Foundations and Trends® in Econometrics > Vol 1 > Issue 1

Copula Modeling: An Introduction for Practitioners

Pravin K. Trivedi, Department of Economics, Indiana University, trivedi@indiana.edu David M. Zimmer, Western Kentucky University, Department of Economics, dmzimmer@gmail.com
 
Suggested Citation
Pravin K. Trivedi and David M. Zimmer (2007), "Copula Modeling: An Introduction for Practitioners", Foundations and TrendsĀ® in Econometrics: Vol. 1: No. 1, pp 1-111. http://dx.doi.org/10.1561/0800000005

Published: 25 Apr 2007
© 2007 P. K. Trivedi and D. M. Zimmer
 
Subjects
Econometric models
 
Keywords
CopulasEconometric modelingEstimation and misspecification
 

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In this article:
1. Introduction
2. Copulas and Dependence
3. Generating Copulas
4. Copula Estimation
5. Conclusion
Appendix A. Copulas and Random Number Generation
References

Abstract

This article explores the copula approach for econometric modeling of joint parametric distributions. Although theoretical foundations of copulas are complex, this paper demonstrates that practical implementation and estimation are relatively straightforward. An attractive feature of parametrically specified copulas is that estimation and inference are based on standard maximum likelihood procedures, and thus copulas can be estimated using desktop econometric software. This represents a substantial advantage of copulas over recently proposed simulation-based approaches to joint modeling.

DOI:10.1561/0800000005
ISBN: 978-1-60198-020-5
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ISBN: 978-1-60198-021-2
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Table of contents:
1. Introduction
2. Copulas and Dependence
3. Generating Copulas
4. Copula Estimation
5. Conclusion
Appendix A. Copulas and Random Number Generation
References

Copula Modeling

Copula Modeling explores the copula approach for econometrics modeling of joint parametric distributions. Copula Modeling demonstrates that practical implementation and estimation is relatively straightforward despite the complexity of its theoretical foundations. An attractive feature of parametrically specific copulas is that estimation and inference are based on standard maximum likelihood procedures. Thus, copulas can be estimated using desktop econometric software. This offers a substantial advantage of copulas over recently proposed simulation-based approaches to joint modeling. Copulas are useful in a variety of modeling situations including financial markets, actuarial science, and microeconometrics modeling.

Copula Modeling provides practitioners and scholars with a useful guide to copula modeling with a focus on estimation and misspecification. The authors cover important theoretical foundations. Throughout, the authors use Monte Carlo experiments and simulations to demonstrate copula properties

 
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