Arima Forecasting Model for Uganda’s Consumer Price Index

Yeko Mwanga

doi:doi:10.11648/j.ajtas.20200905.17

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Arima Forecasting Model for Uganda’s Consumer Price Index

Yeko Mwanga

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 5)

Received: 10 September 2020 Accepted: 23 September 2020 Published: 12 October 2020

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Abstract

In Uganda, the Central Bank watches closely inflation which happens to be one of the key macroeconomic indicators for which the central bank rate is anchored on. Uganda Bureau of Statistics disseminates monthly Consumer Price Indices (CPIs) to the various stakeholders. Currently, the CPI is computed for eight urban centres spread across the country. The monthly CPIs serve mostly those users who require past and current inflation rates. The main objective of this study is to identify and estimate an ARIMA model for the CPI and use it to make short term forecasts. We relied upon monthly Consumer Price Indices from January 2010 to July 2020 obtained from Uganda Bureau of Statistics. The time series was transformed so as to make it stationary, before identification and estimation of ARIMA (p, d, q) x (P, D, Q)₁₂ models. An ARIMA (1, 1, 1) (0, 1, 1)₁₂ with no constant was selected as the best model, because it had the least AIC and BIC. Additionally, all the coefficients of the ARs and MAs were significant at 1% level. Using the selected model, inflation forecasts were generated for 12 months (August 2020 to July 2021) and found to fluctuate between 4.7 and 6 percent. We recommend this model to Uganda Bureau of Statistics and Central Bank to use it to make forecasts and disseminate them to users. In conclusion, generally good forecasts are vital for better resource allocation, planning and decision making.

Published in	American Journal of Theoretical and Applied Statistics (Volume 9, Issue 5)
DOI	10.11648/j.ajtas.20200905.17
Page(s)	238-244
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), 2020. Published by Science Publishing Group

Keywords

Consumer Price Index, Inflation, ARIMA Model, Forecasts, Uganda

References

[1]	Government of Uganda, "Investment Code Act 1991," Uganda Printing and Publishing Corporation, Kampala, 1991.
[2]	Uganda Revenue Authority, "A Guide on Tax Incentives/Exemptions Avialbale to the Uganda Investors," 9 September 2019. [Online]. Available: https://www.ugandainvest.go.ug/wp-content/uploads/2019/12/Tax-Incentives-for-2019.pdf.
[3]	Bank of Uganda, "State of the Economy," Bank of Uganda, Kampala, 2019.
[4]	A. Mugume, "The Inflation Targeting Monetary Policy Framework, Seminar for Business Editors and Reporters," Bank of Uganda, Kampala, 2011.
[5]	D. N. Mubiru, E. Komutunga, A. Agona, A. Apok and T. Ngara, "Characterising agrometeorological climate risks and uncertainties: Crop production in Uganda," South African Journal of Science, pp. 1-11, 2011.
[6]	Bank of Uganda, "monetary policy," 17 August 2020. [Online]. Available: https://www.bou.or.ug/bou/bouwebsite/MonetaryPolicy/.
[7]	International Labour Organization, "Consumer Price Index Manual, Theory and Practice," International Labour Organization, Geneva, 2004.
[8]	R. Adhikari and K. R. Agrawal, "An Introductory Study on Time Series Modeling and Forecasting," LAP LAMBERT Academic Publishing, New Dehli, 2013.
[9]	E. Stellwagen and L. Tashman, "ARIMA: The Models of Box and Jenkins," The International Journal of Applied Forecasting, 2013.
[10]	D. C. Montgomery, C. L. Jennings and M. Kulahci, Introduction to Time Series Analysis & Foresating, Canada: John Wiley & Sons, 2008.
[11]	Uganda Bureau of Statistics, "Consumer Price Index: 2009/2010=100: July 2020," Uganda Bureau of Statitics, Kampala, 2020.
[12]	N. Habimana, A. Wanjoya and A. Waititu, "Modeling and Forecasting Consumer Price Index (Case Study of Rwanda," American Journal of Theoretical and Applied Statistics, pp. 101-107, 2026.
[13]	X. Zhang, "CPI Prediction Based on ARIMA Model," in 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017), Shanghai, 2017.
[14]	S. O. Adams, A. Awujola and A. I. Alumgudu, "Modeling Nigeria's Consumer Price Index using ARIMA Model," International Journal of Development and Economic Sustainability, pp. 37-47, 2014.
[15]	T. Nyoni, "ARIMA Modeling and Focasting of CPI in Germany," Munich Personal RePEc Archive, pp. 1-13, 2019.
[16]	J. Mohamed, "Time Series Modeling and Forecasting of Somaliland Consumer Price Index: A comparision of ARIMA and Regression with ARIMA Error," American Journal of Theoretical and Applied Statistics, pp. 143-153, 2020.

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Yeko Mwanga. (2020). Arima Forecasting Model for Uganda’s Consumer Price Index. American Journal of Theoretical and Applied Statistics, 9(5), 238-244. https://doi.org/10.11648/j.ajtas.20200905.17

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Yeko Mwanga. Arima Forecasting Model for Uganda’s Consumer Price Index. Am. J. Theor. Appl. Stat. 2020, 9(5), 238-244. doi: 10.11648/j.ajtas.20200905.17

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AMA Style

Yeko Mwanga. Arima Forecasting Model for Uganda’s Consumer Price Index. Am J Theor Appl Stat. 2020;9(5):238-244. doi: 10.11648/j.ajtas.20200905.17

