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Dual Regression

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  • Richard H. Spady
  • Sami Stouli

Abstract

We propose an alternative (‘dual regression’) 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 largely avoiding the need for ‘rearrangement’ to repair the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach relies on a mathematical programming characterization of conditional distribution functions which, in its simplest form, provides a simultaneous estimator of location and scale parameters in a linear heteroscedastic model. The statistical properties of this estimator are derived.

Suggested Citation

  • Richard H. Spady & Sami Stouli, 2016. "Dual Regression," Bristol Economics Discussion Papers 16/669, School of Economics, University of Bristol, UK.
    • Handle: RePEc:bri:uobdis:16/669
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    References listed on IDEAS

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    Cited by:

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

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