Instrumental variable estimation of the proportional hazards model by presmoothing

TL;DR

This paper introduces a presmoothing method for instrumental variable estimation of the proportional hazards model, enabling consistent estimation in the presence of endogenous covariates and censoring.

Contribution

It proposes a novel presmoothing approach that simplifies estimation of the semiparametric instrumental variable proportional hazards model, applicable to censored data.

Findings

Estimator is asymptotically normal.

Method performs well in simulations.

Applicable to empirical data with censoring.

Abstract

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