Paper Info
Title
A simulation study of DEA and parametric frontier models in the presence of heteroscedasticity
Abstract
This paper studies the effects of heteroscedasticity on the following five types of estimators: (1) Data Envelopment Analysis (DEA) per se as well as DEA joined to regression forms, (2) Corrected Ordinary Least Squares based on maximum residual (COLS-R), (3) Corrected Ordinary Least Squares based on moments of residuals (COLS-M), (4) Maximum Likelihood Estimation (MLE), and (5) Goal Programming with one-sided deviations as in Aigner and Chu (A&C). This is accomplished with simulated data in an experiment designed around a single output–single input production function which is piecewise Cobb–Douglas. Robustness of results is confirmed with another experiment employing a shifted smooth Cobb–Douglas production function. The model has a composed error term consisting of two components––one for measurement error and the other for inefficiency. The simulation results indicate that heteroscedasticity does not have an adverse impact on DEA-based estimators and that DEA-based estimators are the best estimators of efficient output even under heteroscedasticity.
Year
DOI
Venue
2004
10.1016/S0377-2217(02)00699-9
European Journal of Operational Research
Keywords
Field
DocType
Production function,Inefficiency estimators,Heteroscedasticity,Simulation study
Econometrics,Residual,Mathematical optimization,Heteroscedasticity,Ordinary least squares,Parametric statistics,Data envelopment analysis,Statistics,Observational error,Piecewise,Mathematics,Estimator
Journal
Volume
Issue
ISSN
153
3
0377-2217
Citations 
PageRank 
References 
4
3.06
2
Authors
3
Name
Order
Citations
PageRank
Rajiv D. Banker11826234.81
Hsihui Chang2387.47
William W. Cooper330242.76