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Abstract

Arnold Zellner and Nagesh Revankar in their well-known paper -Generalized Production Functions- The Review of Economic Studies, 362, pp. 241-250, 1969 introduced a new generalized production function, which was illustrated by an example of fitting the generalized Cobb-Douglas function to the U.S. data for Transportation Equipment Industry. For estimating the parameters of their production function, they used a method in which one of the parameters theta is chosen at the trial basis and other parameters relating to elasticity and returns to scale are estimated so as to maximize the likelihood function. Repeated trials are made with different values of theta so as to obtain the global maximum of the likelihood function.

In this paper we show that the method suggested and used by Zellner and Revankar ZR may easily be caught into a local optimum trap. We also show that the estimated parameters reported by them are grossly sub-optimal.

Using the Differential Evolution DE and the Repulsive Particle Swarm RPS methods of global optimization, the present paper re-estimates the parameters of the ZR production function with the U.S. data used by ZR. We find that the DE and the RPS estimates of parameters are significantly different from but much better than those estimated by ZR. We also find that the returns to scale do not vary with the size of output as reported by ZR.



Item Type: MPRA Paper -

Institution: North-Eastern Hill University, Shillong India-

Original Title: Estimation of Zellner-Revankar Production Function Revisited-

Language: English-

Keywords: Zellner-Revankar production function; maximum likelihood; global optimization; Repulsive Particle Swarm; Differential Evolution; U.S. Data; Transport Equipment Industry; variable Returns to scale; sub-optimality-

Subjects: D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; CapacityC - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic AnalysisC - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation ModelingC - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C31 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions ; Social Interaction Models-





Autor: Mishra, SK

Fuente: https://mpra.ub.uni-muenchen.de/1172/







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