Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs

Zhongjun Qu and Jungmo Yoon

Journal of Business & Economic Statistics, 2019, vol. 37, issue 4, 625-647

Abstract: This study develops methods for conducting uniform inference on quantile treatment effects for sharp regression discontinuity designs. We develop a score test for the treatment significance hypothesis and Wald-type tests for the hypotheses related to treatment significance, homogeneity, and unambiguity. The bias from the nonparametric estimation is studied in detail. In particular, we show that under some conditions, the asymptotic distribution of the score test is unaffected by the bias, without under-smoothing. For situations where the conditions can be restrictive, we incorporate a bias correction into the Wald tests and account for the estimation uncertainty. We also provide a procedure for constructing uniform confidence bands for quantile treatment effects. As an empirical application, we use the proposed methods to study the effect of cash-on-hand on unemployment duration. The results reveal pronounced treatment heterogeneity and also emphasize the importance of considering the long-term unemployed.

Date: 2019
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Working Paper: Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs (2015)
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DOI: 10.1080/07350015.2017.1407323

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