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. |
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Copyright © The Author(s), 2016. Published by Science Publishing Group |
Gross Domestic Product (GDP), Gretl and SPSS 21 Statistical Softwares, ARIMA (Autoregressive Integrated Moving Average) Models, AIC Criterion
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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
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
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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Download@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}
}
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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