Endogenous environmental variables in stochastic frontier models

Introducing environmental variables in nonparametric frontier models: A probabilistic approach

This paper proposes a general formulation of a nonparametric frontier model introducing external environmental factors that might influence the production process but are neither inputs nor outputs under the control of the producer. A representation is proposed in terms of a probabilistic model which defines the data generating process. Our approach extends the basic ideas from Cazals, Florens ...

Stochastic Frontier Models

Endogeneity in Stochastic Frontier Models

Stochastic frontier models are typically estimated by maximum likelihood (MLE) orcorrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that ...

Spatial-temporal Stochastic Frontier Models

The stochastic frontier model with heterogeneous technical efficiency explained by exogenous variables is augmented with a sparse spatial autoregressive component for a crosssection data, and a spatial-temporal component for a panel data. An estimation procedure that takes advantage of the additivity of the model is proposed, computational advantages over simultaneous maximum likelihood estimat...

Discrete Endogenous Variables in Weakly Separable Models

This paper contains an extension of the identification method proposed in Jun, Pinkse, and Xu (2009, JPX), which is based on a generated collection of sets, i.e. a Dynkin system. We demonstrate the usefulness of this extension in the context of the model proposed by Vytlacil and Yıldız (2007, VY). VY formulate a fully nonparametric model featuring a nested weakly separable structure in which an...