Is there any goodness of fit tests like those based on probability integral transform (PIT) of Rosenblatt available for Vine copulas as a built in function in R?
I know we can use gofCopula from copula package for bivariate, but how can we do it for higher dimensions or Vine copulas?
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The VineCopula package offers the function RVineGofTest:
"This function performs a goodness-of-fit test for R-vine copula models. There are 15 different goodness-of-fit tests implemented, described in Schepsmeier (2013)."
See the help page for further details.
Schepsmeier, U. (2013) A goodness-of-fit test for regular vine copula models. Preprint http://arxiv.org/abs/1306.0818
Schepsmeier, U. (2013) Efficient goodness-of-fit tests in multi-dimensional vine copula models. http://arxiv.org/abs/1309.5808
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$\begingroup$ does the goodness of fit of vine copula deteriorate as number of covariates increases from 5 to say 1,000? I have yet to see an article that uses vine copula for a 10+ variable dataset, I guess because even at that stage there are already so many conditional copula combinations to estimate? Is this why vine copula have not caught on $\endgroup$ Commented Sep 14, 2020 at 23:27