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... That said, your interpretation of the H matrix is correct, H will be singular = 192*exp(c(1)) times a 2 x 2 matrix of 1s. As to the state error covariance, I am not sure why you say that it is 2 x 2. Perhaps you left off an equation, but ...

... II on State Space Models, my interpretation is as follows: Signal Error Covariance (2x2, H matrix): Since the same named error E1 is used in both signal equations, the resulting H matrix is singular, with all of its elements being equal to 192*EXP(C(1)). Is this correct? ...

... observations: 46 Convergence achieved after 29 iterations Coefficient covariance computed using outer product of gradients WARNING: Singular covariance - coefficients are not unique Coefficient Std. Error z-Statistic Prob. ...

Thanks Startz, that's really helpful! I'll have a go away and look into what I can replace it with, if anything!

The model looks a bit like the following: DLOG(Y) = C(1) + C(2)*(LOG(Yt-1) + C(3)*LOG(Xt-1) + (1-C(3))*LOG(Zt-1)) + C(4)*DLOG(Xt-1) + C(5)*DLOG(Zt-1) + (1-C(4)-C(5))*DLOG(Ut) Where I'd want constraints such as: -1 < C(2) and C(3) < 0 C(4) and C(5) >0 I @LOGIT some of the coefficients to get round t...

In the generic model you posted, the only thing that's nonlinear is c(3). If your real model is different from that then it's hard to say much without seeing the real model. The model looks a bit like the following: DLOG(Y) = C(1) + C(2)*(LOG(Yt-1) + C(3)*LOG(Xt-1) + (1-C(3))*LOG(Zt-1)) + C(4)*DLOG...

In the generic model you posted, the only thing that's nonlinear is c(3). If your real model is different from that then it's hard to say much without seeing the real model.

What do you get for point estimates? I'll get values of something like -0.5 for a coefficient that should be 0.5. Also all the variables are logged! It's also built into an error correction framework, so sometimes the long-run variable will have the right sign while the short-run differenced one wi...

What do you get for point estimates?

... starting values for the parameters I will get the error: "WARNING: Singular covariance - coefficients are not unique" with NA's in place of the std. errors, t-stats, ...

It is the covariance of the coefficients. It is a little difficult to diagnose without looking at ... I think you are right about overidentification. I will note that in the second (non-singular) parameterization, you are only estimating a single share coefficient instead of ...

Hi, I have a concern about a "singular covariance" message which came with the output of a Kalman filter estimation. I am wondering which ...

... State Space equations, but I encounter this message "WARNING: Singular covariance - coefficients are not unique" on state space output. I have been trying to ...

... 1 1004 Included observations: 919 Failure to improve likelihood (singular hessian) after 6 iterations Coefficient covariance computed using observed Hessian WARNING: Singular covariance - coefficients are ...