Abstract
Consider a random sample from a continuous multivariate distribution function
F with copula C. In order to test the null hypothesis that C belongs to a certain
parametric family, we construct an under H0 asymptotically distribution-free process that serves as a tests generator. The process is a transformation of the difference of a semi-parametric and a parametric estimator of C. This transformed empirical process converges weakly to a standard multivariate Wiener process, paving the way for a multitude of powerful asymptotically distribution-free goodness-of-t tests for copula families. We investigate the finite-sample performance of our approach through a simulation study and illustrate its applicability with a data analysis.
F with copula C. In order to test the null hypothesis that C belongs to a certain
parametric family, we construct an under H0 asymptotically distribution-free process that serves as a tests generator. The process is a transformation of the difference of a semi-parametric and a parametric estimator of C. This transformed empirical process converges weakly to a standard multivariate Wiener process, paving the way for a multitude of powerful asymptotically distribution-free goodness-of-t tests for copula families. We investigate the finite-sample performance of our approach through a simulation study and illustrate its applicability with a data analysis.
Original language | English |
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Place of Publication | Tilburg |
Publisher | CentER, Center for Economic Research |
Number of pages | 33 |
Volume | 2017-052 |
Publication status | Published - 11 Dec 2017 |
Publication series
Name | CentER Discussion Paper |
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Volume | 2017-052 |
Keywords
- Khmaladze transform,
- copula estimation
- empirical process