Log-likelihood of the VAR(1) model.

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Description

Log-likelihood of the VAR(1) model specified by the supplied parameters

Usage

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loglikVAR1(Y, A, P, unbalanced=matrix(nrow=0, ncol=2))

Arguments

Y

Three-dimensional array containing the data. The first, second and third dimensions correspond to covariates, time and samples, respectively. The data are assumed to be centered covariate-wise.

A

A matrix \mathbf{A} of auto-regression parameters.

P

Inverse error covariance matrix \mathbf{Ω}_{\varepsilon} (=\mathbf{Σ_{\varepsilon}^{-1}}).

unbalanced

A matrix with two columns, indicating the unbalances in the design. Each row represents a missing design point in the (time x individual)-layout. The first and second column indicate the time and individual (respectively) specifics of the missing design point.

Value

The log-likelihood of the VAR(1) model with supplied parameters.

Author(s)

Wessel N. van Wieringen <w.vanwieringen@vumc.nl>

See Also

ridgeVAR1.

Examples

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# set dimensions
p <- 3; n <- 4; T <- 10

# set model parameters
SigmaE <- diag(p)/4
A <- createA(p, "chain")

# generate data
Y <- dataVAR1(n, T, A, SigmaE)

# center data
Y <- centerVAR1data(Y)

# fit VAR(1) model
VAR1hat <- ridgeVAR1(Y, 1, 1)

# evaluate the log-likelihood of this fit.
loglikVAR1(Y, VAR1hat$A, VAR1hat$P)

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