SEMIFAR models - Jan Beran; Yuanhua Feng; Dirk Ocker (Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen, 1999)
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.
Keywords: 519; trend differencing; long-range dependence; difference stationarity; fractional ARIMA; BIC; kernel estimation; bandwidth; semiparametric models; forecasting; Nichtparametrisches Verfahren; Zeitreihenanalyse; Theorie; ARMA-Modell