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Conditional Stochastic Dominance Testing

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  • Miguel A. Delgado
  • Juan Carlos Escanciano

Abstract

This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional distributions and their moments. We exploit the fact that a conditional distribution dominates the other if and only if the difference between the marginal joint distributions is monotonic in the explanatory variable at each value of the dependent variable. The proposed test statistic compares restricted and unrestricted estimators of the difference between the joint distributions, and it can be implemented under minimal smoothness requirements on the underlying nonparametric curves and without resorting to smooth estimation. The finite sample properties of the proposed test are examined by means of a Monte Carlo study. We illustrate the test by studying the impact on postintervention earnings of the National Supported Work Demonstration, a randomized labor training program carried out in the 1970s.

Suggested Citation

  • Miguel A. Delgado & Juan Carlos Escanciano, 2013. "Conditional Stochastic Dominance Testing," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 16-28, January.
    • Handle: RePEc:taf:jnlbes:v:31:y:2013:i:1:p:16-28
    DOI: 10.1080/07350015.2012.723556
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    Cited by:

    1. Pangpang Liu & Yichuan Zhao, 2024. "Smoothed empirical likelihood for the difference of two quantiles with the paired sample," Statistical Papers, Springer, vol. 65(4), pages 2077-2108, June.
    2. Jesus Gonzalo & Jose Olmo, 2014. "Conditional Stochastic Dominance Tests In Dynamic Settings," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 819-838, August.
    3. Lorenzo Tedesco & Ingrid Van Keilegom, 2023. "Comparison of quantile regression curves with censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 829-864, September.
    4. E. Agliardi & M. Pinar & T. Stengos, 2014. "Assessing temporal trends and industry contributions to air and water pollution using stochastic dominance," Working Papers wp981, Dipartimento Scienze Economiche, Universita' di Bologna.
    5. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    6. Federico A. Bugni & Ivan A. Canay & Deborah Kim, 2025. "Testing Conditional Stochastic Dominance at Target Points," Papers 2503.14747, arXiv.org, revised Nov 2025.
    7. repec:hum:wpaper:sfb649dp2014-028 is not listed on IDEAS
    8. Andrews, Donald W.K. & Shi, Xiaoxia, 2017. "Inference based on many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 196(2), pages 275-287.
    9. Shih-Kang Chao & Katharina Proksch & Holger Dette & Wolfgang Karl Härdle, 2017. "Confidence Corridors for Multivariate Generalized Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 70-85, January.
    10. Pedro H. C. Sant’Anna, 2021. "Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 816-832, July.
    11. Brendan Kline, 2016. "Identification of the Direction of a Causal Effect by Instrumental Variables," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 176-184, April.

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