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 © 2007 John Wiley & Sons, Ltd.
Volume (Year): 26 (2007)
Issue (Month): 2 ()
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References listed on IDEAS
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- Velasco, Carlos, 1999.
"Non-stationary log-periodogram regression,"
Journal of Econometrics,
Elsevier, vol. 91(2), pages 325-371, August.
- Velasco, Carlos, 1998. "Non-stationary log-periodogram regression," DES - Working Papers. Statistics and Econometrics. WS 4554, Universidad Carlos III de Madrid. Departamento de Estadística.
- Peter M. Robinson & Carlos Velasco, 2000. "Whittle pseudo-maximum likelihood estimation for nonstationary time series," LSE Research Online Documents on Economics 2273, London School of Economics and Political Science, LSE Library. Full references (including those not matched with items on IDEAS)