Kernel-based estimation of semiparametric regression in triangular systems

We propose a kernel-based estimator for a partially linear model in triangular systems where endogenous variables appear both in the nonparametric and linear component functions. Our estimator is easy to implement, has an explicit algebraic structure, and exhibits good finite sample performance in a Monte Carlo study.