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
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.
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
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Ahmad, J. and Harnhirun, S. (1996), Cointegration and causality between exports and economic growth: evidence from the Asian countries. Canadian Journal of Economics, 413-6.
Ard, H. J, den Reijer. (2010), Macroeconomic Forecasting using Business Cycle leading indicators, Stockholm: US-AB.
Barro, R. J. (1990). Economic Growth in a Cross section of Countries. The Quarterly Journal of Economics, 47-53.
Box, G. E. P., and Jenkins, G., (1970). Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco.
Box, George E. P. and Gwilym M. Jenkins (1976). Time Series Analysis: Forecasting and Control, Revised Edition, Oakland, CA: Holden-Day.
Cheng, Ming-yu, T. Hui-Boon (2002), Faculty of Management, Multimedia University, Malaysia, Journal of Inflation in Malaysia, 29(5), 411-425.
Chatfield, C. (1996). The Analysis of Time Series, 5th ed., Chapman & Hall, New York, NY.
Dodaro, S. (1993). Exports and Growth: A Reconsideration of causity. Journal of Developing Areas, 227-234.
Dullah, L. and Kasim. (2010). Determinant factors of Economic growth in Malaysia: Multivariate cointegration and Causality analysis. European Journal of Economics, Finance and Administrative Sciences, ISSN, 1450-2275.
Hayek, Friedrich (1989). The Collected Works of F. A. Hayek. University of Chicago Press. p. 202. ISBN 978-0-226-32097-7.
Heller, P. S, and R. C Porter. (1978). “Exports and growth: An Empirical Investigation.” Journal of Development Economics, 191-193.
Leedy, P. D. (1997). Practical research planning and design (6th ed.). Upper Saddle River, NJ: Prentice-Hall, Inc., 232-233.
Litterman, R. B. (1986). Forecasting with Bayesian Vector Autoregressions-five Years of experience. Journal of Business & Economic Statistics, 25-38.
Mugenda O. M. and Mugenda A. G. (2003). Research Methods: Quantitative and Qualitative Approaches. Nairobi, Kenya. Acts Press.
Ning, W., Kuan-jiang, B. and Zhi-fa, Y. (2010), Analysis and forecast of Shaanxi GDP based on the ARIMA Model, Asian Agricultural Research, Vol. 2 No. 1, pp. 34-41.
Ram, R. (1986). Government Size and Economic Growth. A new Framework and some Empirical Evidence from Cross-sectional and Time Series Data, 191-203.
Sheehey, Edmund J., (1992), ‘Exports and Growth: Additional Evidence’, Journal of Development Studies, Vol.28, No.4.
Sinha, D. and Sinha, T., (2007), Toda and Yamamoto causality test between per capita saving and per capita GDP for India, MRPA Paper No 2564.
Slutzky Eugen, (Apr., 1937), The Summation of Random Causes as the Source of Cyclic Processes, Econometrica, Vol. 5,No. 2, pp. 105-146.
Stockton, David J. and James E. Glassman (1987). “An Evaluation of the Forecast Performance of Alternative Models of Inflation,” The Review of Economics and Statistics 69, 108-117.
Wold H. (1938), A Study in Analysis of Stationary Time Series, Uppsala.
Yoo J, Maddala. (1991). Risk Premia and Price Volatility in Future Markets. Journal of Future Markets, 11 (2): 165-177.
Yule G. U., (Jan., 1926). Why do we sometimes get Nonsense-Correlations between Time-Series? A Study in Sampling and the Nature of Time-Series, Journal of the Royal Statistical Society, Vol. 89, No. 1, pp. 1-63.
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