Inference in stochastic frontier analysis with dependent error terms

Abstract

Stochastic frontier analysis (SFA) is often used to estimate technical efficiency of entities such as firms, countries or municipalities. A potential dependence between the two components of the error term can be taken into account by a copula function. While estimation of the model is straightforward using the Corrected Ordinary Least Squares (COLS) and Maximum Likelihood (ML) methods, an open issue concerns the inference of the technical efficiencies. We propose a parametric bootstrap algorithm which is suitable for the dependence case. This allows us to estimate the efficiency percentile confidence intervals. We apply the model to the estimation of technical efficiencies of moroccan municipalities.