Monte Carlo simulations of DEA efficiency measures and hypothesis tests
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TL;DR
No new tests are suggested, but results on bias in simulations of nested small sample DEA models are presented, and the approximating powers of suggested tests under various specifications of scale and omitted variables are examined.
Abstract
The statistical properties of the efficiency estimators based on Data Envelopment Analysis (DEA) are largely unknown. Recent work by Simar et al. and Banker has shown the consistency of the DEA estimators under specific assumptions, and Banker proposes asymptotic tests of whether two subsamples have the same efficiency distribution. There are difficulties arising from bias in small samples and lack of independence in nested models. This paper suggest no new tests, but presents results on bias in simulations of nested small sample DEA models, and examines the approximating powers of suggested tests under various specifications of scale and omitted variables.