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

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 sa...

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Bibliographic Details
Published in:Management science 1998-01, Vol.44 (1), p.49-61
Main Authors: Simar, Leopold, Wilson, Paul W
Format: Article
Language:English
Subjects:
Analytical estimating
Applied sciences
Bootstrap
Bootstrap resampling
Confidence interval
Consistent estimators
Data collection
Data Envelopment Analysis
Density estimation
Economic analysis
Economic efficiency
Efficiency
Efficiency metrics
Energy
Energy economics
Estimation
Estimators
Exact sciences and technology
Frontier Efficiency Models
General, economic and professional studies
Management science
Mathematical models
Methodology. Modelling
Nonparametric models
Production
Production efficiency
Resampling Methods
Sensitivity analysis
Studies
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Summary: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.
ISSN:0025-1909
1526-5501
DOI:10.1287/mnsc.44.1.49