Nonparametric estimation of multivariate multiparameter conditional copulas

Nonparametric estimation of conditional copulas with one parameter has been investigated in Acar et al. (2011). The estimation for multivariate multiparameter conditional copulas, however, has not been considered so far. This paper adopts the local linear smoothing technique and Newton–Raphson metho...

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Bibliographic Details
Published inJournal of the Korean Statistical Society Vol. 46; no. 1; pp. 126 - 136
Main Authors Lin, Jin-Guan, Zhang, Kong-Sheng, Zhao, Yan-Yong
Format Journal Article
LanguageEnglish
Published Singapore Elsevier B.V 01.03.2017
Springer Singapore
한국통계학회
Subjects
Applied Statistics
Bayesian Inference
Calibration function
Conditional copula
Local linear smoothing
Newton–Raphson method
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
통계학
secondary
Conditional copula
Local linear smoothing
Calibration function
Newton–Raphson method
primary
Newton-Raphson method
secondary 62H12
primary 62G08
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Summary:Nonparametric estimation of conditional copulas with one parameter has been investigated in Acar et al. (2011). The estimation for multivariate multiparameter conditional copulas, however, has not been considered so far. This paper adopts the local linear smoothing technique and Newton–Raphson method to estimate those copulas. Under some regularity conditions, the asymptotic normality of the estimators is obtained. Simulation work shows the efficiency of the proposed method. As an application, we analyze a life expectancies data set and show that the conditional t copula outperforms the conditional Clayton, Frank and Gumbel copulas.
ISSN:1226-3192
2005-2863
DOI:10.1016/j.jkss.2016.08.003