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Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs

Liang Jiang, Xiaobin Liu, Peter C. B. Phillips, Yichong Zhang

arXiv 25 May 2020 · Econometrics · publishedThe Review of Economics and Statistics (2021) · 3 citations (OpenAlex)

arXiv:2005.11967 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs). Standard multiplier bootstrap inference fails to capture the negative dependence of observations within each pair and is therefore conservative. Analytical inference involves estimating multiple functional quantities that require several tuning parameters. Instead, this paper proposes two bootstrap methods that can consistently approximate the limit distribution of the original QTE estimator and lessen the burden of tuning parameter choice. Most especially, the inverse propensity score weighted multiplier bootstrap can be implemented without knowledge of pair identities.

Citation extraction

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Groh, M. and D. J. McKenzie (2016) Macroinsurance for microenterprises: A randomized experiment in post-revolution egypt1.000133100%
2Bai, Y., A. Shaikh, and J. P. Romano (2021) Inference in experiments with matched pairs0.98219795%
3Hagemann, A (2017) Cluster-robust bootstrap inference in quantile regression models0.87452100%
4Bruhn, M. and D. McKenzie (2009) In pursuit of balance: Randomization in practice in development field experiments0.73732100%
5Firpo, S (2007) Efficient semiparametric estimation of quantile treatment effects0.73732100%
6van der Vaart, A. and J. A. Wellner (1996) Weak Convergence and Empirical Processes0.64811327%
7Donald, S. G. and Y.-C. Hsu (2014) Estimation and inference for distribution functions and quantile functions in treatment effect models0.64422100%
8Butler, D (2010) Monitoring Bureaucratic Compliance: Using Field Experiments to Improve Governance0.64422100%
9Panagopoulos, C. and D. P. Green (2008) Field experiments testing the impact of radio advertisements on electoral competition0.64422100%
10Banerjee, A., E. Duflo, R. Glennerster, and C. Kinnan (2015) The miracle of microfinance? evidence from a randomized evaluation0.51121100%

Showing the top 10 of 39 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1On the Efficiency of Highly Stratified Experiments0.73732
2Covariate Adjustment in Experiments with Matched Pairs0.51121
3Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations0.40511
4Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance0.40511
5Inference for Matched Tuples and Fully Blocked Factorial Designs0.40511
6Inference in Cluster Randomized Trials with Matched Pairs0.40511
7A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances0.40511
8Gradient Wild Bootstrap for Instrumental Variable Quantile Regressions with Weak and Few Clusters0.40511
9A New Design-Based Variance Estimator for Finely Stratified Experiments0.40511