The new experimental COPULA procedure enables you to simulate realizations or estimate parameters of multivariate distributions by using the copula approach. This approach is based on the fact that a typical multivariate distribution contains information about both the marginal behavior of individual random variables and also about the dependence structure between them. The COPULA procedure enables you to decouple these two effects and model the dependence structure of random variables by linking their cumulative distribution function (CDF) to a vector of their marginal CDFs as described by the Sklar’s Theorem.
The COPULA procedure supports the following types of distributions:
normal distribution
t distribution
Clayton distribution
Gumbel distribution
Frank distribution
The COPULA procedure can both estimate the parameters of copula models from data by using maximum likelihood and simulate random data from copula distributions by using either estimated or specified model parameters. The FIT statement is used for model estimation, and the SIMULATE statement is used for simulation. The PLOTS option in the FIT or SIMULATE statement provides various ODS Graphics plots that help you analyze the underlying data.