Approximate Bayesian Computation for Copula Estimation

Clara Grazian () and Brunero Liseo ()
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Clara Grazian: Università di Roma "La Sapienza", Italy
Brunero Liseo: Università di Roma "La Sapienza", Italy

Statistica, 2015, vol. 75, issue 1, 111-127

Abstract: We describe a simple method for making inference on a functional of a multivariate distribution. The method is based on a copula representation of the multivariate distribution and it is based on the properties of an Approximate Bayesian Monte\,Carlo algorithm, where the proposed values of the functional of interest are weighed in terms of their empirical likelihood. This method is particularly useful when the 'true' likelihood function associated with the working model is too costly to evaluate or when the working model is only partially specified.

Keywords: ABC algorithms; Multivariate distributions; Partially specified models (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:75:y:2015:i:1:p:111-127

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