Mishra, SK (2007): Least squares estimation of joint production functions by the Differential Evolution method of global optimization.
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Abstract
In the economics of joint production one often distinguishes between the two cases: the one in which a firm produces multiple products each produced under separate production process, and the other “true joint production” where a number of outputs are produced from a single production process, where each product shares common inputs. In the econometric practice the first case has often been dealt with by aggregation of individual production functions into a macro production function. The second case has often called for estimation of an implicit aggregate production function.
Most of the studies relating to estimation of joint production functions have noted two difficulties: first that allocation of inputs to different outputs are not known, and the second that a method of estimation (such as the Least Squares) cannot have more than one dependent variable. Construction of a composite (macro) output function is at least partly motivated by the inability of the estimation methods to deal with multiple dependent variables and different forms of production function for different outputs.
This study has conducted some simulation experiments on joint estimation of the CES, the Transcendental and the NerloveRingstad functions. Allocation parameters (of inputs) across the products have been introduced. Estimation has been done jointly, but without constructing a composite macro production function or an output transformation function. We use nonlinear least squares based on the Differential Evolution method of global optimization that permits fitting multiple production functions simultaneously.
Item Type:  MPRA Paper 

Institution:  NorthEastern Hill University, Shillong (India) 
Original Title:  Least squares estimation of joint production functions by the Differential Evolution method of global optimization 
Language:  English 
Keywords:  Joint production; multiple output; allocation parameters; nonlinear least squares; Differential Evolution; NerloveRingstad; Transcendental; CES; macro; implicit; composite production function; transformation function; canonical correlation; multiple dependent variables 
Subjects:  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 > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General D  Microeconomics > D2  Production and Organizations > D24  Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity 
Item ID:  4877 
Depositing User:  Sudhanshu Kumar Mishra 
Date Deposited:  14 Sep 2007 
Last Modified:  28 Sep 2019 18:19 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/4877 
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Least squares estimation of joint production functions by the Differential Evolution method of global optimization. (deposited 12 Sep 2007)
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