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Biometrika 1993 80(4):817-822; doi:10.1093/biomet/80.4.817
© 1993 by Biometrika Trust
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Fitting long-memory models by generalized linear regression

JAN BERAN

Department of Biostatistics, ISPM, University of Zurich Sumatrastr. 30, 8006 Zurich, Switzerland

There is an increasing awareness of the importance of long-memory models in statistical applications. If long memory is present, it has to be taken into account in order to obtain reliable tests and confidence intervals. One obstacle to using models with long memory in routine statistical analysis has been the lack of easily available and sufficiently versatile statistical software. Here we propose a simple but flexible class of parametric models, which can be used to model such behaviour. We demonstrate that these models can be fitted by generalized linear regression. Standard statistical software packages can be used. A data example is discussed.

Key Words: Fractional arima • Generalized linear models • Long-range dependence • Maximum likelihood estimation


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