reduced.form.var: Estimation of a reduced form VAR model
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.
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
$m x m$matrix 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)
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.
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.