Jad Beyhum, Jean-Pierre Florens, Ingrid Van Keilegom
arXiv 3 May 2021 · Statistics — Methodology
arXiv:2105.00946 · PDF · DOI · OpenAlex · Extracted main text
This paper discusses endogenous treatment models with duration outcomes, competing risks and random right censoring. The endogeneity issue is solved using a discrete instrumental variable. We show that the competing risks model generates a non-parametric quantile instrumental regression problem. The cause-specific cumulative incidence, the cause-specific hazard and the subdistribution hazard can be recovered from the regression function. A distinguishing feature of the model is that censoring and competing risks prevent identification at some quantiles. We characterize the set of quantiles for which exact identification is possible and give partial identification results for other quantiles. We outline an estimation procedure and discuss its properties. The finite sample performance of the estimator is evaluated through simulations. We apply the proposed method to the Health Insurance Plan of Greater New York experiment.
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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 \ Hansen (2005) An IV model of quantile treatment effects, Econometrica 73(1): 245–261 | 1.000 | 6 | 3 | 100% |
| 2 | Beyhum, Florens \ Van Keilegom (2021) Nonparametric instrumental regression with right censored duration outcomes, Journal of Business & Economic Statistics, forthcom… | 0.874 | 11 | 2 | 100% |
| 3 | Geskus (2020) Competing risks: Aims and methods, Handbook of Statistics pp. 249–87 | 0.644 | 2 | 2 | 100% |
| 4 | Blanco, Chen, Flores \ Flores-Lagunes (2020) Bounds on average and quantile treatment effects on duration outcomes under censoring, selection, and noncompliance, Journal of… | 0.511 | 2 | 1 | 100% |
| 5 | Richardson, Hudgens, Fine \ Brookhart (2017) Nonparametric binary instrumental variable analysis of competing risks data, Biostatistics 18(1): 48–61 | 0.511 | 2 | 1 | 100% |
| 6 | Shapiro (1997) Periodic screening for breast cancer: the hip randomized controlled trial, JNCI Monographs 1997(22): 27–30 | 0.511 | 2 | 1 | 100% |
| 7 | Wüthrich (2020) A comparison of two quantile models with endogeneity, Journal of Business & Economic Statistics 38(2): 443–456 | 0.511 | 2 | 1 | 100% |
| 8 | Abbring \ Van den Berg (2003) The nonparametric identification of treatment effects in duration models, Econometrica 71(5): 1491–1517 | 0.405 | 1 | 1 | 100% |
| 9 | Abbring \ Van den Berg (2005) Social experiments and instrumental variables with duration outcomes, Technical Report | 0.405 | 1 | 1 | 100% |
| 10 | Bijwaard \ Ridder (2005) Correcting for selective compliance in a re-employment bonus experiment, Journal of Econometrics 125(1-2): 77–111 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 34 scored citations.