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Quantile regression 40 years on

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  • Roger Koenker

Abstract

Since Quetelet's work in the 19th century social science has iconified "the average man", that hypothetical man without qualities who is comfortable with his head in the oven, and his feet in a bucket of ice. Conventional statistical methods, since Quetelet, have sought to estimate the effects of policy treatments for this average man. But such effects are often quite heterogenous: medical treatments may improve life expectancy, but also impose serious short term risks; reducing class sizes may improve performance of good students, but not help weaker ones or vice versa. Quantile regression methods can help to explore these heterogeneous effects. Some recent developments in quantile regression methods are surveyed below.This paper was published in Annual Review of Economics, 9, 155-76, (2017).

Suggested Citation

  • Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers 36/17, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:36/17
    DOI: 10.1920/wp.cem.2017.3617
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    14. Mingshu Li & Bhaskarjit Sarmah & Dhruv Desai & Joshua Rosaler & Snigdha Bhagat & Philip Sommer & Dhagash Mehta, 2024. "Quantile Regression using Random Forest Proximities," Papers 2408.02355, arXiv.org.
    15. Lamarche, Carlos & Shi, Xuan & Young, Derek S., 2024. "Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data," Econometrics and Statistics, Elsevier, vol. 31(C), pages 49-65.
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