  We propose and study a maximum likelihood estimator of stochastic frontier
models with endogeneity in cross-section data when the composite error term may
be correlated with inputs and environmental variables. Our framework is a
generalization of the normal half-normal stochastic frontier model with
endogeneity. We derive the likelihood function in closed form using three
fundamental assumptions: the existence of control functions that fully capture
the dependence between regressors and unobservables; the conditional
independence of the two error components given the control functions; and the
conditional distribution of the stochastic inefficiency term given the control
functions being a folded normal distribution. We also provide a Battese-Coelli
estimator of technical efficiency. Our estimator is computationally fast and
easy to implement. We study some of its asymptotic properties, and we showcase
its finite sample behavior in Monte-Carlo simulations and an empirical
application to farmers in Nepal.
