Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/77353
Authors: 
Beran, Jan
Feng, Yuanhua
Ocker, Dirk
Year of Publication: 
1999
Series/Report no.: 
Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 1999,03
Abstract: 
Recent results on so-called SEMIFAR models introduced by Beran (1997) are discussed. The nonparametric deterministic trend is estimated by a kernel method. The differencing and fractional differencing parameters as well as the autoregressive coefficients are estimated by an approximate maximum likelihood approach. A data-driven algorithm for estimating the whole model is proposed based on the iterative plug-in idea for selecting bandwidth in nonparametric regression with long-memory. Prediction for SEMIFAR models is also discussed briefly. Two examples illustrate the potential usefulness of these models in practice.
Subjects: 
trend differencing
long-range dependence
difference stationarity
fractional ARIMA
BIC
kernel estimation
bandwidth
semiparametric models
forecasting
Document Type: 
Working Paper

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