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Hello world, Just to keep the subject going. I have one question about the fitted and actual values for a dependent variable (say y) . I have found that the fitted y fits PERFECTLY the actual y. Using alternative starting values (maximum likelihood, grid search,ect...), the fit is still perfect. I s...

I used to specify the variances. When i don't, I have the same error than you.
Put it just before the specification of your state space, like this

c(1)=your value,
c(2)= your value,
.
.
.
Here, define your state space.

Give starting value to variances by adding something like
c(1)= your value
c(2)=your value
...

Hope it helps,
A.

Could you post the workfile?

Make sure you do not press twice the "_" in defining the variable.
Check this and tell us!
A.

Hi,
Could you show us the code?

Ok, thanks. But when I do this, I get the following message:' Signal variables are not allowed in state equations'. Here is the complete model: sspace ss1 ss1.append @signal epi = epistate + [var=c(5)] ss1.append @state epistate = c(6)*z1 + c(7)*z(2) +[var=c(8)] ss1.append @signal pid = sv1*epistate...

Hi Startz, Thanks for the reply. I am not sure I understand well what you mean. Here what I understand (tell me if I am wrong) Your last point suggests that the model is estimated according to a two-step approach. In the first step, I have to estimate @signal epi = epistate + [var=c(5)] @state epist...

Hi Startz, thank you. Well, I didn't specify the instrumental variables since I didn't know how to do it.
A.

Hello, Actually, my problem seems a little bit complicated. In my the state space representation, the "signal" equation contains at least one endogenous regressor, named "epi" (which is pid(1)). I think, in order to consistently estimate the model with the Kalman filter tool, one...

Great! Thanks
A.

Many thanks Trubador. That was a problem of starting values, effectively. I'm trying to fix this.
Aqua!

Very good 'hack'. One question: when a constraint is put on a parameter, say c(2), the standard errror Eviews returns is for c(2) or the restriction on c(2)?
Thanks a lot,
Aqua!

Dear Eviews Users, I have to estimate a time-varying parameter model, where the states equation contain error. Say, my model is sspace ss1 ss1.append @signal pid = sv1*epi + sv2*pid(-1) + sv3*mchatqt + [var=(c(1))] ss1c.append @state sv1 = sv1(-1) + [var=(c(2))] ss1.append @state sv2 = sv2(-1) + [va...