Modeling and Forecasting Inflation in Nigeria: A Time Series Regression with ARIMA Method
Publication Date: 14/08/2023
Author(s): Emwinloghosa Kenneth Guobadia, Pamela Owamagbe Omoruyi, Paschal Nnamdi Izuogu, Eloho Sophia Omodio, Agu Chidera.
Volume/Issue: Volume 6 , Issue 3 (2023)
Abstract:
This study uses time series regression with autoregressive integrated moving average (ARIMA) modeling to establish a model for forecasting inflation in Nigeria for the period 1981-2020. Akaike Information Criterion Corrected (AICC) and Bayesian Information Criterion (BIC) were used to select the best model among competing models. Through these methods, regression with ARIMA (0,0,1) error was selected as the most parsimonious model for inflation forecasting in Nigeria. The results of the out-sample-forecast show that a high inflation rate will be experienced by the end of 2023, and between 2024 and 2030, the inflation rate will be alternating but will maintain a lower rate than that of 2023.
Keywords:
Inflation, Forecasting, Time Series Multiple Regression, Regression with ARIMA Error
