Article ID: | iaor19961857 |
Country: | Netherlands |
Volume: | 67 |
Issue: | 3 |
Start Page Number: | 332 |
End Page Number: | 343 |
Publication Date: | Jun 1993 |
Journal: | European Journal of Operational Research |
Authors: | Banker Rajiv D., Gorr Wilpen L., Gadh Vandana M. |
Keywords: | statistics: data envelopment analysis |
This paper reports the results of an experiment with simulated data that compares the estimation accuracy of two simple and very different production frontier methods: corrected ordinary least squares and data envelopment analysis. The experimental design extends a previous published paper by introducing measurement errors, a factor the authors show to be critical for comparative analysis of the frontier methods. Both low and high measurement error distributions are used, resulting in 95% error intervals of roughly ¸±10% and 40%, respectively, of outputs. Other variations include four inefficiency distributions covering a wide range of behavior; four sample sizes, from 25 to 200, and two piecewise Cobb-Doublas technologies with two inputs and one output each. Results include: 1) selection of the proper estimation method for a case can result in substantial gains in estimation accuracy for technical efficiencies, from 15 to 40% in mean absolute deviations; 2) COLS perfroms better for the classical distribution case with sample sizes of 50 or over; 3) DEA performs better for all nonclassical inefficiency distributions, even with relatively high measurement errors; 4) DEA provides surprisingly accurate estimates for the small sample size of 25, for all cases in the experiment; 5) COLS fails to decompose deviations into efficiency and measurement error components (it assumes that deviations from the frontier are either totally due to measurement errors or technical inefficiencies); and 6) neigher method perfroms satisfactorally for high measurement errors.