Nonparametric estimation of concave production technologies by entropic methods

Gad Allon, Michael Beenstock, Steven Hackman, 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
Michael Beenstock: Department of Economics, Hebrew University of Jerusalem, Jerusalem, Israel, Postal: Department of Economics, Hebrew University of Jerusalem, Jerusalem, Israel
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, pages 795-816

Abstract: 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.

Date: 2007
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