BVAR: Bayesian Vector Autoregression

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

Bayesian Vector Autoregression

Description

Estimate a VAR(p) model using Bayesian approach, including the use of Minnesota prior

Usage

BVAR(z,p=1,C,V0,n0=5,Phi0=NULL,include.mean=T)

Arguments

z	A matrix of vector time series, each column represents a series.
p	The AR order. Default is p=1.
C	The precision matrix of the coefficient matrix. With constant, the dimension of C is (kp+1)-by-(kp+1). The covariance matrix of the prior for the parameter vec(Beta) is Kronecker(Sigma_a,C-inverse).
V0	A k-by-k covariance matrix to be used as prior for the Sigma_a matrix
n0	The degrees of freedom used for prior of the Sigma_a matrix, the covariance matrix of the innovations. Default is n0=5.
Phi0	The prior mean for the parameters. Default is set to NULL, implying that the prior means are zero.
include.mean	A logical switch controls the constant term in the VAR model. Default is to include the constant term.

Details

for a given prior, the program provide the posterior estimates of a VAR(p) model.

Value

est	Posterior means of the parameters
Sigma	Residual covariance matrix

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 2).

Examples

data("mts-examples",package="MTS")
z=log(qgdp[,3:5])
zt=diffM(z)*100
C=0.1*diag(rep(1,7))
V0=diag(rep(1,3))
BVAR(zt,p=2,C,V0)

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