Mishra, SK (2006): A Note on Numerical Estimation of Sato’s TwoLevel CES Production Function.

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Abstract
In this paper Sato’s twolevel CES production function has been estimated by nonlinear regression carried out through five different methods of optimization, namely, the HookeJeeves Pattern Moves (HJPM), the HookeJeevesQuasiNewton (HJQN), the RosenbrockQuasiNewton (RQN), the Differential Evolution (DE) and the Repulsive Particle Swarm methods (RPS). The last two methods are particularly suited to optimization of extremely nonlinear (often multimodal) objective functions.
While data may be containing outliers, the method of least squares has a clear disadvantage as it may be pulled by extremely small or large errors. The absolute deviation estimation of parameters is more suitable in such cases. This paper has made an attempt to estimation of parameters of Sato’s twolevel CES production function by minimizing the sum of absolute errors. The minimization has been done by the five methods noted above. While the HJPM and the HJQN perform poorly at minimizing the sum of absolute deviations, the RQN performs much better. The DE and the RPS perform very well in estimating the parameters.As an exercise on real data, the German Sector "MerketDetermined Services" production function has been estimated with three inputs: Capital, Labour and Energy. The Linear Exponential (LINEX) and Sato's twolevel specifications of the "Service Function" have been estimated.
Item Type:  MPRA Paper 

Institution:  NorthEastern Hill University, Shillong (India) 
Original Title:  A Note on Numerical Estimation of Sato’s TwoLevel CES Production Function 
Language:  English 
Keywords:  Sato’s productions function; CES; constant elasticity of substitution; twolevel; nonlinear regression; Hooke Jeeves; QuasiNewton; Rosenbrock; Repulsive Particle swarm; Differential Evolution; Global Optimization; Econometrics; Estimation; Outliers; Least absolute deviation; error; German Sector MarketDetermined Services; Service Production function; LINEX; Linear Exponential specification 
Subjects:  D  Microeconomics > D2  Production and Organizations > D20  General C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C20  General 
Item ID:  1019 
Depositing User:  Sudhanshu Kumar Mishra 
Date Deposited:  04 Dec 2006 
Last Modified:  27 Sep 2019 07:34 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/1019 