Tid: 17 februari 2021, kl. 13-14
Plats: This seminar is given online. E-mail Dan Hedlin if you want to attend.

Abstract

The practical value of Stochastic Frontier Analysis (SFA) is positively related to the level of accuracy at which it can estimate unit-specific inefficiencies. Conventional SFA unit inefficiency estimation is based on the mean/mode of the inefficiency, conditioned on the estimated composite error. This approach shrinks the inefficiency towards its mean/mode, which generates a distribution that is different from the distribution of the unconditional inefficiency; thus, the accuracy of the estimated inefficiency is negatively correlated with the distance the inefficiency is located from its mean/mode. We propose restricted estimators based on Bayesian risk (expected loss) that restrict the inefficiencies to satisfy the underlying theoretical mean and variation assumptions. We analytically investigate some properties of the maximum a posteriori probability estimator under mild assumptions and derive a restricted conditional mode estimator for three different inefficiency densities commonly used in SFA applications. Extensive simulations show that, under common empirical situations, e.g., regarding sample sizes, inefficiency distributions and signal-to-noise ratios, this estimator outperforms the conventional approach when the inefficiency is greater than its mean/mode. With real data from the Swedish electricity distribution sector, we demonstrate that the conventional and our restricted estimators give substantially different results when the inefficiency is high.