CDVineMLE {CDVine} | R Documentation |
This function calculates the MLE of C- or D-vine copula model parameters using sequential estimates as initial values (if not provided).
CDVineMLE(data, family, start=NULL, start2=NULL, type, maxit=200, max.df=30, max.BB=list(BB1=c(5,6),BB6=c(6,6),BB7=c(5,6),BB8=c(6,1)), ...)
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 |
start |
A d*(d-1)/2 numeric vector of starting values for C-/D-vine pair-copula parameters
(optional; otherwise they are calculated via |
start2 |
A d*(d-1)/2 numeric vector of starting values for second C-/D-vine pair-copula parameters
(optional; otherwise they are calculated via |
type |
Type of the vine model: |
maxit |
The maximum number of iteration steps (optional; 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 |
... |
Additional control parameters for |
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. |
loglik |
Optimized log-likelihood value corresponding to the estimated pair-copula parameters. |
convergence |
An integer code indicating either successful convergence ( |
message |
A character string giving any additional information returned by |
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.
## Example 1: 4-dimensional D-vine model with Gaussian pair-copulas data(worldindices) Data = as.matrix(worldindices)[,1:4] fam = rep(1,6) # maximum likelihood estimation ## Not run: CDVineMLE(Data,family=fam,type=2,maxit=100) ## End(Not run) ## Example 2: 4-dimensional D-vine model with mixed pair-copulas fam2 = c(5,1,3,14,3,2) # sequential estimation m = CDVineSeqEst(Data,family=fam2,type=2) m # calculate the log-likelihood LogLik0 = CDVineLogLik(Data,fam2,m$par,m$par2,type=2) LogLik0$loglik # maximum likelihood estimation ## Not run: CDVineMLE(Data,family=fam2,type=2,maxit=5) # 5 iterations CDVineMLE(Data,family=fam2,type=2) # default: 200 iterations ## End(Not run)