Optimal prediction with nonstationary ARFIMA model
AbstractWe 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.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.
Volume (Year): 26 (2007)
Issue (Month): 2 ()
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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966
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- Florian Heinen & Philipp Sibbertsen & Robinson Kruse, 2009.
"Forecasting long memory time series under a break in persistence,"
CREATES Research Papers
2009-53, School of Economics and Management, University of Aarhus.
- Heinen, Florian & Sibbertsen, Philipp & Kruse, Robinson, 2009. "Forecasting long memory time series under a break in persistence," Hannover Economic Papers (HEP) dp-433, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
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