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Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models

Institut de Statistique and CORE, Université Catholique de Louvain, Voie du Roman Pays, 20, Louvain-la-Neuve, Belgium
Department of Economics, University of Texas at Austin, Austin, Texas 78712

Efficiency scores of production units are generally measured relative to an estimated production frontier. Nonparametric estimators (DEA, FDH, ) are based on a finite sample of observed production units. The bootstrap is one easy way to analyze the sensitivity of efficiency scores relative to the sampling variations of the estimated frontier. The main point in order to validate the bootstrap is to define a reasonable data-generating process in this complex framework and to propose a reasonable estimator of it. This paper provides a general methodology of bootstrapping in nonparametric frontier models. Some adapted methods are illustrated in analyzing the bootstrap sampling variations of input efficiency measures of electricity plants.

Key Words: Data Envelopment Analysis; Bootstrap; Resampling Methods; Frontier Efficiency Models

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