Abstract:
In this thesis, 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 GARMA
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 GARMA model. However,the second
data set that was preserved and used for out-of-sample forecasting suggest that the
3-factor GARMA 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.
