VMAe: VMA Estimation with Exact likelihood

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

VMA Estimation with Exact likelihood

Description

Estimation of a VMA(q) model using the exact likelihood method. Multivariate Gaussian likelihood function is used.

Usage

VMAe(da, q = 1, include.mean = T, coef0 = NULL, 
    secoef0 = NULL, fixed = NULL, prelim = F, 
    details = F, thres = 2)

Arguments

da	Data matrix (T-by-k) for a k-dimensional VMA process
q	The order of a VMA model
include.mean	A logical switch to include the mean vector in estimation. Default is to include the mean vector.
coef0	Initial estimates of the coefficients used mainly in model refinement
secoef0	Standard errors of the initial estimates
fixed	A logical matrix to put zero parameter constraints
prelim	A logical switch for preliminary estimation
details	A logical switch to control output in estimation
thres	The threshold value for zero parameter constraints

Value

data	The observed time series
MAorder	The VMA order
cnst	A logical switch to include the mean vector
coef	Parameter estimates
secoef	Standard errors of parameter estimates
residuals	Residual series
Sigma	Residual covariance matrix
Theta	VMA coefficient matrix
mu	The mean vector
aic,bic	The information criteria of the fitted model

Author(s)

Ruey S. Tsay

References

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

See Also

VMA

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