Analyzing Nigeria Monthly Consumer Price Index Using the ARIMA Model
Awogbemi C.A. *
Statistics Programme, National Mathematical Centre, Abuja, Nigeria.
Johnson A.S.
Department of Statistics, Nasarawa State University, Keffi, Nigeria.
Daniel J.
Department of Statistics, Redeemers’ University, Ede, Nigeria.
Ilori, A.K.
Statistics Programme, National Mathematical Centre, Abuja, Nigeria.
Alilu H.
Department of Statistics, Nasarawa State University, Keffi, Nigeria.
Omotayo-Tomo M.S.
Department of Mathematical Sciences, Olusegun Agagu University of Science & Technology, Okitipupa, Nigeria.
Okafor B.I.
Department of Statistics, Federal University of Technology, Owerri, Nigeria.
Huleji B.P.
Department of Statistics, Nasarawa State University, Keffi, Nigeria.
Muhammed I.
Department of Statistics, Usmanu Danfodiyo University, Sokoto, Nigeria.
Paul V.B.
Department of Mathematics, Rivers State University, Port Hartcourt, Nigeria.
Adejumo A.O.
Department of Statistics, University of Ilorin, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Consumer Price Index (CPI) serves as economic vital indicator that helps decision makers to apply the right approach to investment areas in the midst of limited resources. CPI measures the changes in the broad spectrum level of prices pof consumer goods and services that households employ for utilization. This paper seeks to investigate the fluctuations of Nigeria Consumer Price Index from 2009-2024 using an Autoregressive Integrated Moving Average (ARIMA) model to predict future values of Nigeria CPI based on past values. The data was analyzed to make well informed economic decisions as it indicates the change in consumers’ purchasing power. The forecast based on the ARIMA (3,2,2) model shows a continuous upward trend in CPI, with the point forecasts suggesting rising inflation rates throughout 2024 and 2025. The Seasoned Autoregressive Integrated Moving Average, SARIMA (3,2,2) (2,0,0)12 model was identified as the best fitted model for the CPI. The study therefore concluded that the model has been shown to adequately explain the variation in the monthly CPI.
Keywords: ARIMA, consumer price index, stationarity, forecasting, inflation