Hello

I am estimating a small open economy version of the Gali, Smets and Wouters (NBER macro annual 2012) unemployment model.

The model is fairly big and has 21 series and 21 shocks. However, I get an error

```
initial_estimation_checks:: The forecast error variance in the multivariate Kalman filter became singular.
initial_estimation_checks:: This is often a sign of stochastic singularity, but can also sometimes happen by chance
initial_estimation_checks:: for a particular combination of parameters and data realizations.
initial_estimation_checks:: If you think the latter is the case, you should try with different initial values for the estimated parameters.
```

This is quite surprising because I have as many shocks as observables.

Can the stochastic singularity happen because 2 observables are highly correlated, like detrended employment and detrended output? In the production function, I do not use a technology shock or physical capital, so the model predicts that the employment gap and output gap are perfectly collinear.

This I think is the problemâ€¦when I use an additional measurement error for the employment gapâ€¦so with 22 shocks and 21 observablesâ€¦I do not get the stochastic singularity error.

It would be interesting to hear your thoughts.

Cheers

Reuben