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Inference on Two-Component Mixtures under Tail Restrictions

Author

Listed:
  • Koen Jochmans

    (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique)

  • Marc Henry

    (Penn State - Pennsylvania State University [State College, PA] - Penn State System)

  • Bernard Salanié

    (Columbia University [New York])

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, as well as a specification test. 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

  • Koen Jochmans & Marc Henry & Bernard Salanié, 2017. "Inference on Two-Component Mixtures under Tail Restrictions," Sciences Po Economics Publications (main) hal-03945858, HAL.
    • Handle: RePEc:hal:spmain:hal-03945858
    DOI: 10.1017/S0266466616000098
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    Cited by:

    1. Kédagni, Désiré, 2023. "Identifying treatment effects in the presence of confounded types," Journal of Econometrics, Elsevier, vol. 234(2), pages 479-511.
    2. Yuichi Kitamura & Louise Laage, 2018. "Nonparametric Analysis of Finite Mixtures," Papers 1811.02727, arXiv.org.
    3. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.

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