Hello folks!
I'm using R-Package {vars} and I'm trying to estimate an A-Model.
I have serious problems regarding the restrictions.
1) My A-Matrix needs (!) to have the following form:
# 1 NA NA NA
# 0 1 NA NA
# 0 0 1 NA
# 0 0 0 1
That is done in R by:
A_Matrix <- diag(4) # main diagonal = 4 restrictions
A_Matrix [1, 2] <- NA #
A_Matrix [1, 3] <- NA #
A_Matrix [1, 4] <- NA #
A_Matrix [2, 3] <- NA #
A_Matrix [2, 4] <- NA #
A_Matrix [3, 4] <- NA # off diagonal = 6 restrictions
2) The Variance-Covariance-Matrix of the structural residuals needs (!) to
be looking like:
#
# var(X1) 0 0 0
# 0 var(X2) 0 0
# 0 0 var(X3) 0
# 0 0 0 var(X4)
Since cov(xy)=cov(yx) there are 6 more restrictions.
So in total I would have 4+6+6=16 restrictions. The SVAR would be just
identified.
My problem is that I don't know how to implement this
Variance-Covariance-Matrix within R and {vars}.
My Code so far is:
# Prediction SVAR - A-Model (B-Matrix = NULL)
# restrictions:
# 1) Amat = A_Matrix
# 2) ????
VAR.est <- VAR(data.ts, p = 4, type = "none")
SVAR.A.est <- SVAR(x=VAR.est, estmethod = "direct", Amat = A_Matrix ,
Bmat = NULL, hessian = TRUE, lrtest = TRUE)
#--------------------------------------------------------------------
I know, that {vars} restrict the Variance-Covariance-Matrix by default to an
identity-matrix but I wondered if I can't restrict it by myself since the
way I need (!) to do that is quite common.
Thank you for any comments. I'm quite desperate right now :/
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