Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/77353 
Erscheinungsjahr: 
1999
Schriftenreihe/Nr.: 
Technical Report No. 1999,03
Verlag: 
Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, Dortmund
Zusammenfassung: 
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.
Schlagwörter: 
trend differencing
long-range dependence
difference stationarity
fractional ARIMA
BIC
kernel estimation
bandwidth
semiparametric models
forecasting
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
566.55 kB
242.82 kB





Publikationen in EconStor sind urheberrechtlich geschützt.