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Pairwise-comparison estimation with nonparametric controls

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  • Koen Jochmans

    (Département d'économie)

Abstract

The purpose of this paper is the presentation of distribution theory for generic estimators based on the pairwise comparison of observations in problems where identification is achieved through the use of control functions. The controls can be specified semi- or non-parametrically. The criterion function may be non-smooth. The theory is applied to the estimation of the coefficients in a monotone linear-index model and to inference on the link function in a partially-linear transformation model. A number of simulation exercises serve to assess the small-sample performance of these techniques.

Suggested Citation

  • Koen Jochmans, 2013. "Pairwise-comparison estimation with nonparametric controls," Sciences Po Economics Discussion Papers 2013-04, Sciences Po Departement of Economics.
  • Handle: RePEc:spo:wpecon:info:hdl:2441/dambferfb7dfprc9m01h6f4h2
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    References listed on IDEAS

    as
    1. Jochmans, Koen, 2012. "The variance of a rank estimator of transformation models," Economics Letters, Elsevier, vol. 117(1), pages 168-169.
    2. 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.
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    Cited by:

    1. Shakeeb Khan & Arnaud Maurel & Yichong Zhang, 2019. "Informational Content of Factor Structures in Simultaneous Binary Response Models," Boston College Working Papers in Economics 985, Boston College Department of Economics.
    2. Jochmans, Koen, 2015. "Multiplicative-error models with sample selection," Journal of Econometrics, Elsevier, vol. 184(2), pages 315-327.
    3. Koen Jochmans, 2011. "Identification in Bivariate binary-choice Models with elliptical innovations," Working Papers hal-01069483, HAL.
    4. Chiappori, Pierre-André & Komunjer, Ivana & Kristensen, Dennis, 2015. "Nonparametric identification and estimation of transformation models," Journal of Econometrics, Elsevier, vol. 188(1), pages 22-39.

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