Forecasting Nigeria Inflation Rate Using Autoregressive Integrated Moving Average (ARIMA) Model - Ikrimat, Aliyu; Akobi, Clement; Peter, Pantuvo Tsoke; Gatta, Abdulganiy Abdullahi (Darul Yasin Al Sys, 2025)
This study focuses on forecasting Nigeria's inflation rate using the Autoregressive Integrated Moving Average (ARIMA) model. The research utilizes monthly inflation data from January 2010 to December 2024, obtained from the Central Bank of Nigeria (CBN). The primary objective is to model and forecast inflation trends in Nigeria, which has been experiencing significant inflationary pressures in recent years. The study employs the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests to check for stationarity, revealing that the inflation series becomes stationary after a second differencing (I (2)). The ARIMA (2,2,1) model is identified as the best fit based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), providing a balance between model complexity and predictive accuracy. The model reveals strong autoregressive and moving average dynamics, with significant coefficients for AR (1), AR (2), and MA (1) terms. The forecasted inflation rates for 2025 indicate a steady upward trend, with inflation expected to rise from 35.26% in January to 38.93% by December 2025. The findings highlight the persistent inflationary pressures in Nigeria, driven by factors such as currency depreciation, rising food prices, and energy costs. The study concludes that the ARIMA (2,2,1) model is effective for forecasting Nigeria's inflation rate and recommends that policymakers implement measures to stabilize the economy, including tighter monetary policies, fiscal discipline, and investments in domestic production to mitigate inflationary pressures. Continuous monitoring and timely adjustments to economic policies are also emphasized to address the ongoing challenges posed by inflation. Additionally, the study recommends diversifying the economy to reduce dependence on oil exports, improving agricultural productivity to curb food price volatility, and enhancing data collection methods for more accurate inflation forecasting.