BiCopHfunc {CDVine} | R Documentation |
This function evaluates the conditional distribution function (h-function) of a given parametric bivariate copula.
BiCopHfunc(u1, u2, family, par, par2=0)
u1,u2 |
Numeric vectors of equal length with values in [0,1]. |
family |
An integer defining the bivariate copula family: |
par |
Copula parameter. |
par2 |
Second parameter for bivariate copulas with two parameters (t, BB1, BB6, BB7, BB8; default: |
The h-function is defined as the conditional distribution function of a bivariate copula, i.e.,
h(u|v,θ) := F(u|v) = \partial C(u,v) / \partial v,
where C is a bivariate copula distribution function with parameter(s) θ. For more details see Aas et al. (2009).
hfunc1 |
Numeric vector of the conditional distribution function (h-function) evaluated at |
hfunc2 |
Numeric vector of the conditional distribution function (h-function) evaluated at |
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.
BiCopPDF
, BiCopCDF
, CDVineLogLik
, CDVineSeqEst
## Example 1: 4-dimensional C-vine model with mixed pair-copulas data(worldindices) Data = as.matrix(worldindices)[,1:4] d = dim(Data)[2] fam = c(5,1,3,14,3,2) # sequential estimation seqpar1 = CDVineSeqEst(Data,fam,type=1,method="itau") # calculate the inputs of the second tree using h-functions h1 = BiCopHfunc(Data[,1],Data[,2],fam[1],seqpar1$par[1]) h2 = BiCopHfunc(Data[,1],Data[,3],fam[2],seqpar1$par[2]) h3 = BiCopHfunc(Data[,1],Data[,4],fam[3],seqpar1$par[3]) # compare estimated parameters BiCopEst(h1$hfunc1,h2$hfunc1,fam[4],method="itau") seqpar1$par[4] BiCopEst(h1$hfunc1,h3$hfunc1,fam[5],method="itau") seqpar1$par[5] ## Example 2: 4-dimensional D-vine model with mixed pair-copulas # sequential estimation seqpar2 = CDVineSeqEst(Data,fam,type=2,method="itau") # calculate the inputs of the second tree using h-functions h1 = BiCopHfunc(Data[,1],Data[,2],fam[1],seqpar2$par[1]) h2 = BiCopHfunc(Data[,2],Data[,3],fam[2],seqpar2$par[2]) h3 = BiCopHfunc(Data[,3],Data[,4],fam[3],seqpar2$par[3]) # compare estimated parameters BiCopEst(h1$hfunc2,h2$hfunc1,fam[4],method="itau") seqpar2$par[4] BiCopEst(h2$hfunc2,h3$hfunc1,fam[5],method="itau") seqpar2$par[5]