Victor Chernozhukov, Iván Fernández-Val, Blaise Melly, Kaspar Wüthrich
arXiv 18 Aug 2016 · Statistics — Methodology · publishedJournal of the American Statistical Association (2019) · 35 citations (OpenAlex)
arXiv:1608.05142 · PDF · DOI · OpenAlex · Extracted main text
Quantile and quantile effect functions are important tools for descriptive and causal analyses due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This paper offers a simple, practical construction of simultaneous confidence bands for quantile and quantile effect 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 affecting child development especially at the top of the distribution. These are new, interesting empirical findings that complement previous analyses that focused on mean effects only. In both applications, the outcomes of interest are discrete rendering existing inference methods invalid for obtaining uniform confidence bands for observed and counterfactual quantile functions and for their difference -- the quantile effects functions.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V., Fernández-Val, I., Melly, B (2013) Inference on counterfactual distributions self | 1.000 | 12 | 4 | 100% |
| 2 | Finkelstein, A., Taubman, S., Wright, B., Bernstein, M., Gruber, J.,… (2012) a | 0.874 | 5 | 2 | 100% |
| 3 | Fryer, R. G., Levitt, S. D (2013) Testing for racial differences in the mental ability of young children | 0.811 | 4 | 2 | 100% |
| 4 | Machado, J. A. F., Silva, J. S (2005) Quantiles for counts | 0.811 | 4 | 2 | 100% |
| 5 | Williams, O. D., Grizzle, J. E (1972) Analysis of contingency tables having ordered response categories | 0.737 | 3 | 2 | 100% |
| 6 | Foresi, S., Peracchi, F (1995) The conditional distribution of excess returns: An empirical analysis | 0.644 | 2 | 2 | 100% |
| 7 | Kiatsupaibul, S., Hayter, A. J (2015) Recursive confidence band construction for an unknown distribution function | 0.511 | 2 | 1 | 100% |
| 8 | Koenker, R., Xiao, Z (2002) Inference on the quantile regression process | 0.511 | 2 | 1 | 100% |
| 9 | Scheffe, H., Tukey, J. W (1945) Non-parametric estimation | 0.511 | 2 | 1 | 100% |
| 10 | Chernozhukov, V., Fernandez-Val, I., Galichon, A (2009) Improving point and interval estimators of monotone functions by rearrangement self | 0.405 | 1 | 1 | 100% |
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