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HMMcopula (version 1.0.2)

Markov Regime Switching Copula Models Estimation and Goodness of Fit

Description

R functions to estimate and perform goodness of fit test for several Markov regime switching and mixture bivariate copula models. The goodness of fit test is based on a Cramer von Mises statistic and uses the Rosenblatt transform and parametric bootstrap to estimate the p-value. The estimation of the copula parameters are based on the pseudo-maximum likelihood method using pseudo-observations defined as normalized ranks.

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Version

Install

install.packages('HMMcopula')

Monthly Downloads

183

Version

1.0.2

License

GPL (>= 2)

Maintainer

Mamadou Thioub

Last Published

November 14th, 2018

Functions in HMMcopula (1.0.2)

RosenblattClayton

Rosenblatt transform for Clayton copula
GofMixtureCop

Goodness-of-fit of mixture bivariate copula model
Tau2Rho

Spearman's rho
KendallTau

Kendall's tau of a copula
SnB

Cramer-von Mises statistic SnB for GOF based on the Rosenblatt transform
ParamTau

Alpha estimation
RosenblattGumbel

Rosenblatt transform for Gumbel copula
dilog

Dilogarithm function
RosenblattStudent

Rosenblatt transform for Student copula
SimHMMCop

Simulation of bivariate Markov regime switching copula model
EstMixtureCop

Estimation of bivariate mixture bivariate copula model
GofHMMCop

Goodness-of-fit of Markov regime switching bivariate copula model
SimMarkovChain

Markov chain simulation
SimMixtureCop

Simulation of bivariate mixture copula model
EstHMMCop

Estimation of bivariate Markov regime switching bivariate copula model
EstKendallTau

Sample Kendall's tau Estimation
ParamCop

Theta estimation
RosenblattFrank

Rosenblatt transform for Frank copula
bootstrapfun

Bootstrap for the bivariate copula models
RosenblattGaussian

Rosenblatt transform for Gaussian copula
copulaFamiliesPDF

COPULAPDF Probability density function for a copula. COPULAPDF probability density function for a copula with linear correlation parameters RHO and