The Annals of Statistics
- Ann. Statist.
- Volume 46, Number 6B (2018), 3707-3740.
Extremal quantile treatment effects
This paper establishes an asymptotic theory and inference method for quantile treatment effect estimators when the quantile index is close to or equal to zero. Such quantile treatment effects are of interest in many applications, such as the effect of maternal smoking on an infant’s adverse birth outcomes. When the quantile index is close to zero, the sparsity of data jeopardizes conventional asymptotic theory and bootstrap inference. When the quantile index is zero, there are no existing inference methods directly applicable in the treatment effect context. This paper addresses both of these issues by proposing new inference methods that are shown to be asymptotically valid as well as having adequate finite sample properties.
Ann. Statist., Volume 46, Number 6B (2018), 3707-3740.
Received: February 2017
Revised: November 2017
First available in Project Euclid: 11 September 2018
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Zhang, Yichong. Extremal quantile treatment effects. Ann. Statist. 46 (2018), no. 6B, 3707--3740. doi:10.1214/17-AOS1673. https://projecteuclid.org/euclid.aos/1536631288
- Supplement to “Extremal quantile treatment effects”. This supplement contains all the proofs, two empirical applications, and simulation results.