CDVineSeqEst {CDVine} | R Documentation |
This function sequentially estimates the pair-copula parameters of d-dimensional C- or D-vine copula models.
CDVineSeqEst(data, family, type, method="mle", se=FALSE, max.df=30, max.BB=list(BB1=c(5,6),BB6=c(6,6),BB7=c(5,6),BB8=c(6,1)), progress=FALSE)
data |
An N x d data matrix (with uniform margins). |
family |
A d*(d-1)/2 integer vector of C-/D-vine pair-copula families with values |
type |
Type of the vine model: |
method |
Character indicating the estimation method:
either pairwise maximum likelihood estimation ( |
se |
Logical; whether standard errors are estimated (default: |
max.df |
Numeric; upper bound for the estimation of the degrees of freedom parameter of the t-copula
(default: |
max.BB |
List; upper bounds for the estimation of the two parameters (in absolute values) of the BB1, BB6, BB7 and BB8 copulas |
progress |
Logical; whether the pairwise estimation progress is printed (default: |
The pair-copula parameter estimation is performed tree-wise,
i.e., for each C-/D-vine tree the results from the previous tree(s) are used
to calculate the new copula parameters using BiCopEst
.
par |
Estimated (first) C-/D-vine pair-copula parameters. |
par2 |
Estimated second C-/D-vine pair-copula parameters for families with two parameters (t, BB1, BB6, BB7, BB8). All other entries are zero. |
se |
Estimated standard errors of the (first) pair-copula parameter estimates |
se2 |
Estimated standard errors of the second pair-copula parameter estimates |
Carlos Almeida, Ulf Schepsmeier
Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44 (2), 182-198.
Czado, C., U. Schepsmeier, and A. Min (2012). Maximum likelihood estimation of mixed C-vines with application to exchange rates. Statistical Modelling, 12(3), 229-255.
BiCopEst
, BiCopHfunc
, CDVineLogLik
, CDVineMLE
## Example 1: 4-dimensional D-vine model with Gaussian pair-copulas data(worldindices) Data = as.matrix(worldindices)[,1:4] d = dim(Data)[2] fam = rep(1,d*(d-1)/2) # sequential estimation CDVineSeqEst(Data,fam,type=2,method="itau")$par CDVineSeqEst(Data,fam,type=2,method="mle")$par ## Example 2: 4-dimensional D-vine model with mixed pair-copulas fam2 = c(5,1,3,14,3,2) # sequential estimation CDVineSeqEst(Data,fam2,type=2,method="mle",se=TRUE,progress=TRUE)