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. Dual regression can be appropriately modified to provide full structural distribution function estimates of the single equation instrumental variables model; this and similar extensions have implications for the analysis of identification in econometric models of endogeneity.
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.
Report details
- Publisher
- IFS
Suggested citation
Spady, R. (2012). Dual Regression. London: IFS. Available at: https://ifs.org.uk/publications/dual-regression-0 (accessed: 4 May 2024).
Related documents
More from IFS
Understand this issue
Where next for the state pension?
13 December 2023
Social mobility and wealth
12 December 2023
Autumn Statement 2023: IFS analysis
23 November 2023
Policy analysis
Recent trends in and the outlook for health-related benefits
19 April 2024
Progression of nurses within the NHS
12 April 2024
Regional variation in earnings and the retention of NHS staff in Agenda for Change bands 1 to 4
10 April 2024
Academic research
A senior doctor like me: Gender match and occupational choice
24 April 2024
Police infrastructure, police performance, and crime: Evidence from austerity cuts
24 April 2024
Imagine your life at 25: Gender conformity and later-life outcomes
24 April 2024