Optimal prediction with nonstationary ARFIMA model |
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Authors: | Mohamed Boutahar |
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Institution: | GREQAM, and Department of Mathematics, Luminy Faculty of Sciences, Marseille, France |
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Abstract: | 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 © 2007 John Wiley & Sons, Ltd. |
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Keywords: | ARFIMA model long memory nonstationary processes optimal prediction |
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