Authors: Beran, Jan
Feng, Yuanhua
Ocker, Dirk
Title: SEMIFAR models
Language (ISO): en
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.
Subject Headings: bandwidth
BIC
difference stationarity
differencing
forecasting
fractional ARIMA
kernel estimation
long-range dependence
semiparametric models
trend
URI: http://hdl.handle.net/2003/4919
http://dx.doi.org/10.17877/DE290R-6932
Issue Date: 1999
Publisher: Universitätsbibliothek Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

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