SEMIFAR models
| 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.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.identifier.uri | http://hdl.handle.net/2003/4919 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-6932 | |
| 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 |