Modeling and Forecasting Gambia’s Inflation Rates

Abstract

In this paper, we examine the most appropriate method for modeling and forecasting Gambia's inflation rates. We investigate the statistical properties of the inflation data and specify two models namely seasonal autoregressive integrated moving average (SARIMA) and k-factor Gegenbauer Autoregressive Moving Average (k-factor GARMA). The first model seasonal ARIMA(1, 1, 1)(0, 0, 1) 12 was selected using the H-K Algorithm developed by Hyndman and Khandakar (2008) and 3-factor GARM A from both the spectral density graph and further analysis of the residuals from the 3-factor Gegenbauer model. The in-sample characteristics such as the Akaike Criterion and Schwarz Criterion following estimation using the first data set show that the ARIMA(1, 1, 1)(0, 0, 1) 12 outperforms the 3-factor GARM A model. However, the second data set that was preserved and used for out-of-sample forecasting suggest that the 3-factor GARM A model outperforms the seasonal ARIMA(1, 1, 1)(0, 0, 1) 12 model in out-of -sample forecasting. Our results indicated that inflation in Gambia is stationary with long-memory behavior at three distinct frequencies. We also found that the k-factor GARMA outperforms the seasonal ARIMA in out-sample forecasting which may be ascribed to the forecast horizon been large and series strongly long-range dependent.

Młodzi Naukowcy 2.0

“…The rise in inflation causes an increase in the level of inflation uncertainty in the economy. (Manjang et al, 2014) Inflation forecasting based on the phenomenon of autoregression.…”