Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs
Liang Jiang (),
Xiaobin Liu () and
Yichong Zhang ()
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Liang Jiang: Fudan University
Xiaobin Liu: Zhejiang University
Yichong Zhang: School of Economics, Singapore Management University
No 15-2020, Economics and Statistics Working Papers from Singapore Management University, School of Economics
This paper examines inference for quantile treatment eﬀects (QTEs) in randomized experiments with matched-pairs designs (MPDs). We derive the limiting distribution of the QTE estimator under MPDs and highlight the difficulty of analytical inference due to parameter tuning. We show that a naive weighted bootstrap fails to approximate the limiting distribution of the QTE estimator under MPDs because it ignores the dependence structure within the matched pairs. We then propose two bootstrap methods that can consistently approximate that limiting distribution: the gradient bootstrap and the weighted bootstrap of the inverse propensity score weighted (IPW) estimator. The gradient bootstrap is free of tuning parameters but requires the knowledge of pairs’ identities. The weighted bootstrap of the IPW estimator does not require such knowledge but involves one tuning parameter. Both methods are straightforward to implement and able to provide pointwise conﬁdence intervals and uniform conﬁdence bands that achieve exact limiting rejection probabilities under the null. We illustrate their finite sample performance using both simulations and a well-known dataset on microﬁnance.
Keywords: Bootstrap inference; matched pairs; quantile treatment effect; randomized control trials (search for similar items in EconPapers)
JEL-codes: C14 C21 (search for similar items in EconPapers)
Pages: 74 pages
New Economics Papers: this item is included in nep-ecm, nep-exp, nep-ore and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:ris:smuesw:2020_015
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