Abstract
In applied statistical research the practitioner frequently faces the problem that there neither is clear guidance from grounds of theoretical reasoning nor exists empirical (meta) evidence on the choice of functional form of a tentative regression model. Thus, parametric modeling resulting in a parametric benchmark model may easily miss important features of the data. Using recently advanced ...
Abstract
In applied statistical research the practitioner frequently faces the problem that there neither is clear guidance from grounds of theoretical reasoning nor exists empirical (meta) evidence on the choice of functional form of a tentative regression model. Thus, parametric modeling resulting in a parametric benchmark model may easily miss important features of the data. Using recently advanced nonparametric regression methods we illustrate two powerful techniques to validate a parametric benchmark model. We discuss an empirical example using a well-known data set and provide R code snippets for the implementation of simulations and examples.
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