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 ou...
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sg-smu-ink.soe_research-32062019-06-18T01:04:27Z Extremal quantile treatment effects ZHANG, Yichong 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. 2018-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/2207 info:doi/10.1214/17-AOS1673 https://ink.library.smu.edu.sg/context/soe_research/article/3206/viewcontent/MLIP1714_0043.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Extreme quantile Intermediate quantile Econometrics |
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Extreme quantile Intermediate quantile Econometrics ZHANG, Yichong Extremal quantile treatment effects |
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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. |
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ZHANG, Yichong |
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ZHANG, Yichong |
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ZHANG, Yichong |
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Extremal quantile treatment effects |
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Extremal quantile treatment effects |
title_full |
Extremal quantile treatment effects |
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Extremal quantile treatment effects |
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Extremal quantile treatment effects |
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extremal quantile treatment effects |
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Institutional Knowledge at Singapore Management University |
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2018 |
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https://ink.library.smu.edu.sg/soe_research/2207 https://ink.library.smu.edu.sg/context/soe_research/article/3206/viewcontent/MLIP1714_0043.pdf |
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