VARMACpp: Vector Autoregressive Moving-Average Models (Cpp)

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

Vector Autoregressive Moving-Average Models (Cpp)

Description

Performs conditional maximum likelihood estimation of a VARMA model. Multivariate Gaussian likelihood function is used. This is the same function as VARMA, with the likelihood function implemented in C++ for efficiency.

Usage

VARMACpp(da, p = 0, q = 0, include.mean = T, 
    fixed = NULL, beta=NULL, sebeta=NULL, 
    prelim = F, details = F, thres = 2)

Arguments

da	Data matrix (T-by-k) of a k-dimensional time series with sample size T.
p	AR order
q	MA order
include.mean	A logical switch to control estimation of the mean vector. Default is to include the mean in estimation.
fixed	A logical matrix to control zero coefficients in estimation. It is mainly used by the command refVARMA.
beta	Parameter estimates to be used in model simplification, if needed
sebeta	Standard errors of parameter estimates for use in model simplification
prelim	A logical switch to control preliminary estimation. Default is none.
details	A logical switch to control the amount of output.
thres	A threshold used to set zero parameter constraints based on individual t-ratio. Default is 2.

Details

The fixed command is used for model refinement

Value

data	Observed data matrix
ARorder	VAR order
MAorder	VMA order
cnst	A logical switch to include the mean vector
coef	Parameter estimates
secoef	Standard errors of the estimates
residuals	Residual matrix
Sigma	Residual covariance matrix
aic,bic	Information criteria of the fitted model
Phi	VAR coefficients
Theta	VMA coefficients
Ph0	The constant vector

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 3). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

See Also

VARMA

Examples

phi=matrix(c(0.2,-0.6,0.3,1.1),2,2); theta=matrix(c(-0.5,0,0,-0.5),2,2)
sigma=diag(2)
m1=VARMAsim(300,arlags=c(1),malags=c(1),phi=phi,theta=theta,sigma=sigma)
zt=m1$series
m2=VARMA(zt,p=1,q=1,include.mean=FALSE)

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