The most common approach to estimating conditional quantile curves is to fit a curve, typically linear, pointwise for each quantile. Linear functional forms, coupled with pointwise fitting, are used for a number of reasons including parsimony of the resulting approximations and good computational properties. The resulting fits, however, may not respect a logical monotonicity requirement that the quantile curve be increasing as a function of probability. This paper studies the natural monotonization of these empirical curves induced by sampling from the estimated non-monotone model, and then taking the resulting conditional quantile curves that by construction are monotone in the probability.
Authors
Journal article details
- DOI
- 10.3982/ECTA7880
- Publisher
- Wiley Online Library
- Issue
- Volume 78, Issue 3, May 2010
Suggested citation
V, Chernozhukov and I, Fernandez-Val and A, Galichon. (2010). 'Quantile and probability curves without crossing' 78(3/2010)
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