gofCopula: Goodness-of-fit tests for copulas

Description

Goodness-of-fit tests for copulas based on the empirical process comparing the empirical copula with a parametric estimate of the copula derived under the null hypothesis. The test statistic is the Cramer-von Mises functional Sn defined in equation (2) of Genest, Remillard and Beaudoin (2009). Approximate p-values for the test statistic can be obtained either using the parametric bootstrap (see the two first references) or by means of a fast multiplier approach (see the two last references).

Usage

gofCopula(copula, x, N = 1000, method = "mpl",
          simulation = c("pb", "mult"), print.every = 100,
          optim.method = "BFGS", optim.control = list(maxit=20))

Arguments

copula

object of class "copula" representing the hypothesized copula family.

a data matrix that will be transformed to pseudo-observations.

number of bootstrap or multiplier iterations to be used to simulate realizations of the test statistic under the null hypothesis.

method

estimation method to be used to estimate the dependence parameter(s); can be either "mpl" (maximum pseudo-likelihood), "itau" (inversion of Kendall's tau) or "irho" (inversion of Spearman's rho).

simulation

simulation method for generating realizations of the test statistic under the null hypothesis; can be either "pb" (parametric bootstrap) or "mult" (multiplier).

print.every

progress is printed every "print.every" iterations. No progress is printed if it is nonpositive.

optim.method, optim.control

the method and control arguments for optim(), see there.

Value

An object of class "gofCopula" which is a list with components
statisticvalue of the test statistic.
pvaluecorresponding approximate p-value.
parametersestimates of the parameters for the hypothesized copula family.

Details

If the parametric bootstrap is used, the dependence parameters of the hypothesized copula family can be estimated either by maximizing the pseudo-likelihood or by inverting Kendall's tau or Spearman's rho. If the multiplier is used, any estimation method can be used in the bivariate case, but only maximum pseudo-likelihood estimation can be used in the multivariate (multiparameter) case.

For the normal and t copulas, several dependence structures can be hypothesized: "ex" for exchangeable, "ar1" for AR(1), "toep" for Toeplitz, and "un" for unstructured (see ellipCopula). For the t copula, "df.fixed" has to be set to TRUE, which implies that the degrees of freedom are not considered as a parameter to be estimated.

Thus far, the multiplier approach is implemented for six copula families: the Clayton, Gumbel, Frank, Plackett, normal and t.

References

C. Genest and B. Remillard (2008). Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models. Annales de l'Institut Henri Poincare: Probabilites et Statistiques 44, 1096--1127.

C. Genest, B. Remillard and D. Beaudoin (2009). Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and Economics 44, 199--214.

I. Kojadinovic, J. Yan and M. Holmes (2011). Fast large-sample goodness-of-fit tests for copulas. Statistica Sinica 21, 841--871.

I. Kojadinovic and J. Yan (2011). A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems. Statistics and Computing 21, 17--30.

I. Kojadinovic and J. Yan (2010). Modeling Multivariate Distributions with Continuous Margins Using the copula R Package. Journal of Statistical Software 34(9), 1--20. http://www.jstatsoft.org/v34/i09/.

Examples

Run this code

## the following example is available in batch through
## demo(gofCopula)
## A two-dimensional data example ----------------------------------
x <- rcopula(claytonCopula(3), 200)

## Does the Gumbel family seem to be a good choice?
gofCopula(gumbelCopula(1), x)
## What about the Clayton family?
gofCopula(claytonCopula(1), x)

## The same with a different estimation method
gofCopula(gumbelCopula (1), x, method="itau")
gofCopula(claytonCopula(1), x, method="itau")


## A three-dimensional example  ------------------------------------
x <- rcopula(tCopula(c(0.5, 0.6, 0.7), dim = 3, dispstr = "un"),
             200)

## Does the Clayton family seem to be a good choice?
gofCopula(gumbelCopula(1, dim = 3), x)
## What about the t copula?
t.copula <- tCopula(rep(0, 3), dim = 3, dispstr = "un", df.fixed=TRUE)
## this is *VERY* slow currentlygofCopula(t.copula, x)

## The same with a different estimation method
gofCopula(gumbelCopula(1, dim = 3), x, method="itau")
gofCopula(t.copula,                 x, method="itau")

## The same using the multiplier approach
gofCopula(gumbelCopula(1, dim = 3), x, simulation="mult")
gofCopula(t.copula,                 x, simulation="mult")

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