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Thank you very much, Glenn.

I tried @pred_statecov with Eviews6 and it did work!!
and I also tried @sm_statecov, too.

Thank you very much for your help.

Hello all,

I would like to estimate standard error of nonlinear combination of estimated state variables from state space model.
I am using Eviews version6. Does anyone know how to do it? I cannot find it in the manual.

Thank you very much in advance for your help. :)

Oh I have more questions!! Does anyone know that after estimating time varying parameter model and get the smoothed state varibales, do we need to report any goodness of fit of the model? and if yes, how can we get something like R-squaerd or others? Or only the log-likelihood and AIC and SIC provid...

Thank you very much, trubador :D

Hello all, I am trying to estimate the time varying parameter using state space model in eviews6. the model is like this (in general form) signal equation yt = xtβt + ztAt + et et ∼ N(0, R) state equation βt = βt−1 + vt vt ∼ N(0, Q) At = At−1 + wt wt ∼ N(0, H) my questions are 1.) After getting the ...

Hello, trubador

I would like to clarify one more thing.
Is my understanding correct that the smoothed series from state space model in eviews come from utilization of Kalman filter?
what is the proc/make kalman filter for?

Thank you very much trubador :)

Hello, Tambell

If I understand your question correctly, I just asked similar question last week.
You can check here:
http://forums.eviews.com/viewtopic.php?f=4&t=11859
The first two posts may help. :)

Thank you very much, trubador. I tried according to your suggestion by 1) excluding the insignificant variables (from OLS result) 2) using only one or two time-varying parameters and get the following code: @signal dlpm = sv1*dlneer + sv2*dlneer(1) + c(2)*dlbrent + c(3)*ygap(1) + [ename=e1, var = ex...

Thank you so much, Trubador. It is more clear to me now. However, I still get the warning of singular covariance. I tried to search for the solution in this forum and found that adding initial values might solve this problem. But even I add initial value for state variance, it still cannot be solved...

and one more question:
what is the reason for this message in the estimation output of sspace and how to solve it?
"Failure to improve Likelihood after 1 iteration"

Thank you very much in advance for your help.

Thanks Trubador. Let me ask more questions. Clip.jpg To be more specific, here is the result I got from the following model. @signal X = sv1*A + sv2*B + sv3*C + sv4*D + [var = exp(c(1))] @state sv1 = sv1(-1) @state sv2 = sv2(-1) @state sv3 = sv3(-1) @state sv4 = sv4(-1) Do you have any suggestion si...

thank you very much for your answer, Trubador.
However, do you have any suggestion for my first question?
I tried to run the model and the state series are not so different across time so now I'm not quite sure whether I should use time varying for these parameters.

I am trying to estimate time varying parameters by the following equation form: signal equation yt = xtβt + ztAt + et et ∼ N(0, R) state equation βt = βt−1 + vt vt ∼ N(0, Q) At = At−1 + wt wt ∼ N(0, H) my questions are 1) First, I would like to allow all parameters to vary over time (says B and A). ...