Forecasting Inflation Rate in Kenya Using SARIMA Model

Susan W. Gikungu; Anthony G. Waititu; John M. Kihoro

doi:doi:10.11648/j.ajtas.20150401.13

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Forecasting Inflation Rate in Kenya Using SARIMA Model

Susan W. Gikungu, Anthony G. Waititu, John M. Kihoro

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 1)

Received: 16 December 2014 Accepted: 8 January 2015 Published: 20 January 2015

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Abstract

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.

Published in	American Journal of Theoretical and Applied Statistics (Volume 4, Issue 1)
DOI	10.11648/j.ajtas.20150401.13
Page(s)	15-18
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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Box, G.E.P. and Jenkins G.M. (1976), “Time Series Analysis, forecasting and Control, Holden-Day, San Francisco.
[2]	Brent, M., and Mehmet P. (2010), “Simple ways to forecast inflation: What works best?”, Trade Publication ,17, 1-9.
[3]	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.
[4]	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
[5]	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.
[6]	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.
[7]	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.
[8]	Webster, D. (2000), “New Universal Unabridged Dictionary”, Barnes and Noble Books.

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APA Style

Susan W. Gikungu, Anthony G. Waititu, John M. Kihoro. (2015). Forecasting Inflation Rate in Kenya Using SARIMA Model. American Journal of Theoretical and Applied Statistics, 4(1), 15-18. https://doi.org/10.11648/j.ajtas.20150401.13

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ACS Style

Susan W. Gikungu; Anthony G. Waititu; John M. Kihoro. Forecasting Inflation Rate in Kenya Using SARIMA Model. Am. J. Theor. Appl. Stat. 2015, 4(1), 15-18. doi: 10.11648/j.ajtas.20150401.13

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AMA Style

Susan W. Gikungu, Anthony G. Waititu, John M. Kihoro. Forecasting Inflation Rate in Kenya Using SARIMA Model. Am J Theor Appl Stat. 2015;4(1):15-18. doi: 10.11648/j.ajtas.20150401.13

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@article{10.11648/j.ajtas.20150401.13,
  author = {Susan W. Gikungu and Anthony G. Waititu and John M. Kihoro},
  title = {Forecasting Inflation Rate in Kenya Using SARIMA Model},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {1},
  pages = {15-18},
  doi = {10.11648/j.ajtas.20150401.13},
  url = {https://doi.org/10.11648/j.ajtas.20150401.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150401.13},
  abstract = {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.},
 year = {2015}
}

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TY - JOUR
T1 - Forecasting Inflation Rate in Kenya Using SARIMA Model
AU - Susan W. Gikungu
AU - Anthony G. Waititu
AU - John M. Kihoro
Y1 - 2015/01/20
PY - 2015
N1 - https://doi.org/10.11648/j.ajtas.20150401.13
DO - 10.11648/j.ajtas.20150401.13
T2 - American Journal of Theoretical and Applied Statistics
JF - American Journal of Theoretical and Applied Statistics
JO - American Journal of Theoretical and Applied Statistics
SP - 15
EP - 18
PB - Science Publishing Group
SN - 2326-9006
UR - https://doi.org/10.11648/j.ajtas.20150401.13
AB - 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.
VL - 4
IS - 1
ER -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Susan W. Gikungu, Anthony G. Waititu, John M. Kihoro. (2015). Forecasting Inflation Rate in Kenya Using SARIMA Model. American Journal of Theoretical and Applied Statistics, 4(1), 15-18. https://doi.org/10.11648/j.ajtas.20150401.13

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ACS Style

Susan W. Gikungu; Anthony G. Waititu; John M. Kihoro. Forecasting Inflation Rate in Kenya Using SARIMA Model. Am. J. Theor. Appl. Stat. 2015, 4(1), 15-18. doi: 10.11648/j.ajtas.20150401.13

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AMA Style

Susan W. Gikungu, Anthony G. Waititu, John M. Kihoro. Forecasting Inflation Rate in Kenya Using SARIMA Model. Am J Theor Appl Stat. 2015;4(1):15-18. doi: 10.11648/j.ajtas.20150401.13

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@article{10.11648/j.ajtas.20150401.13,
  author = {Susan W. Gikungu and Anthony G. Waititu and John M. Kihoro},
  title = {Forecasting Inflation Rate in Kenya Using SARIMA Model},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {1},
  pages = {15-18},
  doi = {10.11648/j.ajtas.20150401.13},
  url = {https://doi.org/10.11648/j.ajtas.20150401.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150401.13},
  abstract = {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.},
 year = {2015}
}

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