EstHMMCop {HMMcopula} | R Documentation |
Estimation of parameters from a bivariate Markov regime switching bivariate copula model
EstHMMCop(y, reg, family, max_iter, eps)
y |
(nx2) data matrix (observations or residuals) that will be transformed to pseudo-observations |
reg |
number of regimes |
family |
'gaussian' , 't' , 'clayton' , 'frank' , 'gumbel' |
max_iter |
maximum number of iterations of the EM algorithm |
eps |
precision (stopping criteria); suggestion 0.0001. |
theta |
(1 x reg) estimated parameter of the copula according to CRAN copula package (except for Frank copula, where theta = log(theta_R_Package)) for each regime (except for degrees of freedom) |
dof |
estimated degree of freedom, only for the Student copula |
Q |
(reg x reg) estimated transition matrix |
eta |
(n x reg) conditional probabilities of being in regime k at time t given observations up to time t |
tau |
estimated Kendall tau for each regime |
U |
(n x 2) matrix of Rosenblatt transforms |
cvm |
Cramer-von-Mises statistic for goodness-of-fit |
W |
regime probabilities for the conditional distribution given the past Kendall's tau |
Mamadou Yamar Thioub and Bruno Remillard, April 12, 2018
<doi::10.1002/cjs.11534>
Q <- matrix(c(0.8, 0.3, 0.2, 0.7),2,2) ; kendallTau <- c(0.3 ,0.7) ;
data <- SimHMMCop(Q, 'clayton', kendallTau, 10)$SimData;
estimations <- EstHMMCop(data,2,'clayton',10000,0.0001)