Estimating a Semiparametric Spatial Autoregressive Stochastic Frontier Model

Authors: Kien Tran

Abstract: This paper proposes a semiparametric spatial autoregressive stochastic frontier model, where the functional form of the frontier is modeled nonparametrically. A three-step estimation procedure is considered where in the first two steps, a constrained semiparametric profile GMM is used to obtain the estimates of the spatial parameter and the unknown smooth function of the frontier; whilst in the final step, MLE is used to obtain the remaining parameters of the model. We derive the limiting distributions of the proposed estimators for both parametric and nonparametric components in the model. Monte Carlo simulations reveal that our proposed estimators perform well in finite samples.