Beyond simplified pair-copula constructions
- Resource Type
- Authors
- Elif F. Acar; Christian Genest; Johanna Nešlehová
- Source
- Journal of Multivariate Analysis. 110:74-90
- Subject
- Statistics and Probability
Multivariate statistics
Kendall tau rank correlation coefficient
Monte Carlo method
Inference
Conditional copulas
01 natural sciences
Copula (probability theory)
Local likelihood
010104 statistics & probability
Kendall’s tau
0502 economics and business
Pair-copula constructions
Econometrics
Statistics::Methodology
Vines
0101 mathematics
Visual tool
050205 econometrics
Mathematics
Ranks
Numerical Analysis
05 social sciences
Estimator
Data application
Statistics, Probability and Uncertainty
- Language
- ISSN
- 0047-259X
Pair-copula constructions (PCCs) offer great flexibility in modeling multivariate dependence. For inference purposes, however, conditional pair-copulas are often assumed to depend on the conditioning variables only indirectly through the conditional margins. The authors show here that this assumption can be misleading. To assess its validity in trivariate PCCs, they propose a visual tool based on a local likelihood estimator of the conditional copula parameter which does not rely on the simplifying assumption. They establish the consistency of the estimator and assess its performance in finite samples via Monte Carlo simulations. They also provide a real data application.