Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models

Musundi Sammy Wabomba; M’mukiira Peter Mutwiri; Mungai Fredrick

doi:doi:10.11648/j.sjams.20160402.18

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Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models

Musundi Sammy Wabomba, M’mukiira Peter Mutwiri, Mungai Fredrick

Received: 14 March 2016 Accepted: 25 March 2016 Published: 13 April 2016

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Abstract

The Gross Domestic Product (GDP) is the market value of all goods and services produced within the borders of a nation in a year. In this paper, Kenya’s annual GDP data obtained from the Kenya National Bureau of statistics for the years 1960 to 2012 was studied. Gretl and SPSS 21 statistical softwares were used to build a class of ARIMA (autoregressive integrated moving average) models following the Box-Jenkins method to model the GDP. ARIMA (2, 2, 2) time series model was established as the best for modeling the Kenyan GDP according to the recognition rules and stationary test of time series under the AIC criterion. The results of an in-sample forecast showed that the relative and predicted values were within the range of 5%, and the forecasting effect of this model was relatively adequate and efficient in modeling the annual returns of the Kenyan GDP. Finally, we used the fitted ARIMA model to forecast the GDP of Kenya for the next five years.

DOI	10.11648/j.sjams.20160402.18
Published in	Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 2, April 2016)
Page(s)	64-73
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), 2024. Published by Science Publishing Group

Keywords

Gross Domestic Product (GDP), Gretl and SPSS 21 Statistical Softwares, ARIMA (Autoregressive Integrated Moving Average) Models, AIC Criterion

References

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APA Style

Musundi Sammy Wabomba, M’mukiira Peter Mutwiri, Mungai Fredrick. (2016). Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. Science Journal of Applied Mathematics and Statistics, 4(2), 64-73. https://doi.org/10.11648/j.sjams.20160402.18

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ACS Style

Musundi Sammy Wabomba; M’mukiira Peter Mutwiri; Mungai Fredrick. Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. Sci. J. Appl. Math. Stat. 2016, 4(2), 64-73. doi: 10.11648/j.sjams.20160402.18

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AMA Style

Musundi Sammy Wabomba, M’mukiira Peter Mutwiri, Mungai Fredrick. Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. Sci J Appl Math Stat. 2016;4(2):64-73. doi: 10.11648/j.sjams.20160402.18

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@article{10.11648/j.sjams.20160402.18,
  author = {Musundi Sammy Wabomba and M’mukiira Peter Mutwiri and Mungai Fredrick},
  title = {Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {4},
  number = {2},
  pages = {64-73},
  doi = {10.11648/j.sjams.20160402.18},
  url = {https://doi.org/10.11648/j.sjams.20160402.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160402.18},
  abstract = {The Gross Domestic Product (GDP) is the market value of all goods and services produced within the borders of a nation in a year. In this paper, Kenya’s annual GDP data obtained from the Kenya National Bureau of statistics for the years 1960 to 2012 was studied. Gretl and SPSS 21 statistical softwares were used to build a class of ARIMA (autoregressive integrated moving average) models following the Box-Jenkins method to model the GDP. ARIMA (2, 2, 2) time series model was established as the best for modeling the Kenyan GDP according to the recognition rules and stationary test of time series under the AIC criterion. The results of an in-sample forecast showed that the relative and predicted values were within the range of 5%, and the forecasting effect of this model was relatively adequate and efficient in modeling the annual returns of the Kenyan GDP. Finally, we used the fitted ARIMA model to forecast the GDP of Kenya for the next five years.},
 year = {2016}
}

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TY  - JOUR
T1  - Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models
AU  - Musundi Sammy Wabomba
AU  - M’mukiira Peter Mutwiri
AU  - Mungai Fredrick
Y1  - 2016/04/13
PY  - 2016
N1  - https://doi.org/10.11648/j.sjams.20160402.18
DO  - 10.11648/j.sjams.20160402.18
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 64
EP  - 73
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20160402.18
AB  - The Gross Domestic Product (GDP) is the market value of all goods and services produced within the borders of a nation in a year. In this paper, Kenya’s annual GDP data obtained from the Kenya National Bureau of statistics for the years 1960 to 2012 was studied. Gretl and SPSS 21 statistical softwares were used to build a class of ARIMA (autoregressive integrated moving average) models following the Box-Jenkins method to model the GDP. ARIMA (2, 2, 2) time series model was established as the best for modeling the Kenyan GDP according to the recognition rules and stationary test of time series under the AIC criterion. The results of an in-sample forecast showed that the relative and predicted values were within the range of 5%, and the forecasting effect of this model was relatively adequate and efficient in modeling the annual returns of the Kenyan GDP. Finally, we used the fitted ARIMA model to forecast the GDP of Kenya for the next five years.
VL  - 4
IS  - 2
ER  -

