Abstract
Distance, revenue, cost and profit functions can always be written in the form of regression models with unobserved error terms representing statistical noise and different types of inefficiency. In practice, the noise components are almost always assumed to be random variables (i.e., stochastic). The associated frontiers are known as stochastic frontiers. This chapter explains how to estimate and draw inferences concerning the unknown parameters in so-called stochastic frontier models (SFMs). It then explains how the estimated parameters can be used to predict levels of efficiency and analyse productivity change. The focus is on maximum likelihood estimators and predictors.
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