IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v11y2024i12p698-713.html
   My bibliography  Save this article

Forecasting Inflation Rate Using the ARIMA Model: Zambia’s Perspective from 2023 to 2043

Author

Listed:
  • Julius Zulu

    (The Bed-Rock Research Consultancy, Department of Research Services, Lusaka, Zambia)

  • Gardner Mwansa

    (Walter Sisulu University, Department of Information Technology, South Africa)

  • Kenny Changwe

    (Kabamba Secondary School, Serenje, Zambia)

Abstract

The study sought to forecast Zambia’s inflation rate from 2023 to 2043 using the Autoregressive Integrated Moving Average Model (ARIMA). Using the Box–Jenkins modeling method, the study utilized 37 yearly time series data from 1986 to 2022 to forecast the next 20 years by using ARIMA Model. The ARIMA (4, 1, 2) model was used as being the one with the most significant parameters, the least log likelihood, Sigma, and the least Akaike and Bayesian information criteria. The ARIMA (4, 1, 2) model was also used due to its accuracy, mathematical soundness, and flexibility, thanks to the inclusion of AR and MA terms over a regression analysis. The results show that the value of the Zambia’s inflation rate is predicted to rise by 47.27% in 20 years.

Suggested Citation

  • Julius Zulu & Gardner Mwansa & Kenny Changwe, 2024. "Forecasting Inflation Rate Using the ARIMA Model: Zambia’s Perspective from 2023 to 2043," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(12), pages 698-713, December.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:12:p:698-713
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-11-issue-12/698-713.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrsi/articles/forecasting-inflation-rate-using-the-arima-model-zambias-perspective-from-2023-to-2043/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    2. Phiri, Andrew, 2013. "Inflation and Economic Growth in Zambia: A Threshold Autoregressive (TAR) Econometric Approach," MPRA Paper 52093, University Library of Munich, Germany.
    3. repec:aer:wpaper:484 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:prg:jnlcfu:v:2022:y:2022:i:1:id:572 is not listed on IDEAS
    2. Laurence T Kell & Iago Mosqueira & Henning Winker & Rishi Sharma & Toshihide Kitakado & Massimiliano Cardinale, 2024. "Empirical validation of integrated stock assessment models to ensuring risk equivalence: A pathway to resilient fisheries management," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-21, July.
    3. Cabral, Joilson de Assis & Freitas Cabral, Maria Viviana de & Pereira Júnior, Amaro Olímpio, 2020. "Elasticity estimation and forecasting: An analysis of residential electricity demand in Brazil," Utilities Policy, Elsevier, vol. 66(C).
    4. Das, Prashant & Füss, Roland & Hanle, Benjamin & Russ, Isabel Nina, 2020. "The cross-over effect of irrational sentiments in housing, commercial property, and stock markets," Journal of Banking & Finance, Elsevier, vol. 114(C).
    5. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    6. Ioannis Badounas & Georgios Pitselis, 2020. "Loss Reserving Estimation With Correlated Run-Off Triangles in a Quantile Longitudinal Model," Risks, MDPI, vol. 8(1), pages 1-26, February.
    7. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    8. Michael Kostmann & Wolfgang K. Härdle, 2019. "Forecasting in Blockchain-Based Local Energy Markets," Energies, MDPI, vol. 12(14), pages 1-27, July.
    9. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    10. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.
    11. Bloom, David E. & Canning, David & Fink, Gunther & Finlay, Jocelyn E., 2007. "Does age structure forecast economic growth?," International Journal of Forecasting, Elsevier, vol. 23(4), pages 569-585.
    12. Mengyang Wang & Hui Wang & Jiao Wang & Hongwei Liu & Rui Lu & Tongqing Duan & Xiaowen Gong & Siyuan Feng & Yuanyuan Liu & Zhuang Cui & Changping Li & Jun Ma, 2019. "A novel model for malaria prediction based on ensemble algorithms," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
    13. Taleb, Nassim Nicholas, 2020. "On the statistical differences between binary forecasts and real-world payoffs," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1228-1240.
    14. Olivares, Kin G. & Meetei, O. Nganba & Ma, Ruijun & Reddy, Rohan & Cao, Mengfei & Dicker, Lee, 2024. "Probabilistic hierarchical forecasting with deep Poisson mixtures," International Journal of Forecasting, Elsevier, vol. 40(2), pages 470-489.
    15. Marco Zanotti, 2025. "On the stability of global forecasting models," Working Papers 553, University of Milano-Bicocca, Department of Economics.
    16. Marcelo Bourguignon, 2016. "Poisson–geometric INAR(1) process for modeling count time series with overdispersion," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(3), pages 176-192, August.
    17. Dima, Bogdan & Dima, Ştefana Maria & Ioan, Roxana, 2025. "The short-run impact of investor expectations’ past volatility on current predictions: The case of VIX," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 98(C).
    18. Ling Tang & Chengyuan Zhang & Tingfei Li & Ling Li, 2021. "A novel BEMD-based method for forecasting tourist volume with search engine data," Tourism Economics, , vol. 27(5), pages 1015-1038, August.
    19. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    20. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    21. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:11:y:2024:i:12:p:698-713. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.