  We propose a type of generalised additive models with of model components
based on pair-copula constructions, with prediction as a main aim. The model
components are designed such that our model may capture potentially complex
interaction effects in the relationship between the response covariates. In
addition, our model does not require discretisation of continuous covariates,
and is therefore suitable for problems with many such covariates. Further, we
have designed a fitting algorithm inspired by gradient boosting, as well as
efficient procedures for model selection and evaluation of the model
components, through constraints on the model space and approximations, that
speed up time-costly computations. In addition to being absolutely necessary
for our model to be a realistic alternative in higher dimensions, these
techniques may also be useful as a basis for designing efficient models
selection algorithms for other types of copula regression models. We have
explored the characteristics of our method in a simulation study, in particular
comparing it to natural alternatives, such as logic regression, classic
boosting models and penalised logistic regression. We have also illustrated our
approach on the Wisconsin breast cancer dataset and on the Boston housing
dataset. The results show that our method has a prediction performance that is
either better than or comparable to the other methods, even when the proportion
of discrete covariates is high.
