Testing Hypotheses in Nonparametric Models of Production

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiencies of production units. In applications, practitioners use DEA estimators far more frequently than FDH estimators, and thereby assume, at least implicitly, that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. While several bootstrap methods have been developed for making inference about the eciencies of individual units, to date no methods have existed for making consistent inference about differences in mean efficiency across groups of producers or for testing hypotheses about model structure such as returns to scale or convexity of the production set. This paper builds on central limit theorem results of Kneip et al. (2013) to develop additional theoretical results permitting consistent tests of model structure. Monte Carlo results illustrating the performance of the tests in terms of size and power are also presented. In addition, the variable returns to scale version of the DEA estimator is proved to attain the faster convergence rate of the CRS-DEA estimator under CRS