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Estimation of parameters from a bivariate Markov regime switching bivariate copula model
EstHMMCop(y, reg, family, max_iter, eps)(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)
estimated degree of freedom, only for the Student copula
(reg x reg) estimated transition matrix
(n x reg) conditional probabilities of being in regime k at time t given observations up to time t
estimated Kendall tau for each regime
(n x 2) matrix of Rosenblatt transforms
Cramer-von-Mises statistic for goodness-of-fit
regime probabilities for the conditional distribution given the past Kendall's tau
(nx2) data matrix (observations or residuals) that will be transformed to pseudo-observations
number of regimes
'gaussian' , 't' , 'clayton' , 'frank' , 'gumbel'
maximum number of iterations of the EM algorithm
precision (stopping criteria); suggestion 0.0001.
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)
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