Analyzing Inefficiency Using a Frontier Search Approach
Efficiency measurement naturally requires the definition of a frontier as a benchmark indicating efficiency. Usually a measure reflecting the distance of a data point to the frontier indicates the level of efficiency. One of the crucial characteristics to distinguish efficiency measurement tools is the way in which they construct the frontier. The class of deterministic and non parametric tools of constructing the frontier mainly comprises of tools associated with Data Envelopment Analysis. Coming in various flavors all DEA frontiers suffer of their piecewise construction giving rise to numerous vertices. Those vertices do not allow convenient analysis of the frontier properties such as computing elasticities and the like. In this paper we want to contribute to the class of deterministic and non parametric tools of constructing the frontier in an one output and n input setting. We suggest a new empirical approach drawing on functional search in the fashion of Koza's (1992) genetic programming. The frontier search algorithm employed evolves the functional form of the frontier and the parameters simultaneously. The frontier exhibits the neat property that it is smooth and differentiable enabling the computation of elasticities,for example. In particular we introduce both the idea and the algorithm of the frontier search procedure. We discuss the advantages and shortcomings with respect to empirical problems. The arguments brought forth in the preceding sections are illustrated by the investigation of an artificial example.
|Date of creation:||Dec 2000|
|Contact details of provider:|| Postal: Universitaetsstrasse 16, D-86159 Augsburg, Germany|
Phone: +49 821 598 4060
Fax: +49 821 598 4217
Web page: http://www.wiwi.uni-augsburg.de/vwl/institut
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Fare,Rolf & Grosskopf,Shawna & Lovell,C. A. Knox, 2008.
Cambridge University Press, number 9780521072069, November.
- Fare,Rolf & Grosskopf,Shawna & Lovell,C. A. Knox, 1993. "Production Frontiers," Cambridge Books, Cambridge University Press, number 9780521420334, November.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
- Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
- Schmertmann, Carl P, 1996. "Functional Search in Economics Using Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 9(4), pages 275-298, November. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:aug:augsbe:0199. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dr. Albrecht Bossert)
If references are entirely missing, you can add them using this form.