Seminar: Improving Finite Sample Approximation by Central Limit Theorems for DEA and FDH efficiency scores
The School of Economics invites you to a seminar presented by Valentin Zelenyuk (University of Queensland).
Leopold Simar (Universite Catholique de Louvain)
We propose an improvement of the finite sample approximation of the central limit theorems (CLTs) that were recently derived for statistics involving production efficiency scores estimated via Data Envelopment Analysis (DEA) or Free Disposal Hull (FDH) approaches. The improvement is very easy to implement since it involves a simple correction of the variance estimator with an estimate of the bias of the already employed statistics without any additional computational burden and preserves the
original asymptotic results such as consistency and asymptotic normality. The proposed approach persistently showed improvement in all the scenarios that we tried in various Monte-Carlo experiments, especially for relatively small samples or relatively large dimensions (measured by total number of inputs and outputs) of the underlying production model. This approach therefore is expected to produce more accurate estimates of confidence intervals of aggregates of individual efficiency scores in empirical
research using DEA or FDH approaches and so must be valuable for practitioners. We
also illustrate this method using a popular real data set to confirm that the difference in
the estimated confidence intervals can be substantial. A step-by-step implementation
algorithm of the proposed approach is included in the Appendix.
It is a follow up and an improvement on the following paper:
which the attendees of the seminar might find useful to look through before the seminar to get the essence of the background.