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Pairwise-comparison estimation with non-parametric controls

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  • Koen Jochmans

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

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 non-parametric controls," Post-Print hal-03399882, HAL.
    • Handle: RePEc:hal:journl:hal-03399882
    DOI: 10.1111/ectj.1200
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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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    12. Jochmans, Koen, 2012. "The variance of a rank estimator of transformation models," Economics Letters, Elsevier, vol. 117(1), pages 168-169.
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    Cited by:

    1. Jochmans, Koen, 2015. "Multiplicative-error models with sample selection," Journal of Econometrics, Elsevier, vol. 184(2), pages 315-327.
    2. Shakeeb Khan & Arnaud Maurel & Yichong Zhang, 2023. "Informational Content of Factor Structures in Simultaneous Binary Response Models," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 385-410, Emerald Group Publishing Limited.
    3. Juan Carlos Escanciano & Joel Robert Terschuur, 2022. "Machine Learning Inference on Inequality of Opportunity," Papers 2206.05235, arXiv.org, revised Oct 2023.

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