EconPapers    
Economics at your fingertips  
 

On the estimation of nested Archimedean copulas: a theoretical and an experimental comparison

Nathan Uyttendaele ()
Additional contact information
Nathan Uyttendaele: Université catholique de Louvain

Computational Statistics, 2018, vol. 33, issue 2, No 21, 1047-1070

Abstract: Abstract A lot of progress regarding estimation of nested Archimedean copulas has been booked since their introduction by Joe (Multivariate models and dependence concepts. Chapman and Hall, London, 1997). The currently published procedures can be seen as particular cases of two different, more general, approaches. In the first approach, the tree structure of the target nested Archimedean copulas is estimated using hierarchical clustering to get a binary tree, and then parts of this binary tree are collapsed according to some strategy. This two-step estimation of the tree structure paves the way for estimation of the generators according to the sufficient nesting condition afterwards, this sufficient nesting condition on the generators ensuring the resulting estimated nested Archimedean copula is a proper copula. In contrast to the first approach, the second approach estimates the tree structure free of any concern for the generators. While this is the main strength of this second approach, it is also its main weakness: estimation of the generators afterwards so that the resulting nested Archimedean copula is a proper copula still lacks a solution. In this paper, both approaches are formally explored, detailed explanations and examples are given, as well as results from a performance study where a new way of comparing tree structure estimators is offered. A nested Archimedean copula is also estimated based on exams results from 482 students, and a naive attempt to check the fit is made using principal component analysis.

Keywords: Hierarchical Archimedean copulas; Estimation; Hierarchical clustering; Rooted tree; Structure determination; Kendall’s tau; Phylogenetics (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00180-017-0743-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0743-1

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-017-0743-1

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0743-1