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Dual Regression

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  • Richard Spady
  • Sami Stouli

Abstract

We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution functions which, in its simplest form, is the dual program of a simultaneous estimator for linear location-scale models. We apply our general characterization to the specification and estimation of a flexible class of conditional distribution functions, and present asymptotic theory for the corresponding empirical dual regression process.

Suggested Citation

  • Richard Spady & Sami Stouli, 2012. "Dual Regression," Papers 1210.6958, arXiv.org, revised Sep 2018.
    • Handle: RePEc:arx:papers:1210.6958
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    References listed on IDEAS

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    Cited by:

    1. Richard Spady & Sami Stouli, 2018. "Simultaneous mean-variance regression," CeMMAP working papers CWP25/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Newey, Whitney & Stouli, Sami, 2021. "Control variables, discrete instruments, and identification of structural functions," Journal of Econometrics, Elsevier, vol. 222(1), pages 73-88.

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