Comparison of semiparametric and parametric methods for estimating copulas
Computational Statistics & Data Analysis
Generating random correlation matrices based on partial correlations
Journal of Multivariate Analysis
Construction of asymmetric multivariate copulas
Journal of Multivariate Analysis
Testing for equality between two copulas
Journal of Multivariate Analysis
Semiparametric multivariate density estimation for positive data using copulas
Computational Statistics & Data Analysis
Estimation and inference for dependence in multivariate data
Journal of Multivariate Analysis
Efficient maximum likelihood estimation of copula based meta t-distributions
Computational Statistics & Data Analysis
Semiparametric bivariate Archimedean copulas
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Pattern Recognition
Efficient Bayesian inference for stochastic time-varying copula models
Computational Statistics & Data Analysis
A review of copula models for economic time series
Journal of Multivariate Analysis
Comparison of estimators for pair-copula constructions
Journal of Multivariate Analysis
Modelling multi-output stochastic frontiers using copulas
Computational Statistics & Data Analysis
Vine copulas with asymmetric tail dependence and applications to financial return data
Computational Statistics & Data Analysis
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For multivariate copula-based models for which maximum likelihood is computationally difficult, a two-stage estimation procedure has been proposed previously; the first stage involves maximum likelihood from univariate margins, and the second stage involves maximum likelihood of the dependence parameters with the univariate parameters held fixed from the first stage. Using the theory of inference functions, a partitioned matrix in a form amenable to analysis is obtained for the asymptotic covariance matrix of the two-stage estimator. The asymptotic relative efficiency of the two-stage estimation procedure compared with maximum likelihood estimation is studied. Analysis of the limiting cases of the independence copula and Frechet upper bound help to determine common patterns in the efficiency as the dependence in the model increases. For the Frechet upper bound, the two-stage estimation procedure can sometimes be equivalent to maximum likelihood estimation for the univariate parameters. Numerical results are shown for some models, including multivariate ordinal probit and bivariate extreme value distributions, to indicate the typical level of asymptotic efficiency for discrete and continuous data.