Volume 4, Issue 1, January 2015, Page: 15-18
Forecasting Inflation Rate in Kenya Using SARIMA Model
Susan W. Gikungu, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Anthony G. Waititu, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
John M. Kihoro, Computing and E-learning Department, Co-operative University College of Kenya, Nairobi, Kenya
Received: Dec. 16, 2014;       Accepted: Jan. 8, 2015;       Published: Jan. 20, 2015
DOI: 10.11648/j.ajtas.20150401.13      View  3006      Downloads  379
It is the desire of the policy makers in a country is to have access to reliable forecast of inflation rate. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast Kenya's inflation rate using quarterly data for the period 1981 to 2013 obtained from KNBS. SARIMA (0,1,0) (0,0,1)4 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. Diagnostic checks using Jarque-Bera Normality Test indicated that they were normally distributed. ACF and PACF plots for the residuals and squared residuals revealed that they followed a white noise process and were homoskedastic respectively. The predictive ability tests RMSE=0.2871, MAPE=3.9456 and MAE= 0.2369 showed that the model was appropriate for forecasting the inflation rate in Kenya.
Seasonal Autoregressive Integrated Moving Average (SARIMA), Kenya National Bureau of Statistics (KNBS), Autocorrelation function (ACF) and Partial Autocorrelation Function (PACF), Akaike Information Criterion and Jarque-Bera Test
To cite this article
Susan W. Gikungu, Anthony G. Waititu, John M. Kihoro, Forecasting Inflation Rate in Kenya Using SARIMA Model, American Journal of Theoretical and Applied Statistics. Vol. 4, No. 1, 2015, pp. 15-18. doi: 10.11648/j.ajtas.20150401.13
Box, G.E.P. and Jenkins G.M. (1976), “Time Series Analysis, forecasting and Control, Holden-Day, San Francisco.
Brent, M., and Mehmet P. (2010), “Simple ways to forecast inflation: What works best?”, Trade Publication ,17, 1-9.
Fannoh, R., Orwa G., and Mung’atu J. K.. (2014), “Modeling the Inflation Rates in Liberia SARIMA Approach”, International Journal of Science and Research, 3, 1360-1367.
Kibunja H., Kihoro J. and Orwa G.(2014), “Forecasting Precipitation Using SARIMA Model: A Case Study of Mt. Kenya Region”, International Institute for Science, Technology and Education, 4( 11), 50-58
Martinez E. Z., and Soares E.A. (2011), “Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model”, Revista da Sociedade Brasileira de Medicina Tropical, 44(4), 436-440.
OtuA. O., Osuji G. A., Opara J., Ifeyinwa M. H., and Iheagwara A.I. (2014), “Application of SARIMA models in modelling and forecasting Nigeria's inflation rates”, American Journal of Applied Mathematics and Statistics, 2, 16-28.
Saz G. (2011), “The efficacy of SARIMA models in forecasting inflation rates in developing countries: The case for Turkey”, International Research Journal of Finance and Economics, 62, 111-142.
Webster, D. (2000), “New Universal Unabridged Dictionary”, Barnes and Noble Books.
Browse journals by subject