EstHMMCop: Estimation of bivariate Markov regime switching bivariate...

In HMMcopula: Markov Regime Switching Copula Models Estimation and Goodness of Fit

Description

This function estimates parameters from a bivariate Markov regime switching bivariate copula model

Usage

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

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.

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

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)

HMMcopula documentation built on April 21, 2020, 9:05 a.m.