VARs: VAR Model with Selected Lags

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

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))

MTS documentation built on April 11, 2022, 5:07 p.m.