When: 17 february 2021, 13-14
Where: This seminar is given online. E-mail Dan Hedlin if you want to attend.


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.