Nonparametric estimation of concave production technologies by entropic methods
Michael Beenstock (),
Ury Passy and
Alexander Shapiro ()
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Gad Allon: Kellogg School of Management, Northwestern University, USA, Postal: Kellogg School of Management, Northwestern University, USA
Steven Hackman: School of Industrial and Systems Engineering, Georgia Institute of Technology Atlanta, GA USA, Postal: School of Industrial and Systems Engineering, Georgia Institute of Technology Atlanta, GA USA
Ury Passy: Faculty of Industrial Engineering and Management Technion-Israel Institute of Technology, Haifa, Israel, Postal: Faculty of Industrial Engineering and Management Technion-Israel Institute of Technology, Haifa, Israel
Journal of Applied Econometrics, 2007, vol. 22, issue 4, 795-816
An econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, based on the principle of maximum likelihood, uses entropic distance and convex programming techniques to estimate production functions. Empirical applications are presented to demonstrate the feasibility of the methodology in small and large datasets. Copyright © 2007 John Wiley & Sons, Ltd.
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Working Paper: Nonparametric estimation of concave production technologies by entropic methods (2005)
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