Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 2, March 2018, Pages: 67-79
Received: Sep. 1, 2017;
Accepted: Sep. 18, 2017;
Published: Mar. 16, 2018
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Abraham Kipkosgei Lagat, Department of Basic and Applied Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Gichuhi Waititu, Department of Basic and Applied Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Kibera Wanjoya, Department of Basic and Applied Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
There has been increased interest of late on the application of nonlinear methods to economic and financial data due to their robustness in handling large and complex data. With increasingly complex ‘big data’, focus has shifted into use of robust techniques in analysis of data. Various nonlinear approaches have so far been established including support vector machine which is widely adapted in classification and regression problems. This research project applied support vector regression technique and neural network models in modeling and forecasting economic growth for the five countries in the East Africa Community including Kenya, Uganda, United Republic of Tanzania, Rwanda and Burundi. Data for the period 1990 to 2014 from World Bank databases was used for the research. Support vector model and neural network models were trained using the data for the 1990-2002 whereas the remaining data was used for prediction performance to determine the robustness of the two models on external datasets. The study revealed that specific country models had better performance compared to the combined model and that although the two models compared similarly under specific-country models, the neural network performed better in most countries. The study recommends the use of the two machine learning techniques in economic growth modeling. It also recommends that the performance be compared with the traditional econometric models but using countries with more data periods.
Abraham Kipkosgei Lagat,
Anthony Gichuhi Waititu,
Anthony Kibera Wanjoya,
Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community, American Journal of Theoretical and Applied Statistics.
Vol. 7, No. 2,
2018, pp. 67-79.
Swan, T. W. (1956). Economic growth and capital accumulation. Economic Record. Wiley. 32 (2): 334–361.
Solow, R. M. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics. 70 (1): 65–94.
Cobb, C. W. and Douglas, P. H. (1928). A Theory of Production. American Economic Review, 139-65.
Liand, Q. and Pan, C. G. (2005). The Research of High Precision Gray Forecast Model and Its Application in GDP Forecast of 2005. Research On Financial And Economic Issues, No. 8, pp. 11-13.
Kilian, L. and Taylor, M. P. (2003). Why is it So Di¢ cult to Beat the Random Walk Forecast of Exchange Rates?. Journal of International Economics, 60, 85-107.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57 (2), 357–384.
Granger, C. W. J. (2001). Essays in Econometrics: The Collected Papers of Clive W. J. Granger. Cambridge: Cambridge University Press.
Diebold, F. X., Nason, J., (1990). Nonparametric exchange rate prediction. Journal of International Economics 28 (3-4), 315-332.
Mizrach, B. M., (1992). Multivariate nearest-neighbor forecasts of EMS exchange rates. Journal of Applied Econometrics, 7, S151–S163.
Pagan, A. R. and Schwert, G. W. (1990). Alternative models for conditional stock volatility. Journal of Econometrics, 45, 267-290.
Shin, K., Lee, T., Kim, H., (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, Volume 28, pp. 127-135.
Molinet, T., J. A. Molinet, M. E. Betancourt, A. Palmer, J. J. Montaño (2015). “Models of Artificial Neural Networks Applied to Demand Forecasting in Nonconsolidated Tourist Destinations”. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences. Forthcoming.
Claveria, O., E. Monte, and S. Torra (2016). “A New Forecasting Approach for the Hospitality Industry”. International Journal of Contemporary Hospitality Management, 28 (2).
F. Zhang, C. Deb, S. E. Lee, J. Yang, and K. W. Shah, “Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique,” Energy and Buildings, vol. 126, pp. 94–103, 2016.
W. D. Li, D. M. Kong, and J. R. Wu (2017), “A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting”, Energies, vol. 10, no. 5, p. 694.
Shenify, M. et al. (2016). Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform. Water Resource Management. pp 30: 641.
Cortes, C.; Vapnik, V. (1995). Support vector networks, In Proceedings of Machine Learning 20: 273–297.
Vapnik, V. N. (1998). Statistical Learning Theory. New York: John Wiley and Sons.
Peng, Y.; Kou, G.; Shi, Y.; Chen, Z. X. (2008). A descriptive framework for the field of data mining and knowledge discovery. International Journal of Information Technology and Decision Making, 7 (4): 639–682.
Yang, Q.; Wu, X. D. (2006). 10 Challenging problems in data mining research. International Journal of Information Technology and Decision Making 5 (4): 567–604.
Cassel, G. (1918). Abnormal Deviations in International Exchanges. The Economic Journal, 28, No. 112 (112).: 413–415.
Bassanini, A., Scarpetta, A. and Hemmings, P. (2011), Economic Growth: The Role of Policies and Institutions. Panel Data Evidence from OECD Countries (Working Paper No. 283). OECD Economics Department.
Karush, W. (1939). Minima of functions of several variables with inequalities as side constraints. Master’s thesis, Department of Mathematics, Univ. of Chicago.
Kuhn H. W. and Tucker A. W. (1951). Nonlinear programming. In: Proc. 2nd Berkeley Symposium on Mathematical Statistics and Probabilistics, Berkeley. University of California Press, pp. 481–492.
Serneels, P. and Verpoorten, M (2012). The Impact of Armed Conflict on Economic Performance: Evidence from Rwanda. Discussion Paper No. 6737. IZA.
Lopez, H., Wodon, Q. (2005). The Economic Impact of Armed Conflict in Rwanda. Journal of African Economies, 14 (4): 586-602.
Demiriz, A., Bennett, K., Breneman, C. and Embrechts, M. (2001). Support vector machine regression in chemometrics. Computing Science and Statistics, 2001.