Bootstrapping non-parametric models is a fairly complicated exercise which is associated with implicit assumptions or requirements that are not always obvious to the non-expert user. Bootstrap DEA is a significant development of the past decade; however, some of its assumptions and properties are still quite unclear, which may lead to mistakes in implementation and hypothesis testing. This paper clarifies these issues and proposes a hypothesis testing procedure, along with its limitations, which could be extended to test almost any hypothesis in bootstrap DEA. Moreover, it enhances the intuition behind bootstrap DEA and highlights logical and theoretical pitfalls that should be avoided.
Data Envelopment Analysis Efficiency Bootstrap Bootstrap DEA Hypothesis Testing