Lunchseminar in Economics: Instrumental Variable Quantile Regression under Random Right Censoring

Supported by the Luxembourg National Research Fund (FNR) 17539924

Abstract

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.

Ingrid van Keilegom

Ingrid van Keilegom is Professor of Statistics at the KU Leuven in Belgium since 2016. She received a B.S. degree in mathematics (1993) from the Universiteit Antwerpen, and a master in biostatistics and a PhD in statistics (both in 1998) from the Universiteit Hasselt. She held positions as professor at the Pennsylvania State University (1998-1999), Eindhoven University of Technology (1999-2000), and Université catholique de Louvain (2000-2016). Dr. Van Keilegom’s main research areas are survival analysis, causal inference, measurement errors, quantile regression, non- and semiparametric regression, and instrumental variables.

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