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@article{10.11648/j.ajtas.20200905.17,
  author = {Yeko Mwanga},
  title = {Arima Forecasting Model for Uganda’s Consumer Price Index},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {5},
  pages = {238-244},
  doi = {10.11648/j.ajtas.20200905.17},
  url = {https://doi.org/10.11648/j.ajtas.20200905.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200905.17},
  abstract = {In Uganda, the Central Bank watches closely inflation which happens to be one of the key macroeconomic indicators for which the central bank rate is anchored on. Uganda Bureau of Statistics disseminates monthly Consumer Price Indices (CPIs) to the various stakeholders. Currently, the CPI is computed for eight urban centres spread across the country. The monthly CPIs serve mostly those users who require past and current inflation rates. The main objective of this study is to identify and estimate an ARIMA model for the CPI and use it to make short term forecasts. We relied upon monthly Consumer Price Indices from January 2010 to July 2020 obtained from Uganda Bureau of Statistics. The time series was transformed so as to make it stationary, before identification and estimation of ARIMA (p, d, q) x (P, D, Q)12 models. An ARIMA (1, 1, 1) (0, 1, 1)12 with no constant was selected as the best model, because it had the least AIC and BIC. Additionally, all the coefficients of the ARs and MAs were significant at 1% level. Using the selected model, inflation forecasts were generated for 12 months (August 2020 to July 2021) and found to fluctuate between 4.7 and 6 percent. We recommend this model to Uganda Bureau of Statistics and Central Bank to use it to make forecasts and disseminate them to users. In conclusion, generally good forecasts are vital for better resource allocation, planning and decision making.},
 year = {2020}
}

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TY - JOUR
T1 - Arima Forecasting Model for Uganda’s Consumer Price Index
AU - Yeko Mwanga
Y1 - 2020/10/12
PY - 2020
N1 - https://doi.org/10.11648/j.ajtas.20200905.17
DO - 10.11648/j.ajtas.20200905.17
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 - 238
EP - 244
PB - Science Publishing Group
SN - 2326-9006
UR - https://doi.org/10.11648/j.ajtas.20200905.17
AB - In Uganda, the Central Bank watches closely inflation which happens to be one of the key macroeconomic indicators for which the central bank rate is anchored on. Uganda Bureau of Statistics disseminates monthly Consumer Price Indices (CPIs) to the various stakeholders. Currently, the CPI is computed for eight urban centres spread across the country. The monthly CPIs serve mostly those users who require past and current inflation rates. The main objective of this study is to identify and estimate an ARIMA model for the CPI and use it to make short term forecasts. We relied upon monthly Consumer Price Indices from January 2010 to July 2020 obtained from Uganda Bureau of Statistics. The time series was transformed so as to make it stationary, before identification and estimation of ARIMA (p, d, q) x (P, D, Q)12 models. An ARIMA (1, 1, 1) (0, 1, 1)12 with no constant was selected as the best model, because it had the least AIC and BIC. Additionally, all the coefficients of the ARs and MAs were significant at 1% level. Using the selected model, inflation forecasts were generated for 12 months (August 2020 to July 2021) and found to fluctuate between 4.7 and 6 percent. We recommend this model to Uganda Bureau of Statistics and Central Bank to use it to make forecasts and disseminate them to users. In conclusion, generally good forecasts are vital for better resource allocation, planning and decision making.
VL - 9
IS - 5
ER -

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Author Information

Yeko Mwanga

School of Statistics and Planning, College of Business and Management Sciences, Makerere University, Kampala, Uganda

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APA Style

Yeko Mwanga. (2020). Arima Forecasting Model for Uganda’s Consumer Price Index. American Journal of Theoretical and Applied Statistics, 9(5), 238-244. https://doi.org/10.11648/j.ajtas.20200905.17

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ACS Style

Yeko Mwanga. Arima Forecasting Model for Uganda’s Consumer Price Index. Am. J. Theor. Appl. Stat. 2020, 9(5), 238-244. doi: 10.11648/j.ajtas.20200905.17

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AMA Style

Yeko Mwanga. Arima Forecasting Model for Uganda’s Consumer Price Index. Am J Theor Appl Stat. 2020;9(5):238-244. doi: 10.11648/j.ajtas.20200905.17

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@article{10.11648/j.ajtas.20200905.17,
  author = {Yeko Mwanga},
  title = {Arima Forecasting Model for Uganda’s Consumer Price Index},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {9},
  number = {5},
  pages = {238-244},
  doi = {10.11648/j.ajtas.20200905.17},
  url = {https://doi.org/10.11648/j.ajtas.20200905.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20200905.17},
  abstract = {In Uganda, the Central Bank watches closely inflation which happens to be one of the key macroeconomic indicators for which the central bank rate is anchored on. Uganda Bureau of Statistics disseminates monthly Consumer Price Indices (CPIs) to the various stakeholders. Currently, the CPI is computed for eight urban centres spread across the country. The monthly CPIs serve mostly those users who require past and current inflation rates. The main objective of this study is to identify and estimate an ARIMA model for the CPI and use it to make short term forecasts. We relied upon monthly Consumer Price Indices from January 2010 to July 2020 obtained from Uganda Bureau of Statistics. The time series was transformed so as to make it stationary, before identification and estimation of ARIMA (p, d, q) x (P, D, Q)12 models. An ARIMA (1, 1, 1) (0, 1, 1)12 with no constant was selected as the best model, because it had the least AIC and BIC. Additionally, all the coefficients of the ARs and MAs were significant at 1% level. Using the selected model, inflation forecasts were generated for 12 months (August 2020 to July 2021) and found to fluctuate between 4.7 and 6 percent. We recommend this model to Uganda Bureau of Statistics and Central Bank to use it to make forecasts and disseminate them to users. In conclusion, generally good forecasts are vital for better resource allocation, planning and decision making.},
 year = {2020}
}

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