VMACpp: Vector Moving Average Model (Cpp)

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

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)

MTS documentation built on April 11, 2022, 5:07 p.m.