Before revision:  We also present a related method that allows the researcher to assess the sensitivity of their NPIV estimates to misspecification. This sensitivity analysis can inform the choice of sieve space in point estimation .
After revision:  We also present a related method that allows the researcher to make meaningful inferences about the structural function when misspecification cannot ruled out. The method is easy to implement and computationally light. The first stage resembles that of sieve minimum-distance estimation, and the second stage requires numerical solution of a linear programming problem. In contrast to existing methods our second stage does not require that the researcher choose any penalty function, penalty parameter nor a particular dimension of a sieve space. To compare our method to existing approaches we present an empirical application based on Horowitz (2011) .