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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Authors
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
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Abstract
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.
Keywords
Support Vector Regression, Artificial Neural Network, Machine Learning, Economic Growth, East Africa, Economic Data
To cite this article
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. doi: 10.11648/j.ajtas.20180702.13
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Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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