Nonparametric Instrumental Regression with Right Censored Duration Outcomes

Jad Beyhum, Jean-Pierre Florens and Ingrid Van Keilegom

No 20-1164, TSE Working Papers from Toulouse School of Economics (TSE)

Abstract: This paper analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confoundingness 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.

Keywords: Duration Models; Endogeneity; Instrumental variable; Nonseparability; Partial identification (search for similar items in EconPapers)
Date: 2020-11-20
New Economics Papers: this item is included in nep-ecm and nep-ore
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