CES VE TRANSLOG ÜRETİM FONKSİYONLARI KULLANILARAK ÜRETİM FAKTÖRLERİNİN GDP ÜZERİNDEKİ ETKİSİNİN KESTİRİMİ: ÇİN EKONOMİSİ İÇİN BİR UYGULAMA

Abstract

Bu çalışmada, CES üretim fonksiyonu ve Translog üretim fonksiyonunu yardımıyla Çin ekonomisine ait sermaye, emek ve enerji girdi faktörlerinin ekonomik büyüme (GSYİH) üzerindeki etkisi araştırılmaktadır. Çalışmanın ampirik bulgularına göre, CES üretim fonksiyonu kullanılarak elde edilen GSYİH verilerinin Translog üretim fonksiyonu kullanılarak elde edilen kestirimlerden daha az verimli olduğu söylenebilir. Modelde yer alan değişkenler arasında çoklu doğrusal bağıntı problemi mevcut olduğu için parametre kestiriminde Ridge regresyon tekniği kullanılmıştır. Daha sonra sermaye, emek ve enerji girdi faktörlerini içeren uygun Translog üretim modeline dayalı olarak, faktörlerin her biri için çıktı esnekliklerinin sonuçları ve girdi faktörleri arasındaki ikame esnekliklerinin sonuçları dinamik olarak tahmin edilmiştir. Ayrıca, Çin ekonomisinin gelecekteki ekonomik büyümesini tahmin etmek için Translog üretim modelinin girdileri Holt-Winter’s yöntemi kullanılarak tahmin edilmiştir. Tüm girdi faktörlerinin çıktı esneklikleri pozitif olup, girdi faktörlerini GSYİH üzerindeki etki derecelerine göre, sırasıyla, emek, sermaye ve enerji olarak sıralayabiliriz. Bu durum Çin ekonomisinin emek ve sermaye yoğun olduğunu göstermektedir.

Keywords

• Gayri Safi Yurtiçi Hasıla
• CES Üretim Fonksiyonu
• Translog Üretim Fonksiyonu
• Holt-Winter’s yöntemi
• Ridge regresyon

ESTIMATION OF EFFECT ON GROSS DOMESTIC PRODUCT OF PRODUCTION FACTORS USING CES AND TRANSLOG PRODUCTION FUNCTIONS: AN APPLICATION TO CHINA ECONOMY

Abstract

In this study, the effects on the economic growth (GDP) of capital, labor and energy input factors for the Chinese economy are investigated with the help of the CES and Translog production functions. According to the empirical findings of the study, it can be said that the GDP data obtained using the CES production function are less efficient than the estimates obtained using the Translog production function. The Ridge regression technique was used for parameter estimation of Translog production model since there is multicollinearity between the variables in the model. The output elasticities and the substitution elasticities between each input factor are then dynamically estimated, based on the appropriate Translog production model that includes the capital, labor and energy input factors. In addition, the inputs of the Translog production model were estimated using the Holt-Winter's method to predict the future economic growth of the Chinese economy. Consequently, output elasticities of all input factors are positive, and we can rank the input factors as labor, capital and energy according to their degree of impact on GDP, respectively. This shows that the Chinese economy is labor and capital intensive.

Keywords

• Gross Domestic Product
• CES production function
• Translog production function
• Holt-Winter’s method
• Ridge Regression

References

1. Arrow, K. J., Chenery, H. B., Minhas, B. S., & Solow, R. M. (1961). Capital-Labor Substitution and Economic Efficiency. The Review of Economics and Statistics, 43(3), 225–250. http://www.aae.wisc.edu/aae705/References/Arrow_etal_1961.pdf
2. 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. https://doi.org/10.1016/0304-3932(94)90047-7
3. Cheng, M., & Han, Y. (2017). Application of a New Superposition CES Production Function Model. Journal of Systems Science and Information, 5(5), 462–472. https://doi.org/10.21078/JSSI-2017-462-11
4. Christensen, L. R., Jorgenson, D. W., & Lau, L. J. (1973). Transcendental Logarithmic Production Frontiers. The Review of Economics and Statistics, 55(1), 28–45.
5. Dhakre, D. S., Sarkar, K. A., & Manna, S. (2016). Forecast Price of Brinjal By Holt Winters Method in West Bengal Using Ms Excel. International Journal of Bio-Envoironment and Agricultural Sciences, 2(1), 232–236.
6. Hamilton J. D. (1994). Time-Series Analysis. Princeton University Press. https://doi.org/10.2307/1270781
7. Henningsen, A., & Henningsen, G. (2012). On Estimation of The CES Production Function-Revisited. Economics Letters, 115(1), 67–69. https://doi.org/10.1016/j.econlet.2011.12.007
8. Henningsen, A., Henningsen, G., & Van der Werf, E. (2019). Capital-Labour-Energy Substitution in A Nested CES Framework: A Replication and Update of Kemfert (1998). Energy Economics, Article in. https://doi.org/10.1016/j.eneco.2017.12.019

