VARs function

VAR Model with Selected Lags

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

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.

Returns

• 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

References

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

Author(s)

Ruey S. Tsay

See Also

VAR command

Examples

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