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), 2015. Published by Science Publishing Group |
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
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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
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
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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Download@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}
}
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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