SEMIFAR models
Loading...
Date
1999
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Universitätsbibliothek Dortmund
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.
Description
Table of contents
Keywords
bandwidth, BIC, difference stationarity, differencing, forecasting, fractional ARIMA, kernel estimation, long-range dependence, semiparametric models, trend