Mixed data kernel copulas

Jeffrey Racine

Empirical Economics, 2015, vol. 48, issue 1, 37-59

Abstract: A number of approaches toward the kernel estimation of copula have appeared in the literature. Most existing approaches use a manifestation of the copula that requires kernel density estimation of bounded variates lying on a $$d$$ d -dimensional unit hypercube. This gives rise to a number of issues as it requires special treatment of the boundary and possible modifications to bandwidth selection routines, among others. Furthermore, existing kernel-based approaches are restricted to continuous data types only, though there is a growing interest in copula estimation with discrete marginals. We demonstrate that using a simple inversion method can sidestep boundary issues while admitting mixed data types directly thereby extending the reach of kernel copula estimators. Bandwidth selection proceeds by a recently proposed cross-validation method. Furthermore, there is no curse of dimensionality for the kernel-based copula estimator (though there is for the copula density estimator, as is the case for existing kernel copula density methods). Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Nonparametric; Multivariate; Dependence; Discrete and continuous data (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (9)

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Working Paper: Mixed Data Kernel Copulas (2013)
Working Paper: Mixed Data Kernel Copulas (2013)
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DOI: 10.1007/s00181-015-0913-3

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