Nonparametric instrumental regression with right censored duration outcomes

TL;DR

This paper develops a nonparametric instrumental variable approach for estimating treatment effects on duration outcomes with right censoring, providing identification, estimation procedures, and empirical application.

Contribution

It introduces a novel nonparametric framework for censored duration data with instrumental variables, including identification, estimation, and partial identification results.

Findings

Estimation procedure with convergence rates

Asymptotic normality under certain conditions

Successful application to Illinois Reemployment Bonus Experiment

Abstract

This paper analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental variable explaining the treatment and independent of the error term of the model. Our framework is nonparametric and allows for random right censoring. This specification generates a nonlinear inverse problem and the average treatment effect is derived from its solution. We provide local and global identification properties that rely on a nonlinear system of equations. We propose an estimation procedure to solve this system and derive rates of convergence and conditions under which the estimator is asymptotically normal. When censoring makes identification fail, we develop partial identification results. Our estimators exhibit good finite sample properties in simulations. We also apply our methodology to the Illinois Reemployment Bonus Experiment.