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cwp041616.pdf
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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.
Authors
![Person graphic](/sites/default/files/styles/square_desktop/public/2022-06/IFS-person-graphic.png?itok=hWCtTSrz)
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.2016.0516
- Publisher
- Institute for Fiscal Studies
Suggested citation
Spady, R. (2016). Dual regression. London: Institute for Fiscal Studies. Available at: https://ifs.org.uk/publications/dual-regression-1 (accessed: 2 July 2024).
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