EstMixtureCop: Estimation of bivariate mixture bivariate copula model

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

Description

This function estimates parameters from a mixture bivariate copula model

Usage

EstMixtureCop(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 component (except for degrees of freedom)
dof	estimated degree of freedom, only for the Student copula
Q	(1 x reg) estimated weights vector
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

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