VineCopula (version 1.6-1)

RVineStdError: Standard Errors of an R-Vine Copula Model

Description

This function calculates the standard errors of a d-dimensional R-vine copula model given the Hessian matrix.

Usage

RVineStdError(hessian, RVM)

Arguments

hessian

The Hessian matrix of the given R-vine.

RVM

An RVineMatrix object including the structure, the pair-copula families, and the parameters.

Value

• se
  The calculated standard errors for the first parameter matrix. The entries are ordered with respect to the ordering of the RVM$par matrix.
• se2
  The calculated standard errors for the second parameter matrix.

References

Dissmann, J. F., E. C. Brechmann, C. Czado, and D. Kurowicka (2013). Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis, 59 (1), 52-69. Schepsmeier, U. and J. Stoeber (2012). Derivatives and Fisher information of bivariate copulas. Submitted for publication. http://mediatum.ub.tum.de/node?id=1106541. Stoeber, J. and U. Schepsmeier (2012). Is there significant time-variation in multivariate copulas? Submitted for publication. http://arxiv.org/abs/1205.4841.

See Also

BiCopDeriv, BiCopDeriv2, BiCopHfuncDeriv, BiCopHfuncDeriv2, RVineMatrix, RVineHessian, RVineGrad

Examples

# define 5-dimensional R-vine tree structure matrix
Matrix <- c(5, 2, 3, 1, 4,
            0, 2, 3, 4, 1,
            0, 0, 3, 4, 1,
            0, 0, 0, 4, 1,
            0, 0, 0, 0, 1)
Matrix <- matrix(Matrix, 5, 5)

# define R-vine pair-copula family matrix
family <- c(0, 1, 3, 4, 4,
            0, 0, 3, 4, 1,
            0, 0, 0, 4, 1,
            0, 0, 0, 0, 3,
            0, 0, 0, 0, 0)
family <- matrix(family, 5, 5)

# define R-vine pair-copula parameter matrix
par <- c(0, 0.2, 0.9, 1.5, 3.9,
         0, 0, 1.1, 1.6, 0.9,
         0, 0, 0, 1.9, 0.5,
         0, 0, 0, 0, 4.8,
         0, 0, 0, 0, 0)
par <- matrix(par, 5, 5)

# define second R-vine pair-copula parameter matrix
par2 <- matrix(0, 5, 5)

# define RVineMatrix object
RVM <- RVineMatrix(Matrix = Matrix, family = family,
                   par = par, par2 = par2,
                   names = c("V1", "V2", "V3", "V4", "V5"))

# simulate a sample of size 300 from the R-vine copula model
set.seed(123)
simdata <- RVineSim(300, RVM)

# compute the Hessian matrix of the first row of the data
out2 <- RVineHessian(simdata,RVM)

# get the standard errors
RVineStdError(out2$hessian, RVM)