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Robust Inference for Inverse Stochastic Dominance

Author

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  • Francesco Andreoli
Abstract
The notion of inverse stochastic dominance is gaining increasing support in risk, inequality, and welfare analysis as a relevant criterion for ranking distributions, which is alternative to the standard stochastic dominance approach. Its implementation rests on comparisons of two distributions’ quantile functions, or of their multiple partial integrals, at fixed population proportions. This article develops a novel statistical inference model for inverse stochastic dominance that is based on the influence function approach. The proposed method allows model-free evaluations that are limitedly affected by contamination in the data. Asymptotic normality of the estimators allows to derive tests for the restrictions implied by various forms of inverse stochastic dominance. Monte Carlo experiments and an application promote the qualities of the influence function estimator when compared with alternative dominance criteria.

Suggested Citation

  • Francesco Andreoli, 2018. "Robust Inference for Inverse Stochastic Dominance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 146-159, January.
    • Handle: RePEc:taf:jnlbes:v:36:y:2018:i:1:p:146-159
    DOI: 10.1080/07350015.2015.1137758
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    Cited by:

    1. Hongyi Jiang & Zhenting Sun & Shiyun Hu, 2023. "A Nonparametric Test of $m$th-degree Inverse Stochastic Dominance," Papers 2306.12271, arXiv.org, revised Jul 2023.
    2. Francesco Andreoli & Tarjei Havnes & Arnaud Lefranc, 2019. "Robust Inequality of Opportunity Comparisons: Theory and Application to Early Childhood Policy Evaluation," The Review of Economics and Statistics, MIT Press, vol. 101(2), pages 355-369, May.
    3. Andreoli, Francesco & Fusco, Alessio, 2019. "Robust cross-country analysis of inequality of opportunity," Economics Letters, Elsevier, vol. 182(C), pages 86-89.
    4. Francesco Andreoli & Eugenio Peluso, 2021. "Inference for the neighbourhood inequality index," Spatial Economic Analysis, Taylor & Francis Journals, vol. 16(3), pages 313-332, July.
    5. Andreoli, Francesco & Olivera, Javier, 2020. "Preferences for redistribution and exposure to tax-benefit schemes in Europe," European Journal of Political Economy, Elsevier, vol. 63(C).
    6. Rolf Aaberge & Tarjei Havnes & Magne Mogstad, 2021. "Ranking intersecting distribution functions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 639-662, September.
    7. Francesco Andreoli & Claudio Zoli, 2020. "From unidimensional to multidimensional inequality: a review," METRON, Springer;Sapienza Università di Roma, vol. 78(1), pages 5-42, April.
    8. Francesco Andreoli & Mauro Mussini & Vincenzo Prete & Claudio Zoli, 2021. "Urban poverty: Measurement theory and evidence from American cities," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 599-642, December.
    9. Belzunce, Félix & Martínez-Riquelme, Carolina, 2023. "A new stochastic dominance criterion for dependent random variables with applications," Insurance: Mathematics and Economics, Elsevier, vol. 108(C), pages 165-176.

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