Statistical Inference in a Spatial-Temporal Stochastic Frontier Model

TL;DR

This paper introduces a spatial-temporal stochastic frontier model that accounts for dependencies over space and time, improving efficiency estimates and providing a testing procedure for model assumptions.

Contribution

It generalizes existing models by relaxing panel independence, offering a computationally efficient estimation method and a test for model assumptions.

Findings

Spatial-temporal component improves efficiency estimates

Additivity allows for computational advantages

Model assumption test facilitates estimation

Abstract

The stochastic frontier model with heterogeneous technical efficiency explained by exoge-nous variables is augmented with a spatial-temporal component, a generalization relaxing the panel independence assumption in a panel data. The estimation procedure takes advantage of additivity in the model, computational advantages over maximum likelihood estimation of parameters is exhibited. The spatial-temporal component can improve estimates of technical efficiency in a production frontier that is usually biased downwards. We present a test to veri-fy model assumptions that facilitates estimation of parameters.