Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/211094 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
cemmap working paper No. CWP01/19
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
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.
Schlagwörter: 
Conditional distribution
Duality
Monotonicity
Quantile regression
Method of moments
Mathematical programming
Convex approximation
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Dokumentart: 
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