Identification_rank of J is deficient

Dear Johannes,

I have read several posts related to this issue. But I am confused what are the sources of identification problem in my model. I have attached the mod file, data, some part of log file and others. Its always the rank J deficient. Its a stationary model. data are hp filtered. The model still runs. What should I do to fix this? Is it because of the priors? Many thanks.

Kind Regards. (297 KB)

  1. A general remark: You are not supposed to use a two-sided HP-filter for likelihood-based estimation
  2. I cannot replicate the message with the unstable version (Dynare 4.5). I guess this was a numerical problem that has been fixed.

Hi Johannes,

  1. I am using one-sided hp filtered data. I can try using linear-detrending instead if you suspect filtering can be a reason.

  2. I am using Dynare version 4.4.3. I never used unstable 4.5. The file I sent you still produce the following message if I use the simple identification command. I have checked it once again. I don’t understand why its happening, estimation runs smoothly.

==== Identification analysis ====

Testing prior mean
Evaluating simulated moment uncertainty … please wait
Doing 378 replicas of length 300 periods.
Simulated moment uncertainty … done!

All parameters are identified in the model (rank of H).

The rank of J (moments) is deficient!

Monte Carlo Testing

Testing MC sample

All parameters are identified in the model (rank of H).

The rank of J (moments) is deficient for 214 out of 250 MC runs!

Kind Regards,


  1. Then it should be fine. Note that the data treatment has no bearing on the identification command. Identification is a theoretical concept here. Your data treatment can therefore not be the reason.
  2. Then please try the unstable version ( There have been several bugfixes around identification ( This might explain why I don’t get an error. From what I can see, your model is fine.

Hi Johannes,
Yes, you are right. It was a bug. I got the unstable version and it detects no problem relating to identification.
Thank you very much.