Lorenzo Tedesco, Jad Beyhum, Ingrid Van Keilegom
arXiv 5 Sep 2023 · Econometrics · publishedElectronic Journal of Statistics (2025) · 1 citations (OpenAlex)
arXiv:2309.02183 · PDF · DOI · OpenAlex · Extracted main text
We consider instrumental variable estimation of the proportional hazards model of Cox (1972). The instrument and the endogenous variable are discrete but there can be (possibly continuous) exogenous covariables. By making a rank invariance assumption, we can reformulate the proportional hazards model into a semiparametric version of the instrumental variable quantile regression model of Chernozhukov and Hansen (2005). A na\"ive estimation approach based on conditional moment conditions generated by the model would lead to a highly nonconvex and nonsmooth objective function. To overcome this problem, we propose a new presmoothing methodology. First, we estimate the model nonparametrically - and show that this nonparametric estimator has a closed-form solution in the leading case of interest of randomized experiments with one-sided noncompliance. Second, we use the nonparametric estimator to generate “proxy” observations for which exogeneity holds. Third, we apply the usual partial likelihood estimator to the “proxy” data. While the paper focuses on the proportional hazards model, our presmoothing approach could be applied to estimate other semiparametric formulations of the instrumental variable quantile regression model. Our estimation procedure allows for random right-censoring. We show asymptotic normality of the resulting estimator. The approach is illustrated via simulation studies and an empirical application to the Illinois
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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. and Hansen, C (2005) An IV model of quantile treatment effects | 1.000 | 11 | 4 | 100% |
| 2 | Cox, D. R (1972) Regression models and life tables | 0.928 | 4 | 3 | 100% |
| 3 | Beyhum, J., Florens, J.-P., and Van Keilegom, I (2022) Nonparametric instrumental regression with right censored duration outcomes self | 0.737 | 4 | 3 | 50% |
| 4 | Beran, R (1981) Nonparametric regression with randomly censored survival data. Technical report, Univ. California, Berkeley | 0.737 | 3 | 2 | 100% |
| 5 | Tsiatis, A. A (1981) A large sample study of Cox's regression model | 0.511 | 2 | 2 | 50% |
| 6 | Chernozhukov, V. and Hansen, C (2006) Instrumental quantile regression inference for structural and treatment effect models | 0.511 | 2 | 1 | 100% |
| 7 | Akritas, M. G (1996) On the use of nonparametric regression techniques for fitting parametric regression models | 0.405 | 1 | 1 | 100% |
| 8 | Angrist, J. D., Imbens, G. W., and Rubin, D. B (1996) Identification of causal effects using instrumental variables | 0.405 | 1 | 1 | 100% |
| 9 | Beyhum, J., Tedesco, L., and Van Keilegom, I. (2023+b) (2023) Instrumental variable quantile regression under random right censoring self | 0.405 | 1 | 1 | 100% |
| 10 | Beyhum, J., Centorrino, S., Florens, J.-P., and Van Keilegom, I (2023) Instrumental variable estimation of dynamic treatment effects on a duration outcome self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 33 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimation of the complier causal hazard ratio under dependent censoring | 0.405 | 1 | 1 |
| 2 | Tests of exogeneity in duration models with censored data | 0.405 | 1 | 1 |