The COPULA Procedure (Experimental)

Overview: COPULA Procedure

A multivariate distribution for a random vector contains a description of both the marginal distributions and their dependence structure. A copula approach to formulating a multivariate distribution provides a way to isolate the description of the dependence structure from the marginal distributions. A copula is a function that combines marginal distributions of variables into a specific multivariate distribution. All of the one-dimensional marginals in the multivariate distribution are the cumulative distribution functions of the factors. Copulas help perform large-scale multivariate simulation from separate models, each of which can be fitted using different, even nonnormal, distributional specifications.

The COPULA procedure enables you to fit multivariate distributions or copulas from a given sample data set. You can do the following:

  • estimate the parameters for a specified copula type

  • simulate a given copula

  • plot dependent relationships among the variables

The following types of copulas are supported:

  • normal copula

  • $t$ copula

  • Clayton copula

  • Gumbel copula

  • Frank copula