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 problem…when I use an additional measurement error for the employment gap…so with 22 shocks and 21 observables…I do not get the stochastic singularity error.
It would be interesting to hear your thoughts.