| Summary: | A solution to the problem of having to deal with a large number of interrelated explanatory variables within a generalized additive model for location, scale, and shape (GAMLSS) is given here using as an example the Greek-German government bond yield spreads from the 25th of April 2005 to the 31th of March 2010. Those were turbulent financial years, and in order to capture the spreads behaviour, a model has to be able to deal with the complex nature of the financial indicators used to predict the spreads. Fitting a model, using principal components regression of both main and first order interaction terms, for all the parameters of the assumed distribution of the response variable seems to produce promising results.
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