RVineCopSelect: Sequential Pair-Copula Selection and Estimation for R-Vine...

In VineCopula: Statistical Inference of Vine Copulas

Sequential Pair-Copula Selection and Estimation for R-Vine Copula Models

Description

This function fits a R-vine copula model to a d-dimensional copula data set. Pair-copula families are selected using BiCopSelect() and estimated sequentially.

Usage

RVineCopSelect(
  data,
  familyset = NA,
  Matrix,
  selectioncrit = "AIC",
  indeptest = FALSE,
  level = 0.05,
  trunclevel = NA,
  weights = NA,
  rotations = TRUE,
  se = FALSE,
  presel = TRUE,
  method = "mle",
  cores = 1
)

Arguments

data	N x d data matrix (with uniform margins).
familyset	integer vector of pair-copula families to select from. The vector has to include at least one pair-copula family that allows for positive and one that allows for negative dependence. Not listed copula families might be included to better handle limit cases. If familyset = NA (default), selection among all possible families is performed. If a vector of negative numbers is provided, selection among all but abs(familyset) is performed. Coding of pair copula families is the same as in BiCop().
Matrix	lower or upper triangular d x d matrix that defines the R-vine tree structure.
selectioncrit	Character indicating the criterion for pair-copula selection. Possible choices: selectioncrit = "AIC" (default), "BIC", or "logLik" (see BiCopSelect()).
indeptest	Logical; whether a hypothesis test for the independence of u1 and u2 is performed before bivariate copula selection (default: indeptest = FALSE; see BiCopIndTest()). The independence copula is chosen for a (conditional) pair if the null hypothesis of independence cannot be rejected.
level	numeric; significance level of the independence test (default: level = 0.05).
trunclevel	integer; level of truncation.
weights	Numerical; weights for each observation (optional).
rotations	logical; if TRUE, all rotations of the families in familyset are included.
se	Logical; whether standard errors are estimated (default: se = FALSE).
presel	Logical; whether to exclude families before fitting based on symmetry properties of the data. Makes the selection about 30\ (on average), but may yield slightly worse results in few special cases.
method	indicates the estimation method: either maximum likelihood estimation (method = "mle"; default) or inversion of Kendall's tau (method = "itau"). For method = "itau" only one parameter families and the Student t copula can be used (⁠family = 1,2,3,4,5,6,13,14,16,23,24,26,33,34⁠ or 36). For the t-copula, par2 is found by a crude profile likelihood optimization over the interval (2, 10].
cores	integer; if cores > 1, estimation will be parallelized within each tree (using parallel::parLapply()). Note that parallelization causes substantial overhead and may be slower than single-threaded computation when dimension, sample size, or family set are small or method = "itau".

Details

R-vine copula models with unknown structure can be specified using RVineStructureSelect().

Value

An RVineMatrix() object with the selected families (RVM$family) as well as sequentially estimated parameters stored in RVM$par and RVM$par2. The object is augmented by the following information about the fit:

se, se2	standard errors for the parameter estimates (if se = TRUE; note that these are only approximate since they do not account for the sequential nature of the estimation,
nobs	number of observations,
logLik, pair.logLik	log likelihood (overall and pairwise)
AIC, pair.AIC	Aikaike's Informaton Criterion (overall and pairwise),
BIC, pair.BIC	Bayesian's Informaton Criterion (overall and pairwise),
emptau	matrix of empirical values of Kendall's tau,
p.value.indeptest	matrix of p-values of the independence test.

#'

Note

For a comprehensive summary of the vine copula model, use summary(object); to see all its contents, use str(object).

Author(s)

Eike Brechmann, Thomas Nagler

References

Brechmann, E. C., C. Czado, and K. Aas (2012). Truncated regular vines in high dimensions with applications to financial data. Canadian Journal of Statistics 40 (1), 68-85.

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.

See Also

RVineMatrix(), BiCop(), BiCopSelect(), plot.RVineMatrix(), contour.RVineMatrix()

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 500 from the R-vine copula model
set.seed(123)
simdata <- RVineSim(500, RVM)

## determine the pair-copula families and parameters
RVM1 <- RVineCopSelect(simdata, familyset = c(1, 3, 4, 5 ,6), Matrix)

## see the object's content or a summary
str(RVM1)
summary(RVM1)

## inspect the fitted model using plots
## Not run: plot(RVM1)  # tree structure

contour(RVM1)  # contour plots of all pair-copulas

VineCopula documentation built on Sept. 11, 2024, 5:26 p.m.