Hi Hector,
I think the equation R = alpha * pi / beta should actually be a dynamic relationship describing the interest rate reaction to inflation. This is your Taylor rule, no? I am working on something similar, so I can tell you how I think about it.
What is typically done is to take some latent factor model (Diebold and Li (2006)):
and augment it with some observable factors which are then linked to the latent factors as in Diebold, Rudebusch and Aruoba (2006):
However, I don't see any reason that it's not possible to include a general equilibrium model with coefficient matrices
A and B as:
The question is then which macro states S to allow to affect the latent factors
L. Presumably the policy rate is one of these, so this informs how you setup the coefficient matrix
D.
I think what you're trying to do is restrict the dynamics of your short maturity (say, three month) treasury so that it follows a Taylor rule, which is sort of sensible. But what I think is better is to not include the fed funds rate in
Y but rather put it in Z, let your general equilibrium model drive
Z and then estimate the relationship to your yield curve, including the three month treasury.
But if you really want to create a nonlinear constraint on parameters in an IRIS model estimation, then you have two choices:
 Read the section in help model/estimate on using a usersupplied minimization routine with extra input arguments. Use this to setup a call to
fmincon in the optimization toolbox and include a nonlinear constraint.
 Solve the constraint for one of the parameters and use this relationship to write one in terms of the other in the
!links section. If you have a closed form expression for one parameter in terms of the others then this is obviously more efficient than (1) because you have fewer parameters and no nonlinear constraint to deal with.
Mike
