The RATS Software Forum

Hello,

I am wondering how to estimate an SVAR using short-run and long-run restrictions that has more restrictions than necessary to just-identify it.

In particular, I am thinking about a 4-variables model with two shocks that have no long-run effects on two of the variables, and one of these two shocks plus one of the other two shocks without any restricted long-run effects shouldn't impact two of the four variables contemporaneously.

Using the usual notation used e.g. for @shortandlong I thus have:
Since @shortandlong cannot deal with overidentified SVARs, my question is, how I can estimate such a model using cvmodel.

This is addressed in the RATS v8 User's Guide on page UG-221. In effect, you can't because the overidentification places restrictions on the lag coefficients.

Thanks for pointing to the paragraph that I missed in the User Guide.

I guess the "rather nasty non-linear constraints" are either very hard to derive and/or prohibit a proper convergence of the estimation procedure?

They would be implicit functions of the dynamic behavior of the whole model. I've never seen an example of anyone doing it, so I have no idea whether it's computationally feasible.