vines (version 1.0.1)

vineGoF: Vine Goodness-of-fit Tests

Description

Goodness-of-fit tests to verify whether the dependence structure of a sample is appropriately modeled by vine model.

Usage

vineGoF(vine, data, method = "PIT", ...)

Arguments

vine

A Vine object.

data

Data matrix of pseudo-observations.

method

Goodness-of-fit method. Supported values: "PIT" (Probability Integral Transform).

...

Additional arguments for the goodness-of-fit method.

Value

• A vineGoF or a subclass with specific information about the goodness-of-fit method used. The statistic slot of this object contains the value of the statistic and pvalue the p-value.

Details

The "PIT" (Probability Integral Transform) method uses the vinePIT function to transform the data into variables which are independent and Uniform(0,1) and then use a hypothesis test to verify whether the resulting variables are independent and Uniform(0,1). The additional parameter statistic specifies the test to be applied for this purpose.

[object Object]

References

Aas, K. and Czado, C. and Frigessi, A. and Bakken, H. (2009) Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44, 182--198.

Breymann, W. and Dias, A. and Embrechts, P. (2003) Dependence structures for multivariate high-frequency data in finance. Quantitative Finance 1, 1--14.

See Also

vineGoF, vinePIT.

Examples

copula <- normalCopula(c(-0.25, -0.21, 0.34, 0.51, -0.07, -0.18), 
                       dispstr = "un", dim = 4)
data <- rCopula(100, copula)

selectCopula <- function (vine, j, i, x, y) {
    data <- cbind(x, y)
    fit <- fitCopula(normalCopula(), data, method = "itau")
    fit@copula
}
normalCVine <- vineFit("CVine", data, method = "ml",
                       selectCopula = selectCopula,
                       optimMethod = "")@vine
normalDVine <- vineFit("DVine", data, method = "ml",
                       selectCopula = selectCopula,
                       optimMethod = "")@vine
show(normalCVine)
show(normalDVine)

normalCVineGof <- vineGoF(normalCVine, data, method = "PIT",
                          statistic = "Breymann")
normalDVineGof <- vineGoF(normalDVine, data, method = "PIT",
                          statistic = "Breymann")
show(normalCVineGof)
show(normalDVineGof)