixtures Under Tail Restrictions," Sciences Po publications 2014-01, Sciences Po.
  • Handle: RePEc:spo:wpmain:info:hdl:2441/f6h8764enu2lskk9p2m96cphi
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    References listed on IDEAS

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    1. V. Chernozhukov & I. Fernández-Val & A. Galichon, 2009. "Improving point and interval estimators of monotone functions by rearrangement," Biometrika, Biometrika Trust, vol. 96(3), pages 559-575.
    2. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2013. "Nonparametric estimation of finite mixtures," Sciences Po Economics Discussion Papers 2013-09, Sciences Po Departement of Economics.
    3. Arthur Lewbel, 2007. "Estimation of Average Treatment Effects with Misclassification," Econometrica, Econometric Society, vol. 75(2), pages 537-551, March.
    4. Einmahl, J.H.J., 1992. "Limit theorems for tail processes with application to intermediate quantile estimation," Other publications TiSEM 063e51b0-445d-4764-96a2-4, Tilburg University, School of Economics and Management.
    5. Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
    6. Donald W. K. Andrews & Marcia M. A. Schafgans, 1998. "Semiparametric Estimation of the Intercept of a Sample Selection Model," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 497-517.
    7. Aprajit Mahajan, 2006. "Identification and Estimation of Regression Models with Misclassification," Econometrica, Econometric Society, vol. 74(3), pages 631-665, May.
    8. Hiroyuki Kasahara & Katsumi Shimotsu, 2009. "Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices," Econometrica, Econometric Society, vol. 77(1), pages 135-175, January.
    9. Laurent Bordes & Céline Delmas & Pierre Vandekerkhove, 2006. "Semiparametric Estimation of a Two-component Mixture Model where One Component is known," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(4), pages 733-752.
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    Cited by:

    1. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2014. "Nonparametric spectral-based estimation of latent structures," CeMMAP working papers CWP18/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Jean-Marc Robin & Stéphane Bonhomme & Koen Jochmans, 2014. "Estimating Multivariate Latent-Structure Models," Sciences Po Economics Discussion Papers 2014-18, Sciences Po Departement of Economics.

    More about this item

    Keywords

    mixture model; nonparametric identification and estimation; tail empirical process;

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