Modeling and Forecasting the Consumer Price Index (CPI) in Nigeria during COVID-19 Pandemic: A Comparative Analysis of Econometric Models - Agboluaje, Ayodele Abraham; Etuk, Iniabasi Emmanuel; Joseph, Elijah (Science and Technology: Developments and Applications Vol. 3, 2025)
Consumer Price Index is considered in this study since it is regarded as the chief indicator of inflationary change, and investors and others in the economies of the nation also consider the CPI to be the best gauge of inflation available. This study models the Nigeria Consumer Price Index (CPI) and forecasts its accuracy with the COVID-19 impact on the data. The CPI data is homoscedastic and heteroscedastic in nature. The CPI of a society is considered as the best gauge of inflation and it is heteroscedastic in nature when estimated. The models Autoregressive (AR) Autoregressive Conditional Heteroscedastic (ARCH), Generalized Autoregressive Conditional Heteroscedastic (GARCH), Threshold GARCH, Exponential GARCH and Power ARCH are used for comparing and forecasting when estimating. The comparison of the models revealed that AR has the model of best fit according to the minimum information criteria. The dynamic forecast evaluation reveals that GARCH-N has the minimum forecast residuals in the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the AR model has the minimum Mean Absolute Percentage Error (MAPE) which reveals the minimum forecast accuracy among the models. AR has the minimum forecast residuals on average. Employing the AR model for modeling and forecasting Nigeria's CPI which results in a low inflation rate when compared with the results of other models considered. AR can improve the economy of the society and this will assist in policy decision making. The future work will focus on improving Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for more accurate forecasting.