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 25 April 2005 to 31 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.

此处以 2005 年 4 月 25 日的希腊-德国政府债券收益率差为例，给出了必须在位置、规模和形状的广义加性模型 (GAMLSS) 中处理大量相互关联的解释变量的问题的解决方案到 2010 年 3 月 31 日。那是动荡的财政年度，为了捕捉利差行为，模型必须能够处理用于预测利差的财务指标的复杂性。使用主要和一阶交互项的主成分回归拟合模型，对于响应变量的假定分布的所有参数似乎产生了有希望的结果。