Volume 4, Issue 2, April 2016, Page: 64-73
Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models
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
Received: Mar. 14, 2016;       Accepted: Mar. 25, 2016;       Published: Apr. 13, 2016
DOI: 10.11648/j.sjams.20160402.18      View  5016      Downloads  206
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.
Keywords
Gross Domestic Product (GDP), Gretl and SPSS 21 Statistical Softwares, ARIMA (Autoregressive Integrated Moving Average) Models, AIC Criterion
To cite this article
Musundi Sammy Wabomba, M’mukiira Peter Mutwiri, Mungai Fredrick, Modeling and Forecasting Kenyan GDP Using Autoregressive Integrated Moving Average (ARIMA) Models, Science Journal of Applied Mathematics and Statistics. Vol. 4, No. 2, 2016, pp. 64-73. doi: 10.11648/j.sjams.20160402.18
Copyright
Copyright © 2016 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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