A spatial stochastic frontier model with endogenous frontier and environmental variables

We propose a spatial autoregressive stochastic frontier model, which allows for the endogeneity in both the frontier and environmental variables (i.e., endogeneity due to correlation of inefficiency term and two-sided error term). The model parameters are estimated using a single-stage control function approach. Monte Carlo simulations show that our proposed model and approach perform well in finite samples. We employed our methodology to the Chinese chemicals firm data and found evidence for both spatial effects and endogeneity. (C) 2020 Elsevier B.V. All rights reserved.