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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 specication and estimation of a exible class of conditional distribution functions, and present asymptotic theory for the corresponding empirical dual regression process.
Authors
International Research Fellow Johns Hopkins
Richard's research has been primarily in theoretical econometrics, but has included topics in empirical industrial organization, labor economics, statistical theory, and government regulation of industry.
Working Paper details
- DOI
- 10.1920/wp.cem.2019.0119
- Publisher
- The IFS
Suggested citation
Spady, R. (2019). Dual regression. London: The IFS. Available at: https://ifs.org.uk/publications/dual-regression (accessed: 11 May 2024).
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