  Copula modelling has in the past decade become a standard tool in many areas
of applied statistics. However, a largely neglected aspect concerns the design
of related experiments. Particularly the issue of whether the estimation of
copula parameters can be enhanced by optimizing experimental conditions and how
robust all the parameter estimates for the model are with respect to the type
of copula employed. In this paper an equivalence theorem for (bivariate) copula
models is provided that allows formulation of efficient design algorithms and
quick checks of whether designs are optimal or at least efficient. Some
examples illustrate that in practical situations considerable gains in design
efficiency can be achieved. A natural comparison between different copula
models with respect to design efficiency is provided as well.
