Estimation of stochastic production functions: the state of the art

E-mail: balazskotosz@gmail.com This article presents a comprehensive review of frontier studies for productivity analysis. The authors discuss the two main frontier approaches and highlight the reasons for selecting the parametric approach. The review also identifies the reason for considering unobserved heterogeneity when estimating firm performance. The classical stochastic frontier model is found to suffer from an empirical artefact in which the residuals of the production function may have positive skewness, contrary to the expected negative skewness which leads to estimated full efficiencies of all firms, as well as the possible problem of collinearity among inputs in the stochastic frontier model. By relaxing the hypotheses of random error symmetry and the independence of the components of the composite error, a sufficiently flexible re-specification of the stochastic frontier model can be achieved by decomposing the third moment of the composite error into three components that include the asymmetry of the inefficiency term, the asymmetry of the random error, and the dependence structure of the error components. Finally, instead of excluding insignificant variables from the model that can be of policy relevance, a principalcomponents-based solution can be adopted for collinearity in a stochastic frontier model.