Fitting long-memory models by generalized linear regression

Abstract

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.