Non‐parametric inference on (conditional) quantile differences and interquantile ranges, using L‐statistics

Matt Goldman and David Kaplan

Econometrics Journal, 2018, vol. 21, issue 2, 136-169

Abstract: We provide novel, high‐order accurate methods for non‐parametric inference on quantile differences between two populations in both unconditional and conditional settings. These quantile differences correspond to (conditional) quantile treatment effects under (conditional) independence of a binary treatment and potential outcomes. Our methods use the probability integral transform and a Dirichlet (rather than Gaussian) reference distribution to pick appropriate L‐statistics as confidence interval endpoints, achieving high‐order accuracy. Using a similar approach, we also propose confidence intervals/sets for vectors of quantiles, interquantile ranges and differences of linear combinations of quantiles. In the conditional setting, when smoothing over continuous covariates, optimal bandwidth and coverage probability rates are derived for all methods. Simulations show that the new confidence intervals have a favourable combination of robust accuracy and short length compared with existing approaches. Detailed steps for confidence interval construction are provided in online Appendix E as supporting information, and code for all methods, simulations and empirical examples is provided.

Date: 2018
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https://doi.org/10.1111/ectj.12095

Related works:
Working Paper: Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:wly:emjrnl:v:21:y:2018:i:2:p:136-169

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