Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes

• 成果类型: Article
• 署名作者: Chernozhukov, Victor; Fernandez-Val, Ivan; Melly, Blaise; Wuthrich, Kaspar
• 署名单位: Massachusetts Institute of Technology (MIT); Boston University; University of Bern; University of California System; University of California San Diego
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1611581
• 发表日期: 2020
• 页码: 123-137
• 关键词: regression; medicaid; misspecification

摘要

Quantile and quantile effect (QE) functions are important tools for descriptive and causal analysis due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This article offers a simple, practical construction of simultaneous confidence bands for quantile and QE functions of possibly discrete random variables. It is based on a natural transformation of simultaneous confidence bands for distribution functions, which are readily available for many problems. The construction is generic and does not depend on the nature of the underlying problem. It works in conjunction with parametric, semiparametric, and nonparametric modeling methods for observed and counterfactual distributions, and does not depend on the sampling scheme. We apply our method to characterize the distributional impact of insurance coverage on health care utilization and obtain the distributional decomposition of the racial test score gap. We find that universal insurance coverage increases the number of doctor visits across the entire distribution, and that the racial test score gap is small at early ages but grows with age due to socio-economic factors especially at the top of the distribution. (additional results, R package, replication files) for this article are available online.