This paper establishes an asymptotic theory and inference method for quantile treatment effect estimators when the quantile index is close or equal to zero. Such quantile treatment effects are of interest in many economic 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 establishes new estimation and inference theory for cases close or equal to zero. In addition, finite sample properties of the new procedures are illustrated through both simulation studies and empirical applications.
Extreme quantile, Intermediate quantile
City or Country
Extremal quantile treatment effects. (2016). 1-126. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/2030
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