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Local Composite Quantile Regression for Regression Discontinuity

Xiao Huang, Zhaoguo Zhan

arXiv 8 Sep 2020 · Econometrics · publishedJournal of Business and Economic Statistics (2021) · 7 citations (OpenAlex)

arXiv:2009.03716 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. Kai et al. (2010) study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted t-test for inference, which leads to confidence intervals with good coverage probabilities. A bandwidth selector is also discussed. For illustration, we conduct a simulation study and revisit a classic example from Lee (2008). A companion R package rdcqr is developed.

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21
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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
1–-, –- and Titiunik, R (2014) Robust nonparametric confidence intervals for regression-discontinuity designs1.00085100%
2Lee, D. S (2008) Randomized experiments from non-random selection in us house elections1.00084100%
3Imbens, G. and Kalyanaraman, K (2012) Optimal bandwidth choice for the regression discontinuity estimator1.00053100%
4–-, –- and –- (2010) Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression0.93717882%
5Kai, B., Li, R. and Zou, H (2009) Supplement material for local composite quantile regression smoothing: an efficient and safe alternative to local polynomial reg…0.90912575%
6Calonico, S., Cattaneo, M. D. and Farrell, M. H (2018) On the effect of bias estimation on coverage accuracy in nonparametric inference0.87462100%
7Fan, J. and Gijbels, I (1996) Local polynomial modelling and its applications0.8434475%
8–-, –- and –- (2020) b)0.81142100%
9–-, –-, –- and Titiunik, R (2019) Regression Discontinuity Designs Using Covariates0.7817271%
10–- and Lemieux, T (2008) Regression discontinuity designs: A guide to practice0.58531100%

Showing the top 10 of 21 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
1Regression Discontinuity Designs0.40511