Modeling and Forecasting Inflation Dynamics in Nigeria: A Time Series Approach
DOI:
https://doi.org/10.62054/ijdm/0204.04Keywords:
Inflation rate, ARIMA, AIC, Accuracy, Performance, ForecastingAbstract
This study utilized an Auto Regressive Integrated Moving Average (ARIMA) Model to forecast Nigeria’s inflation rate, leveraging monthly data from January 2015 to May 2024 to predict trends from June 2024 to September 2027. The ARIMA model, identified as (3,2,2)(0,0,2)(12) based on the Akaike Information Criteria (AIC), forecasts a persistent upward trend in inflation, with increasing uncertainty over time. The findings suggest that Nigeria’s inflation rate will continue to raise steadily over time next three years, posing significant economic challenges, including decreased purchasing power and unstable economic growth. The study’s results underscore the importance of prudent economic policy making to mitigate the effects of inflation. To manage the inflationary pressures, the study suggests implementing monetary tightening measures, maintaining exchange rate stability, and conducting regular economic monitoring to inform timely policy interventions. The research demonstrates the utility of ARIMA models in informing policy decisions and highlights the need for continued vigilance in economic management. By providing valuable insights into future inflation trends, the study contributes to the development of effective economic policies that promote stability and growth in Nigeria. The study’s findings have implications for policymakers, economists and stakeholders seeking to understand and address the challenges posed by inflation in Nigeria.
References
Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis: forecasting and control. San Francisco: Holden-Day.
Deebom, Z. D., Essi, I. D., & Amos, E. (2021). Evaluating Properties and Performance of Long Memory Models from an Emerging Foreign Markets Return Innovations. Asian Journal of Probability and Statistics 11(4): 1-23.
Iqbal, N., & Naveed, A. (2021). Forecasting Inflation Rate in Pakistan using ARIMA and Functional Time Series Models. Journal of Economic Forecasting 18(2): 112- 128.
Jere, T., & Siyanga, M. (2022). Forecasting Inflation in Zambia Using ARIMA Models. International Journal of Economics and Finance, 14(4): 87-99.
Nyoni, T. (2018). Modeling and Forecasting Inflation in Kenya: An Application of ARIMA and GARCH Models. Journal of Economics Library, 5(2): 126-139.
Nyoni, T., & Nathaniel, S. P. (2019). Time Series Analysis of Inflation in Nigeria using ARMA, ARIMA, and GARCH Models. Journal of Financial and Economic Policy, 11(4): 524-539.
Nyoni, T., & Nathaniel, S. P. (2021). Comparative Analysis of ARIMA and GARCH Models in Forecasting Inflation in Kenya. Journal of Economics and Sustainable Development, 12(3): 50-65.
Otu, A .O., Osuji, G. A., Jude, O., Ifeyinwa, M. H., & Andrew, I. I. (2014). Application of SARIMA models in modeling and forecasting Nigeria’s inflation rates. American Journal of Applied mathematics and Statistics, 2(1):16 – 28.
Stock, J. H., & Watson, M. W. (2008). ‘‘Phillips Curve Inflation Forecasts’’, National Bureau of Economic Research, Working Papers 14322
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Copyright (c) 2025 Nasiru Yakubu, Habeeb R. Abdulrahman, Adamu Abubakar, Ikrimat A. Babando (Author)
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