ESTIMATION OF ECONOMIC GROWTH USING GREY COBB-DOUGLAS PRODUCTION FUNCTION: AN APPLICATION FOR US ECONOMY

Yıl 2018, Cilt: 7 Sayı: 2, 178 - 190, 15.07.2018

Omer Onalan , Hulya Basegmez

https://doi.org/10.17261/Pressacademia.2018.840

Öz

Purpose- In this paper, we apply the Grey Cobb-Douglas production model to predict the GDP, examine the effects of the variation rate of capital, and labor inputs to economic growth. Many factors contribute to economic growth, such that technological progress, labor force, capital accumulation, the optimal using of sources, energy, institutional innovation ext. In reality, a variate of economic factors often intertwine with each other.

Methodology- The capital and labor are main elements of economic growth. Improving the capital and labor performance plays important role in increase the wealth of a country. Traditionally, Cobb-Douglas (C-D) production model use only capital stock and labor to describe the economic growth. In this study, firstly C-D production function is established and confirmed that the capital and labor has a positive impact on economic growth (GDP). Then GM(1,1) prediction model is used to predict the future values of capital stock and labor force inputs.

Findings- The future GDP values are predicted by the estimated capital and labor values putting into the Cobb-Douglas model. We also obtained the production elasticities of capital and labor inputs. Findings suggest that the contribution rate of capital is 0.403 and labor is 1.094 to economic growth. The sum of the contributions of factors is 1.497 and greater than one.

Conclusion- Findings of this empirical studies shows that percentage of the increase in GDP is greater than that of the increase in capital stock and labor.

Anahtar Kelimeler

Cobb-Douglas production function, economic growth, Grey-prediction model

Kaynakça

• Benhabib, J. & Spiegel, M. M. (1994). The role of human capital in economic development evidence from aggregate cross-country data. Journal of Monetary economics, 34(2), 143-173.
• Besanko, D. A. & Braeutigam, R. R. (2010). Microeconomics. 4th. ed. John Wiley & Sons, pp. 211.
• Chen, B. Y. (2012). Classification of h-homogeneous production functions with constant elasticity of substitution. Tamkang Journal of Mathematics, 43(2), 321-328.
• Cheng, M. & Han, Y. (2017). Application of a new superposition CES production function model. Journal of Systems Science and Information, 5(5), 462-472.
• Cobb, C. W. & Douglas, P. H. (1928). A theory of production. The American Economic Review, 18(1), 139-165.
• De Loecker, J. (2011). Product differentiation, multiproduct firms, and estimating the impact of trade liberalization on productivity. Econometrica, Econometric Society, vol. 79(5), pages 1407-1451, 09.
• Deng, J. L. (1982). Control problems of grey systems. Sys. & Contr. Lett., 1(5), 288-294.
• Godin, A. & Kinsella, S. (2013). Production functions at the business end: the case of the European Fiscal Compact. Global & Local Economic Review, 17(1), 153.
• Hicks, J. R. & Allen, R. G. (1934). A reconsideration of the theory of value: Part I. Economica, 1(1), 52-76.
• Honghong, L. (2010). A prediction and influence factor analysis of China's food production. In Information Management, Innovation Management and Industrial Engineering (ICIII), 2010 International Conference on (Vol. 2, pp. 30-33). IEEE.
• Li, J. F., Zhao, J. & Zhu, J. (2010). Empirical study on the influencing factors of building industry's performance based on the Cobb-Douglas production function. 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE), (pp. 1-4). IEEE.
• Liao, Q., Wu, Z. & Xu, J. (2010). A new production function with technological innovation factor and its application to the analysis of energy-saving effect in LSD. Modelling and Simulation, 6(4), 257-266.
• Lucas Jr, R. E. (1988). On the mechanics of economic development. Journal of monetary economics, 22(1), 3-42.
• Mahaboob, B., Venkateswarlu, B. & Balasiddamuni, P. (2017). Estimation of parameters of generalized Cobb-Douglas production functional model. IJPT, 9 (1), p.29186.
• Makridakis, S., Wheelwright, S. C. & Hyndman, R. J. (2008). Forecasting methods and applications. John wiley & sons.
• Mankiw, N. G., Romer, D. & Weil, D. N. (1992). A contribution to the empirics of economic growth. The quarterly journal of economics, 107(2), 407-437.
• Miller, E. (2008). An assessment of CES and Cobb-Douglas production functions. Congressional Budget Office. Mishra, S. (2007). A brief history of production functions.
• Qi, W., Yingsheng, S. & Pengfei, J. (2010). On the random disturbance term and parameter estimation of CD production function. Information Science and Engineering (ISISE), 2010 International Symposium on (pp. 51-53). IEEE.
• Romer, P. M. (1986). Increasing returns and long-run growth. Journal of political economy, 94(5), 1002-1037.
• Stern, D. I. (2011). Elasticities of substitution and complementarity. Journal of Productivity Analysis, 36(1), 79-89.
• Thompson, H. (2016). A physical production function for the US economy. Energy Economics, 56, 185-189.
• Vîlcu, G. E. (2018). On a generalization of a class of production functions. Applied Economics Letters, 25(2), 106-110.
• Wang, X. (2016). A geometric characterization of homogeneous production models in economics. Filomat, 30(13), 3465-3471.
• Zhu, S., Wu, Q. J. & Wang, Y. (2011). Impact of labor force on China's economic growth based on grey metabolic GM (1, 1) model. 2011 IEEE International Conference on Grey Systems and Intelligent Services (GSIS), (pp. 262-265). IEEE.