Skip to Main Content
A note on a simplified and general approach to simulating from multivariate copula functionsAuthor(s): Barry K. Goodwin
Source: Applied Economics Letters 20(9):910-915
Publication Series: Scientific Journal (JRNL)
Station: Southern Research Station
PDF: View PDF (458.08 KB)
DescriptionCopulas have become an important analytic tool for characterizing multivariate distributions and dependence. One is often interested in simulating data from copula estimates. The process can be analytically and computationally complex and usually involves steps that are unique to a given parametric copula. We describe an alternative approach that uses ‘Probability-Proportional-to-Size’ random sampling with weights formed from the copula likelihood function. The method is flexible and can be applied to parametric and nonparametric marginal density estimates. The precision of the simulation can be calibrated by adjusting the density of the multidimensional grid used in the simulation process. The approach is fully transparent to any copula function with continuous random variables. An example evaluates a number of goodness-of-fit criteria and provides strong support for the validity and practicality of the method.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationGoodwin, Barry K. 2013. A note on a simplified and general approach to simulating from multivariate copula functions. Applied Economics Letters 20(9):910-915.
Keywordscopulas, simulation, nonparametric marginal densities
- A Simpli#12;ed, General Approach to Simulating from Multivariate Copula Functions
- Moment and maximum likelihood estimators for Weibull distributions under length- and area-biased sampling
- Predicting Tree Mortality From Diameter Growth: A Comparison of Maximum Likelihood and Bayesian Approaches
XML: View XML