Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Beran, Jan | de |
dc.contributor.author | Feng, Yuanhua | de |
dc.contributor.author | Ocker, Dirk | de |
dc.date.accessioned | 2004-12-06T18:39:40Z | - |
dc.date.available | 2004-12-06T18:39:40Z | - |
dc.date.issued | 1999 | de |
dc.identifier.uri | http://hdl.handle.net/2003/4919 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-6932 | - |
dc.description.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. | en |
dc.format.extent | 237934 bytes | - |
dc.format.extent | 580150 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | application/postscript | - |
dc.language.iso | en | de |
dc.publisher | Universitätsbibliothek Dortmund | de |
dc.subject | bandwidth | en |
dc.subject | BIC | de |
dc.subject | difference stationarity | en |
dc.subject | differencing | en |
dc.subject | forecasting | en |
dc.subject | fractional ARIMA | en |
dc.subject | kernel estimation | en |
dc.subject | long-range dependence | en |
dc.subject | semiparametric models | en |
dc.subject | trend | en |
dc.subject.ddc | 310 | de |
dc.title | SEMIFAR models | en |
dc.type | Text | de |
dc.type.publicationtype | report | en |
dcterms.accessRights | open access | - |
Appears in Collections: | Sonderforschungsbereich (SFB) 475 |
Files in This Item:
File | Description | Size | Format | |
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99_03.pdf | DNB | 232.36 kB | Adobe PDF | View/Open |
tr03-99.ps | 566.55 kB | Postscript | View/Open |
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