This paper uses the gross domestic product growth rates of Malaysia,
Thailand, Indonesia and China in an empirical examination to
determine whether an integrated time series should be differenced
before it is used for forecasting. The results reveal that Mallows model
combination (M.M.A.) of original and differenced series is a better
choice than just differencing the series only if the perturbation
instability measure is more than 1.25 for autoregressive (A.R.) model,
and 1.105 for moving average (M.A.) model and autoregressive
fractional integrated moving average (A.R.F.I.M.A.) model.
Furthermore, it is found that M.M.A. performs better in forecasting
with better model stability for the case of M.A. and A.R.F.I.M.A. than
A.R. However, M.M.A. is very sensitive in financial crisis
