  It has been shown that some macroeconomic time series, especially those where
outliers could be present, can be well modelled using heavy tailed
distributions for the noise components. Methods for deciding when and where
heavy-tailed models should be preferred are investigated. These investigations
primarily focus on automatic methods for model identification and selection.
Current methods are extended to incorporate a non-Gaussian selection element,
and various different criteria for deciding on which overall model should be
used are examined.
