reduced.form.var: Estimation of a reduced form VAR model

In MSBVAR: Markov-Switching, Bayesian, Vector Autoregression Models

Description

Estimates a reduced form VAR using equation-by-equation seemingly unrelated regression (SUR).

Usage

reduced.form.var(Y, p, z=NULL)

Arguments

Y	T x m multiple time series object created with ts().
p	Lag length
z	T x k exogenous variables in a matrix of T rows. Can be NULL if there are none.

Details

This is a frequentist VAR estimator. This is a workhorse function — you will want to use other functions such as irf, mc.irf or dfev to report and interpret the results of this object.

Value

List of class "VAR" with elements,

intercept	Row vector of the m intercepts.
ar.coefs	m x m x p array of the AR coefficients. The first m x m array is for lag 1, the p'th array for lag p.
Bhat	(mp + k + 1) x m matrix of the coefficients, where the columns correspond to the variables in the VAR. Intercepts follow the AR coefficients, etc.
exog.coefs	k x m matrix of exogenous coefficients, or NA if z=NULL
vcv	m x m matrix of the maximum likelihood estimate of the residual covariance
mean.S	m x mmatrix of the posterior residual covariance.
hstar	mp x mp right hand side variables crossproduct.
X	Right hand side variables for the estimation of BVAR
Y	Left hand side variables for the estimation of BVAR
y	Input data (Y)

Author(s)

Patrick T. Brandt

References

Sims, C.A. 1980. "Macroeconomics and Reality" Econometrica 48(1): 1-48.

See Also

See also szbvar for BVAR models with the Sims-Zha prior and szbsvar for Bayesian SVAR models with the Sims-Zha prior.

Examples

data(IsraelPalestineConflict)
rf.var <- reduced.form.var(IsraelPalestineConflict, p=6)
plot(irf(rf.var, nsteps=12))

MSBVAR documentation built on May 30, 2017, 1:23 a.m.