Calendrier du 11 janvier 2023
Job Market Seminar
Du 11/01/2023 de 12:30 à 13:45
R2-21
BEYHUM Jad (ENSAI)
Instrumental variable quantile regression under random right censoring
écrit avec Lorenzo Tedesco
This paper studies a semiparametric quantile regression model
with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the
structural quantile of the logarithm of the outcome variable is linear
in the covariates and censoring is independent. The regressors and
instruments can be either continuous or discrete. The specification
generates a continuum of equations of which the quantile regression
coefficients are a solution. Identification is obtained when this system
of equations has a unique solution. Our estimation procedure solves
an empirical analogue of the system of equations. We derive conditions under which the estimator is asymptotically normal and prove
the validity of a bootstrap procedure for inference. The finite sample
performance of the approach is evaluated through numerical simulations. The method is illustrated by an application to the national Job
Training Partnership Act study