9. Hoerl, A. E., & Kennard, R. W. (1970). Ridge Regression : Applications to Nonorthogonal Problems. Technometrics, 12(1), 69–82.
10. Hoff, A. (2002). The translog approximation of the constant elasticity of substitution production function with more than two input variables (Issue 14). Fødevareøkonomisk Institut. http://curis.ku.dk/ws/files/135535225/14.pdf.pdf
11. Hossain, M. M., Majumder, A. K., & Basak, T. (2013). An Application of Non–Linear Cobb-Douglas Production Function to Selected Manufacturing Industries in Bangladesh. Open Journal of Statistics, 3, 173–178. https://doi.org/10.4236/ojs.2012.24058
12. Hrishikesh D. Vinod. (1978). A Survey of Ridge Regression and Related Techniques for Improvements over Ordinary Least Squares. The Review of Economics and Statistics, 60(1), 121–131.
13. Kemfert, C. (1998). Estimated substitution of a nested CES production function approach for Germany. Energy Economics, 20, 249–264. https://doi.org/10.1016/S0140-9883(97)00014-5
14. Kmenta, J. (1967). On Estimation of the CES Production Function. International Economic Review, 8(2), 180–189.
15. Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. London : Butterworth Scientific.
16. Lin, B., & Ahmad, I. (2016). Energy substitution effect on transport sector of Pakistan based on trans-log production function. Renewable and Sustainable Energy Reviews, 56, 1182–1193. https://doi.org/10.1016/j.rser.2015.12.012
17. Lin, B., & Liu, W. (2017). Estimation of energy substitution effect in China’s machinery industry–based on the corrected formula for elasticity of substitution. Energy, 129, 246–254. https://doi.org/10.1016/j.energy.2017.04.103
18. Lloyd, P. J., & MacLaren, D. (2002). Measures of trade openness using CGE analysis. Journal of Policy Modeling, 24(1), 67–81. https://doi.org/10.1016/S0161-8938(01)00096-5
19. Lucas Jr., R. E. (1988). On the mechanics of economic development. Journal of Monetary Economics, 22(1), 3–4. https://doi.org/Doi 10.1016/0304-3932(88)90168-7
20. Mahaboob, B., Venkateswarlu, B., & Balasiddamuni, P. (2017). Estimation of Parameters of Generalized Cobb-Douglas production Functional Model. IJPT, 9(1), 29181–29188.
21. Mankiw, N.G., Romer, D. and Weil, D. . (1992). A contribution to the empirics of Economic Growth. The Quarterly Journal of Economics, 152, 407–437. https://doi.org/10.1016/j.jpolmod.2014.01.001
22. McFadden, D. (1963). Constant elasticity of substitution production functions. The Review of Economic Studies, 30(2), 73–83. https://www.jstor.org/stable/2295804
23. McFarland, J. R., Reilly, J. M., & Herzog, H. J. (2004). Representing energy technologies in top-down economic models using bottom-up information. Energy Economics, 26(4), 685–707. https://doi.org/10.1016/j.eneco.2004.04.026
24. Muniz, G., & Kibria, B. M. G. (2009). On some ridge regression estimators: An empirical comparisons. Communications in Statistics: Simulation and Computation, 38(3), 621–630. https://doi.org/10.1080/03610910802592838
25. Onalan, O., & Basegmez, H. (2018). Estimation of economic growth using Grey Cobb-Douglas production function: An application for US economy. Journal of Business, Economics and Finance, 7(2), 178–190. https://doi.org/10.17261/Pressacademia.2018.840
26. Papageorgiou, C., & Saam, M. (2008). Two-level CES production technology in the solow and diamond growth models. Scandinavian Journal of Economics, 110(1), 119–143. https://doi.org/10.1111/j.1467-9442.2008.00529.x
27. Prado, R., & West, M. (2010). Time series: Modelling, Computation and Inference. CRC Press.
28. Raymond W. Goldsmith. (1951). A Perpetual Inventory of National Wealth (Vol. 14). In Studies in Income and Wealth. https://doi.org/DOI 10.1002/chem.200903127
29. Romer, P. M. (1986). Increasing Returns and Long-Run Growth. Journal of Political Economy, 94(5), 1002–1037. https://doi.org/10.1086/261420
30. Stern, D. I. (2011). Elasticities of substitution and complementarity. Journal of Productivity Analysis, 36(1), 79–89. https://doi.org/10.1007/s11123-010-0203-1
31. Thursby, J. G., & Lovell, C. A. K. (1978). An Investigation of the Kmenta Approximation to the CES Function. International Economic Review, 19(2), 363–377. https://www.jstor.org/stable/2526306
32. Uzawa, H. (1962). Production Functions with Constant Elasticities of Substitution. The Review of Economic Studies, 29(4), 291. https://doi.org/10.2307/2296305