Shittu, O.I., Department of Statistics, University of Ibadan, Nigeria; Yaya, O.S., Department of Statistics, University of Ibadan, Nigeria
The classical approach to modelling economic series is to apply the Box - Jenkins approach of ARMA or ARIMA depending on whether the series is stationary or non- stationary. If such series exhibits long memory property, forecast values based on ARIMA model may not be reliable. This studies therefore focused on measuring forecast performance of ARIMA(p,d,q) and ARFIMA (p,d,q) models for stationary type series that exhibit Long memory properties. The UK Pound/US Dollar exchange rate data were analysed by OX 5.1 package using the Root mean Square forecast Error (RMSFE) and Mean Absolute Percentage Forecast Error (MAPFE) as measurement criteria. The ARFIMA model was found to be better than ARMA model as indicated by model diagnostic tools. The estimated forecast values from ARFIMA model is more realistic and closely reflect the current economic reality in the two countries as indicated by the forecast evaluation tools. The results are in agreement with Kwiatkowski et.al.(1992) and Boutahar, M. (2008). © EuroJournals Publishing, Inc. 2009.
