Optimal prediction with nonstationary ARFIMA model
We propose two methods to predict nonstationary long-memory time series. In the first one we estimate the long-range dependent parameter d by using tapered data; we then take the nonstationary fractional filter to obtain stationary and short-memory time series. In the second method, we take successive differences to obtain a stationary but possibly long-memory time series. For the two methods the forecasts are based on those obtained from the stationary components. Copyright Â© 2007 John Wiley & Sons, Ltd.
Volume (Year): 26 (2007)
Issue (Month): 2 ()
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