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Author Information

Musundi Sammy Wabomba

Department of Physical Sciences, Chuka University, Nairobi, Kenya
M’mukiira Peter Mutwiri

Department of Physical Sciences, Chuka University, Nairobi, Kenya
Mungai Fredrick

Department of Physical Sciences, Chuka University, Nairobi, Kenya

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Plain Text BibTeX RIS

APA Style

Musundi Sammy Wabomba, M’mukiira Peter Mutwiri, Mungai Fredrick. (2016). Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. Science Journal of Applied Mathematics and Statistics, 4(2), 64-73. https://doi.org/10.11648/j.sjams.20160402.18

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ACS Style

Musundi Sammy Wabomba; M’mukiira Peter Mutwiri; Mungai Fredrick. Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. Sci. J. Appl. Math. Stat. 2016, 4(2), 64-73. doi: 10.11648/j.sjams.20160402.18

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AMA Style

Musundi Sammy Wabomba, M’mukiira Peter Mutwiri, Mungai Fredrick. Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models. Sci J Appl Math Stat. 2016;4(2):64-73. doi: 10.11648/j.sjams.20160402.18

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@article{10.11648/j.sjams.20160402.18,
  author = {Musundi Sammy Wabomba and M’mukiira Peter Mutwiri and Mungai Fredrick},
  title = {Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {4},
  number = {2},
  pages = {64-73},
  doi = {10.11648/j.sjams.20160402.18},
  url = {https://doi.org/10.11648/j.sjams.20160402.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160402.18},
  abstract = {The Gross Domestic Product (GDP) is the market value of all goods and services produced within the borders of a nation in a year. In this paper, Kenya’s annual GDP data obtained from the Kenya National Bureau of statistics for the years 1960 to 2012 was studied. Gretl and SPSS 21 statistical softwares were used to build a class of ARIMA (autoregressive integrated moving average) models following the Box-Jenkins method to model the GDP. ARIMA (2, 2, 2) time series model was established as the best for modeling the Kenyan GDP according to the recognition rules and stationary test of time series under the AIC criterion. The results of an in-sample forecast showed that the relative and predicted values were within the range of 5%, and the forecasting effect of this model was relatively adequate and efficient in modeling the annual returns of the Kenyan GDP. Finally, we used the fitted ARIMA model to forecast the GDP of Kenya for the next five years.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models
AU  - Musundi Sammy Wabomba
AU  - M’mukiira Peter Mutwiri
AU  - Mungai Fredrick
Y1  - 2016/04/13
PY  - 2016
N1  - https://doi.org/10.11648/j.sjams.20160402.18
DO  - 10.11648/j.sjams.20160402.18
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 64
EP  - 73
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20160402.18
AB  - The Gross Domestic Product (GDP) is the market value of all goods and services produced within the borders of a nation in a year. In this paper, Kenya’s annual GDP data obtained from the Kenya National Bureau of statistics for the years 1960 to 2012 was studied. Gretl and SPSS 21 statistical softwares were used to build a class of ARIMA (autoregressive integrated moving average) models following the Box-Jenkins method to model the GDP. ARIMA (2, 2, 2) time series model was established as the best for modeling the Kenyan GDP according to the recognition rules and stationary test of time series under the AIC criterion. The results of an in-sample forecast showed that the relative and predicted values were within the range of 5%, and the forecasting effect of this model was relatively adequate and efficient in modeling the annual returns of the Kenyan GDP. Finally, we used the fitted ARIMA model to forecast the GDP of Kenya for the next five years.
VL  - 4
IS  - 2
ER  -

Copy | Download