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Goodness-of-fit testing for copulas: A distribution-free approach
Can,S.U. Einmahl,John Laeven,R.J.A.
Can,S.U.
Einmahl,John
Laeven,R.J.A.
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 empirical process on the unit hypercube that converges weakly to a standard Wiener process under the null hypothesis. This process can therefore serve as a ‘tests generator’ for asymptotically distribution-free goodness-of-fit testing of copula families. We also prove maximal sensitivity of this process to contiguous alternatives. Finally, we demonstrate through a Monte Carlo simulation study that our approach has excellent finite-sample performance, and we illustrate its applicability with a data analysis.
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Publisher Copyright: © 2020 ISI/BS.
Date
2020-11-02
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1710.11504_1_.pdf
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Keywords
Copula, goodness-of-fit, distribution-free, semi-parametric estimation, Monte Carlo simulation
Citation
Can, S U, Einmahl, J & Laeven, R J A 2020, 'Goodness-of-fit testing for copulas: A distribution-free approach', Bernoulli, vol. 26, no. 4, pp. 3163-3190. https://doi.org/10.3150/20-BEJ1219