VMACpp {MTS}

Vector Moving Average Model (Cpp)

Description

Performs VMA estimation using the conditional multivariate Gaussian likelihood function. This is the same function as VMA, with the likelihood function implemented in C++ for efficiency.

Usage

VMACpp(da, q = 1, include.mean = T, fixed = NULL,
    beta=NULL, sebeta=NULL, prelim = F,
    details = F, thres = 2)

Arguments

da

Data matrix of a k-dimensional VMA process with each column containing one time series

q

The order of VMA model

include.mean

A logical switch to include the mean vector. The default is to include the mean vector in estimation.

fixed

A logical matrix used to fix parameter to zero

beta

Parameter estimates for use in model simplification

sebeta

Standard errors of parameter estimates for use in model simplification

prelim

A logical switch to select parameters to be included in estimation

details

A logical switch to control the amount of output

thres

Threshold for t-ratio used to fix parameter to zero. Default is 2.

Value

data

The data of the observed time series

MAorder

The VMA order

cnst

A logical switch to include the mean vector

coef

Parameter estimates

secoef

Standard errors of the parameter estimates

residuals

Residual series

Sigma

Residual covariance matrix

Theta

The VAR coefficient matrix

mu

The constant vector

aic, bic

The information criteria of the fitted model

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 3).

See Also

VMA

Examples

theta=matrix(c(0.5,0.4,0,0.6),2,2); sigma=diag(2)
m1=VARMAsim(200,malags=c(1),theta=theta,sigma=sigma)
zt=m1$series
m2=VMACpp(zt,q=1,include.mean=FALSE)