A New Class of Production Function Model and Its Application

Maolin CHENG, Zedi JIANG

Journal of Systems Science and Information ›› 2016, Vol. 4 ›› Issue (2) : 177-185.

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Journal of Systems Science and Information ›› 2016, Vol. 4 ›› Issue (2) : 177-185. DOI: 10.21078/JSSI-2016-177-09
Article

A New Class of Production Function Model and Its Application

  • Maolin CHENG, Zedi JIANG
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Abstract

Under some circumstances, the studies on economic growth theory can be translated into the researches on production function which will beneficial for the government to analyze the pattern of economic growth and then make reasonable policies. The commonly used production functions include C-D production function, CES production function, VES production function with different elasticity of substitution. This paper will put forward to a new class of production function which elasticity of substitution σ is a non-linear function of K/L. With this new model, a calculation formula for accurately measure the influence rates of various factors to economic growth will be derived, which is significant for in-depth studies on functions and scientific measurement. The empirical analysis on the influence rates of China's economic growth factors and its good results will be presented in the end of this paper.

Key words

production function / elasticity of substitution / economic growth / influence rate of factors / empirical analysis

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Maolin CHENG, Zedi JIANG. A New Class of Production Function Model and Its Application. Journal of Systems Science and Information, 2016, 4(2): 177-185 https://doi.org/10.21078/JSSI-2016-177-09

References

[1] Avvakumov S N, Kiselev Yu N, Orlov M V, et al. Profit maximization problem for Cobb-Douglas and CES production functions. Computational Mathematics and Modeling, 2010, 21(3):336-378.
[2] Choi E J, Moon H R, Ridder G. Estimation of an education production function under random assignment with selection. American Economic Review, 2014, 104(5):206-211.
[3] Epple D, Gordon B, Sieg H. A new approach to estimating the production function for housing. American Economic Review, 2010, 100(3):905-924.
[4] Fayissa B, Gutema P. Estimating a health production function for Sub-Saharan Africa (SSA). Applied Economics, 2005, 37(2):155-164.
[5] Kuosmanen T, Pemsl D, Wesseler J. Specification and estimation of production functions involving damage control inputs:A two-stage, semiparametric approach. American Journal of Agricultural Economics, 2006, 88(2):499-511.
[6] Levinsohn J, Petrin A. Estimating production functions using inputs to control for unobservables. The Review of Economic Studies, 2003, 70(243):317-341.
[7] Mundlak Y. Production function estimation:Reviving the primal. Econometrica, 1996, 64(4):31-38.
[8] Zhengfei G, Lansink A O, van Ittersum M, et al. Integrating agronomic principles into production function specification:A dichotomy of growth inputs and facilitating inputs. American Journal of Agricultural Economics, 2006, 88(1):203-214.
[9] Cheng M L. China forecasting model of economic growth based on production function. Statistics and Decision, 2010, 20:34-36.
[10] Li Z N, Pang W Q. Econometrics. Beijing:Higher Education Press, 2010:156-167.
[11] Sun J S, Ma S Q. Econometrics. Beijing:Tsinghua University Press, 2004:203-212.
[12] Cheng M L. A new measure model of factor contribution ratio for economic growth. Mathematics in Economics, 2004, 21(3):235-239.
[13] Cheng M L. Correction and empirical analysis of the CES production function model. Journal of Engineering Mathematics, 2013, 30(4):535-544.
[14] Cheng M L, Han Y. The measure model and analysis of contribution ratio of economic growth factor on Suzhou foreign capital manufacturing. Application of Statistics and Management, 2009, 28(3):381-385.
[15] Liu H L. A trust region algorithm for nonlinear least squares problems. Mathematics in Economics, 2007, 24(2):213-216.
[16] Wang L Q, Leblanc A. Second-order nonlinear least squares estimation. Annals of the Institute of Statistical Mathematics, 2008, 60(4):883-900.
[17] Argyros I K, Magreñán Á A. Local convergence analysis of proximal Gauss-Newton method for penalized nonlinear least squares problems. Applied Mathematics and Computation, 2014, 241:401-408.

Funding

Supported by National Natural Science Foundation of China (11401418)

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