Difference or not to difference an integrated time series? An empirical investigation

Abstract

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.