Nonlinear autoregressive stochastic frontier model with dynamic technical inefficiency in panel data

Abstract

In this paper, we focused on the new stochastic frontier model with the nonlinear autoregressive structure in panel data. In classic finance models, uncorrelated errors are considered, which are frequently not met in empirical situations. In our stochastic frontier model, the composite error consists of two components, the statistical error and technical inefficiency, whereas the technical inefficiency is assumed to be autocorrelated. A semiparametric method is suggested to estimate nonlinear autoregressive function by two steps procedure as parametric Taylor series expansion and nonparametric adjustment factor. For the model parameters estimation, the Expectation-Maximization approach is applied and the performance of the estimation is checked by Monte Carlo simulation. Finally, we investigate the applicability of new model using a real data set.