Conditions for the Asymptotic Semiparametric Efficiency of an Omnibus Estimator of Dependence Parame

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Selection of Vine Copulas (2013, background): “of parameters to be estimated may however be too large, so that one typically either uses empirical distribution functions for the margins as proposed by [25, 26] or estimates parameters of the marginal distributions in a first step and then fixes”
Modelling the statistical dependence of rainfall event variables through copula functions (2011, methodology): “In general this estimator is less computationally intensive than the previous ones but is not efficient, except for some particular cases (Genest and Werker, 2002).”
Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask (2007, unknown): “Circumstances under which the latter approach is asymptotically semiparametrically efficient were delineated by Klaassen and Wellner 1997 and by Genest and Werker 2002 .”
Efficient Estimation of Semiparametric Multivariate Copula Models (2006, unknown): “Except for a few special cases, the two-step estimator of the copula parameter θ o is inefficient in general (see Genest and Werker 2002).”
Efficient Estimation of Copula-Based Semiparametric Markov Models (2009, background): “However, as pointed out by Genest and Werker (2002), Gaussian copula and the independence copula are the only two copulas for which the two-step semiparametric estimator is efficient for α0.”