msdccc: Compute mean and variance of estimators through Monte-Carlo...

In TaoussD/EbEEMGARCH: Estimating MGARCH(1,1) model equation by equation

Description

Generate a sample of the estimator to compute mean and standard error

Usage

MSD.CCC.EbEE(theta0, init, nobs, iter, type, noise, nu = Inf)

Arguments

theta0	List of the real parameters
init	List of initialisation parameters
nobs	Number of observations in the sample
iter	Number of iterations
type	type="diagonal" for estimation as an MGARCH(1,1) CCC-diagonal type="sdiagonal" for estimation as a MGARCH(1,1) CCC semi-diagonal
noise	"normal" or "student"
nu	Degrees of freedom of the t-distribution, leave blank if normal-noise

Details

Check example to see how to create the lists in argument

Value

With usual notations of MGARCH(1,1) CCC models

Omega.mean	Mean of Omega
Omega.sd	Standard deviation of Omega
A.mean	Mean of A
A.sd	Standard deviation of A
B.mean	Mean of B
B.sd	Standard deviation of B
R.mean	Mean of R, correlation matrix
R.sd	Standard deviation of R

Author(s)

D. Taouss & C. Francq

References

C. Francq & J.M. Zakoian, Estimating multivariate GARCH and Stochastic Correlation models equation by equation, October 2014

See Also

EbEEMGARCH Homepage of the documentation

Examples

m<-3
Omega0 <- rep(0.01, m)
Alpha0 <- rep(0.05, m)
Beta0 <- rep(0.90, m)
R0 <- diag(rep(1, m))
theta0<-list(Omega=Omega0,A=Alpha0,B=Beta0,R=R0)


Omegainit <- rep(0.1, m)
Alphainit <- rep(0.5, m)
Betainit <- rep(0.7, m)
init<-list(Omega=Omegainit,A=Alphainit,B=Betainit)

MSD.CCC.EbEE(theta0,init,2000,10,"diagonal","normal")

TaoussD/EbEEMGARCH documentation built on May 9, 2019, 4:18 p.m.