Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Let H<sub>0</sub>(X) be a function that can be nonparametrically estimated. Suppose E [Y&7CX]=F<sub>0</sub>[X⊤β<sub>0</sub>, H<sub>0</sub>(X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector β<sub>0</sub> and unknown function F<sub>0</sub> are generally point identified without exclusion restrictions or instruments, in contrast to the usual assumption that identification without instruments requires fully specified functional forms. We propose an estimator with asymptotic properties allowing for data dependent bandwidths and random trimming. A Monte Carlo experiment and an empirical application to migration decisions are also included.