Open Access
June 2010 Asymptotic distribution of conical-hull estimators of directional edges
Byeong U. Park, Seok-Oh Jeong, Léopold Simar
Ann. Statist. 38(3): 1320-1340 (June 2010). DOI: 10.1214/09-AOS746


Nonparametric data envelopment analysis (DEA) estimators have been widely applied in analysis of productive efficiency. Typically they are defined in terms of convex-hulls of the observed combinations of inputs×outputs in a sample of enterprises. The shape of the convex-hull relies on a hypothesis on the shape of the technology, defined as the boundary of the set of technically attainable points in the inputs × outputs space. So far, only the statistical properties of the smallest convex polyhedron enveloping the data points has been considered which corresponds to a situation where the technology presents variable returns-to-scale (VRS). This paper analyzes the case where the most common constant returns-to-scale (CRS) hypothesis is assumed. Here the DEA is defined as the smallest conical-hull with vertex at the origin enveloping the cloud of observed points. In this paper we determine the asymptotic properties of this estimator, showing that the rate of convergence is better than for the VRS estimator. We derive also its asymptotic sampling distribution with a practical way to simulate it. This allows to define a bias-corrected estimator and to build confidence intervals for the frontier. We compare in a simulated example the bias-corrected estimator with the original conical-hull estimator and show its superiority in terms of median squared error.


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Byeong U. Park. Seok-Oh Jeong. Léopold Simar. "Asymptotic distribution of conical-hull estimators of directional edges." Ann. Statist. 38 (3) 1320 - 1340, June 2010.


Published: June 2010
First available in Project Euclid: 8 March 2010

zbMATH: 1189.62055
MathSciNet: MR2662344
Digital Object Identifier: 10.1214/09-AOS746

Primary: 62G05
Secondary: 62G20

Keywords: asymptotic distribution , Conical-hull , constant returns-to-scale , CRS , data envelopment analysis , DEA , efficiency

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.38 • No. 3 • June 2010
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