A Nonlinear Autoregressive Stochastic Frontier Model with Dynamic Technical Inefficiency in Panel Data

A branch of researches is devoted to semiparametric and nonparametric estimation of stochastic frontier models to employ the advantages in the operations research technique of data envelopment analysis. The stochastic frontier model is the parametric competition of data envelopment technique. This paper focused on a nonlinear autoregressive stochastic frontier production model that covers dynamic technical inefficiency. We consider a semiparametric method for the model ‎by combining a parametric regression estimator with a nonparametric adjustment‎. The unknown parameters are estimated using the full maximum likelihood and pairwise composite likelihood methods‎. After the parameters are estimated by parametric methods‎, ‎the obtained regression function is adjusted by a nonparametric factor‎, ‎and the nonparametric factor is obtained through a natural consideration of the local -fitting criterion‎. ‎Some asymptotic and simulation results for the semiparametric method are discussed‎.