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

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.

Published in Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 2)
DOI 10.11648/j.sjams.20160402.18
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), 2016. 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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[10] Hayek, Friedrich (1989). The Collected Works of F. A. Hayek. University of Chicago Press. p. 202. ISBN 978-0-226-32097-7.
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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
  • Department of Physical Sciences, Chuka University, Nairobi, Kenya

  • Department of Physical Sciences, Chuka University, Nairobi, Kenya

  • Department of Physical Sciences, Chuka University, Nairobi, Kenya

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