RT Monograph
SR 00
A1 Beyhum, Jad
A1 Florens, Jean-Pierre
A1 Van Keilegom, Ingrid
T1 Nonparametric Instrumental Regression with Right Censored Duration Outcomes
YR 2020
FD 2020-11
VO 20-1164
SP 36
K1 Duration Models
K1 Endogeneity
K1 Instrumental variable
K1 Nonseparability
K1 Partial identification
AB 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.
T2 TSE Working Paper
PB TSE Working Paper
PP Toulouse
AV Published
LK https://publications.ut-capitole.fr/id/eprint/41947/
UL http://tse-fr.eu/pub/124931