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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
decision sciences
Semiparametric estimation of conditional copulas
Journal of Multivariate Analysis, Volume 110, Year 2012
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Description
The manner in which two random variables influence one another often depends on covariates. A way to model this dependence is via a conditional copula function. This paper contributes to the study of semiparametric estimation of conditional copulas by starting from a parametric copula function in which the parameter varies with a covariate, and leaving the marginals unspecified. Consequently, the unknown parts in the model are the parameter function and the unknown marginals. The authors use a local pseudo-likelihood with nonparametrically estimated marginals approximating the unknown parameter function locally by a polynomial. Under this general setting, they prove the consistency of the estimators of the parameter function as well as its derivatives; they also establish asymptotic normality. Furthermore, they derive an expression for the theoretical optimal bandwidth and discuss practical bandwidth selection. They illustrate the performance of the estimation procedure with data-driven bandwidth selection via a simulation study and a real-data case. © 2012 Elsevier Inc.
Authors & Co-Authors
Abegaz, Fentaw
Ethiopia, Addis Ababa
Addis Ababa University
Belgium, Heverlee
Leuven Statistics Research Centre
Gijbels, Irène
Belgium, Heverlee
Leuven Statistics Research Centre
Veraverbeke, Noël
Belgium, Hasselt
Universiteit Hasselt
Statistics
Citations: 54
Authors: 3
Affiliations: 3
Identifiers
Doi:
10.1016/j.jmva.2012.04.001
ISSN:
0047259X
e-ISSN:
10957243