Dette, HolgerVan Hecke, RiaVolgushev, Stanislav2013-10-112013-10-112013-10-11http://hdl.handle.net/2003/3109610.17877/DE290R-10832In a recent paper Noh et al. (2013) proposed a new semiparametric estimate of a regression function with a multivariate predictor, which is based on a specification of the dependence structure between the predictor and the response by means of a parametric copula. This paper investigates the effect which occurs under misspecification of the parametric model. We demonstrate that even for a one or two dimensional predictor the error caused by a \wrong" speci fication of the parametric family is rather severe, if the regression is not monotone in one of the components of the predictor. Moreover, we also show that these problems occur for all of the commonly used copula families and we illustrate in several examples that the copula-based regression may lead to invalid results even when more flexible copula models such as vine copulae (with the common parametric families) are used in the estimation procedure.enDiscussion Paper / SFB 823;39/2013copulaecurse of dimensionalitypairwise copulae,semiparametric inferencevine copulae310330620working paper