GofHMMCop: Goodness-of-fit of Markov regime switching bivariate copula...

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

Description

This function performs goodness-of-fit test of a Markov regime switching bivariate copula model

Usage

GofHMMCop(R, reg, family, max_iter, eps, n_sample, n_cores)

Arguments

R	(n x 2) data matrix that will be transformed to pseudo-observations
reg	number of regimes
family	'gaussian' , 't' , 'clayton' , 'frank' , 'gumbel'
max_iter	maxmimum number of iterations of the EM algorithm
eps	precision (stopping criteria); suggestion 0.0001
n_sample	number of bootstrap; suggestion 1000
n_cores	number of cores to use in the parallel computing

Value

pvalue	pvalue (significant when the result is greater than 5)
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

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