Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community

Abraham Kipkosgei Lagat; Anthony Gichuhi Waititu; Anthony Kibera Wanjoya

doi:doi:10.11648/j.ajtas.20180702.13

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Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community

Abraham Kipkosgei Lagat, Anthony Gichuhi Waititu, Anthony Kibera Wanjoya

Published in American Journal of Theoretical and Applied Statistics (Volume 7, Issue 2)

Received: 1 September 2017 Accepted: 18 September 2017 Published: 16 March 2018

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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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 7, Issue 2)
DOI	10.11648/j.ajtas.20180702.13
Page(s)	67-79
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Support Vector Regression, Artificial Neural Network, Machine Learning, Economic Growth, East Africa, Economic Data

References

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APA Style

Abraham Kipkosgei Lagat, Anthony Gichuhi Waititu, Anthony Kibera Wanjoya. (2018). Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community. American Journal of Theoretical and Applied Statistics, 7(2), 67-79. https://doi.org/10.11648/j.ajtas.20180702.13

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ACS Style

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. Am. J. Theor. Appl. Stat. 2018, 7(2), 67-79. doi: 10.11648/j.ajtas.20180702.13

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AMA Style

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. Am J Theor Appl Stat. 2018;7(2):67-79. doi: 10.11648/j.ajtas.20180702.13

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@article{10.11648/j.ajtas.20180702.13,
  author = {Abraham Kipkosgei Lagat and Anthony Gichuhi Waititu and Anthony Kibera Wanjoya},
  title = {Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {7},
  number = {2},
  pages = {67-79},
  doi = {10.11648/j.ajtas.20180702.13},
  url = {https://doi.org/10.11648/j.ajtas.20180702.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20180702.13},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community
AU  - Abraham Kipkosgei Lagat
AU  - Anthony Gichuhi Waititu
AU  - Anthony Kibera Wanjoya
Y1  - 2018/03/16
PY  - 2018
N1  - https://doi.org/10.11648/j.ajtas.20180702.13
DO  - 10.11648/j.ajtas.20180702.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 67
EP  - 79
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20180702.13
AB  - 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.
VL  - 7
IS  - 2
ER  -

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Author Information

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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APA Style

Abraham Kipkosgei Lagat, Anthony Gichuhi Waititu, Anthony Kibera Wanjoya. (2018). Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community. American Journal of Theoretical and Applied Statistics, 7(2), 67-79. https://doi.org/10.11648/j.ajtas.20180702.13

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ACS Style

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. Am. J. Theor. Appl. Stat. 2018, 7(2), 67-79. doi: 10.11648/j.ajtas.20180702.13

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AMA Style

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. Am J Theor Appl Stat. 2018;7(2):67-79. doi: 10.11648/j.ajtas.20180702.13

Copy | Download

@article{10.11648/j.ajtas.20180702.13,
  author = {Abraham Kipkosgei Lagat and Anthony Gichuhi Waititu and Anthony Kibera Wanjoya},
  title = {Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {7},
  number = {2},
  pages = {67-79},
  doi = {10.11648/j.ajtas.20180702.13},
  url = {https://doi.org/10.11648/j.ajtas.20180702.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20180702.13},
  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.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Support Vector Regression and Artificial Neural Network Approaches: Case of Economic Growth in East Africa Community
AU  - Abraham Kipkosgei Lagat
AU  - Anthony Gichuhi Waititu
AU  - Anthony Kibera Wanjoya
Y1  - 2018/03/16
PY  - 2018
N1  - https://doi.org/10.11648/j.ajtas.20180702.13
DO  - 10.11648/j.ajtas.20180702.13
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 67
EP  - 79
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20180702.13
AB  - 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.
VL  - 7
IS  - 2
ER  -

Copy | Download