Mixed Data Kernel Copulas

Jeffrey Racine

Department of Economics Working Papers from McMaster University

Abstract: A number of approaches towards 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-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 date types only, though there is a growing interest in copula estimation with discrete marginals (see e.g. Smith & Khaled (2012) for a Bayesian approach). We demonstrate that using a simple inversion method (cf Nelsen (2006), Fermanian & Scaillet (2003)) can sidestep boundary issues while admitting mixed data types directly thereby extending the reach of kernel copula estimators. Bandwidth selection proceeds by the recently proposed method of Li & Racine (2013). 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).

Pages: 21 pages
Date: 2013-08
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Mixed data kernel copulas (2015)
Working Paper: Mixed Data Kernel Copulas (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:mcm:deptwp:2013-12

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