VARs {MTS}

VAR Model with Selected Lags

Description

This is a modified version of VAR command by allowing the users to specify which AR lags to be included in the model.

Usage

VARs(x, lags, include.mean = T, output = T, fixed = NULL)

Arguments

x

A T-by-k data matrix of k-dimensional time series with T observations

lags

A vector of non-zero AR lags. For instance, lags=c(1,3) denotes a VAR(3) model with Phi2 = 0.

include.mean

A logical switch to include the mean vector

output

A logical switch to control output

fixed

A logical matrix to fix parameters to zero.

Details

Performs VAR estimation by allowing certain lag coefficient matrices being zero.

Value

data

Observed time series data

lags

The selected VAR lags

order

The VAR order

cnst

A logical switch to include the mean vector

coef

Parameter estimates

aic, bic

Information criteria of the fitted model

residuals

Residual series

secoef

Standard errors of the estimates

Sigma

Residual covariance matrix

Phi

VAR coefficient matrix

Ph0

A constant vector

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 2). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

See Also

VAR command

Examples

data("mts-examples",package="MTS")
zt=log(qgdp[,3:5])
m1=VARs(zt,lags=c(1,2,4))