This paper develops a non-linear stochastic frontier (SF) model. The main advantage of this model over standard SF models is that the model does not need an assumption for the distributional form of the inefficiency component. Another advantage of the developed model is that it can be used to...

Stochastic frontier models all need an assumption on the distributional form of the efficiency component. Generally this efficiency component is assumed to be half normally, truncated normally, or exponentially distributed. This paper shows that the exponential distribution is, just like the...

In this paper, we consider the stochastic ray production function that has been revived recently by Henningsen et al. (2017). We use a profit-maximizing framework to resolve endogeneity problems that are likely to arise, as in all distance functions, and we derive the system of equations after...

Heckman's (1979) sample selection model has been employed in three decades of applications of linear regression studies. The formal extension of the method to nonlinear models, however, is of more recent vintage. A generic solution for nonlinear models is proposed in Terza (1998). We have...

The field of production frontier estimation is divided between the parametric Stochastic Frontier Analysis (SFA) and the deterministic, nonparametric Data Envelopment Analysis (DEA). This paper explores an amalgam of DEA and SFA that melds a nonparametric frontier with a stochastic composite...