Data Envelopment Analysis Journal > Vol 2 > Issue 1

Nonparametric Estimation of Production Functions

Trevor Collier, University of Dayton, School of Business, USA, Kelli Marquardt, University of Dayton, School of Business, USA, John Ruggiero, University of Dayton, School of Business, USA, jruggiero1@udayton.edu
 
Suggested Citation
Trevor Collier, Kelli Marquardt and John Ruggiero (2016), "Nonparametric Estimation of Production Functions", Data Envelopment Analysis Journal: Vol. 2: No. 1, pp 35-52. http://dx.doi.org/10.1561/103.00000013

Published: 26 Oct 2016
© 2016 T. Collier, K. Marquardt and J. Ruggiero
 
Subjects
Econometric models: Model choice and specification analysis
 
Keywords
DEAproduction frontiersproduction functions
 

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In this article:
1. Introduction
2. Sports Production Function
3. Alternative Estimators
4. Conclusions
References

Abstract

While the primary use of data envelopment analysis is the estimation of production frontiers and the subsequent measurement of efficiency, a more recent literature has been concerned with the estimation of production functions that allow observed points beyond the frontier. This could arise with noisy data for example. Banker and Maindiratta (1992) provided a foundation by extending DEA to estimate efficiency in the presence of statistical noise. The programming model estimates the frontier via maximum likelihood while constraining the production set to be convex by imposing the celebrated Afriat conditions. Since then, there have been several alternative models that have been developed. In this paper we apply several competing methodologies to estimate production functions using data from the English Premier League from 2009 to 2010.

DOI:10.1561/103.00000013