HMMcopula (version 1.1.0)

EstHMMCop: Estimation of bivariate Markov regime switching bivariate copula model

Description

Estimation of parameters from a bivariate Markov regime switching bivariate copula model

Usage

EstHMMCop(y, reg, family, max_iter, eps)

Value

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

Arguments

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.

Author

Mamadou Yamar Thioub and Bruno Remillard, April 12, 2018

References

<doi::10.1002/cjs.11534>

Examples

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)