VAR: Vector Autoregressive Model

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

Vector Autoregressive Model

Description

Perform least squares estimation of a VAR model

Usage

VAR(x, p = 1, output = T, include.mean = T, fixed = NULL)

Arguments

x	A T-by-k matrix of k-dimensional time series
p	Order of VAR model. Default is 1.
output	A logical switch to control output. Default is with output.
include.mean	A logical switch. It is true if mean vector is estimated.
fixed	A logical matrix used in constrained estimation. It is used mainly in model simplification, e.g., removing insignificant estimates.

Details

To remove insignificant estimates, one specifies a threshold for individual t-ratio. The fixed matrix is then defined automatically to identify those parameters for removal.

Value

data	Observed data
cnst	A logical switch to include the mean constant vector
order	VAR order
coef	Coefficient matrix
aic,bic,hq	Information criteria of the fitted model
residuals	Residuals
secoef	Standard errors of the coefficients to be used in model refinement
Sigma	Residual covariance matrix
Phi	AR coefficient polynomial
Ph0	The constant vector

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 3). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

See Also

refVAR command

Examples

data("mts-examples",package="MTS")
gdp=log(qgdp[,3:5])
zt=diffM(gdp)
m1=VAR(zt,p=2)

d-/MTS documentation built on June 12, 2022, 12:50 a.m.