VMA Estimation with Exact likelihood

Description

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

Usage

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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 logicak 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 inlcude the mean vector

coef

Parameter estimayes

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

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