  We consider a nonparametric regression model with continuous endogenous
independent variables when only discrete instruments are available that are
independent of the error term. While this framework is very relevant for
applied research, its implementation is cumbersome, as the regression function
becomes the solution to a nonlinear integral equation. We propose a simple
iterative procedure to estimate such models and showcase some of its asymptotic
properties. In a simulation experiment, we discuss the details of its
implementation in the case when the instrumental variable is binary. We
conclude with an empirical application in which we examine the effect of
pollution on house prices in a short panel of U.S. counties.
