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Optimal prediction with nonstationary ARFIMA model

โœ Scribed by Mohamed Boutahar

Publisher
John Wiley and Sons
Year
2007
Tongue
English
Weight
320 KB
Volume
26
Category
Article
ISSN
0277-6693

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โœฆ Synopsis

Abstract

We propose two methods to predict nonstationary longโ€memory time series. In the first one we estimate the longโ€range dependent parameter d by using tapered data; we then take the nonstationary fractional filter to obtain stationary and shortโ€memory time series. In the second method, we take successive differences to obtain a stationary but possibly longโ€memory time series. For the two methods the forecasts are based on those obtained from the stationary components.โ€‰โ€‰Copyright ยฉ 2007 John Wiley & Sons, Ltd.

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