SEMIFAR models

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Date

1999

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.

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Keywords

bandwidth, BIC, difference stationarity, differencing, forecasting, fractional ARIMA, kernel estimation, long-range dependence, semiparametric models, trend

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