VMAs: VMA Model with Selected Lags
In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

VMAs	R Documentation

VMA Model with Selected Lags

Performs the conditional maximum likelihood estimation of a VMA model with selected lags in the model

VMAs(da, malags, include.mean = T, fixed = NULL, prelim = F, details = F, thres = 2)

`da`	A T-by-k matrix of a k-dimensional time series with T observations
`malags`	A vector consisting of non-zero MA lags
`include.mean`	A logical switch to include the mean vector
`fixed`	A logical matrix to fix coefficients to zero
`prelim`	A logical switch concerning initial estimation
`details`	A logical switch to control output level
`thres`	A threshold value for setting coefficient estimates to zero

A modified version of VMA model by allowing the user to select non-zero MA lags

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

Ruey S. Tsay

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