Dual regression

Richard Spady and Sami Stouli
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Richard Spady: Institute for Fiscal Studies and Johns Hopkins

No CWP01/19, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 speci cation and estimation of a exible class of conditional distribution functions, and present asymptotic theory for the corresponding empirical dual regression process.

Keywords: Conditional distribution; Duality; Monotonicity; Quantile regression; Method of moments; Mathematical programming; Convex approximation (search for similar items in EconPapers)
Date: 2019-01-16
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Dual regression (2018)
Working Paper: Dual Regression (2018)
Working Paper: Dual regression (2016)
Working Paper: Dual Regression (2016)
Working Paper: Dual regression (2016)
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