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Ah, that makes sense. The estimation works when I set the variances in the state equations equal to a small number. So now the question is, how can I estimate the variances of the time varying parameters separately from the state space regression? Stock and Watson (1998 I think) show how to do it by...

The data is in fact stationary, but I think the assumption of random walk parameters is pretty standard. I've never heard of others having a problem with stationary data. Is there a problem with having all the error terms have unrestricted variances? Here's the file.

Yes I do. Why do you ask?

Can anyone help me figure out what I'm doing wrong? I must be fundamentally misunderstanding something about Sspace estimation. I'm trying to estimate an augmented Taylor rule equation with time-varying parameters. I set up the model like so: f = sv1 + sv2*f(-1) + sv3*p + sv4*y + sv5*s + [VAR=EXP(C(...