Forecasting a long memory process subject to structural breaks
نویسندگان
چکیده
منابع مشابه
Forecasting Long Memory Processes Subject to Structural Breaks
We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model and suggest using an information criterion (AIC o...
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ژورنال
عنوان ژورنال: Journal of Econometrics
سال: 2013
ISSN: 0304-4076
DOI: 10.1016/j.jeconom.2013.04.006