As Johannes said, you probably have a stochastic singularity issue, which needs to be solved. You do not give a lot of details but I assume that the problem is related to the covariance matrix of the forecast errors. Did you try to estimate the model with less observed variables, as Johannes suggested? You can also do this the other way, starting with one observed variable and adding one by one new observed variables.
The stochastic singularity may have data and /or theoretical roots. You can check that you are not doing something stupid by simulating the model (with
order=1, and a large value for the
periods option) and then compute the covariance of the simulated observed variables. If this matrix is not full rank, then it means that your model says that at least one linear combination of the observed variables has zero variance. The number of such linear combinations is the number of zero eigenvalues in this covariance matrix. If you look at the associated eigenvector you will be able to identify the culprit(s) (the eigenvector associated to a zero eigenvalue defines the linear combination of variables with zero variance).
You can also check that there is no similar obvious problem with the data themselves, by doing the same exercise on the ``true’’ observed data.
You can also do the same eigenvalue/eigenvector analysis on the covariance matrix of the forecast errors, but you would have to hack the Dynare codes.