# Problem came out with Bayesian

Dear Colleague
After running the simulation, I attempt to use the Bayesian Method for it and then Problem came out like this
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.

ESTIMATION_CHECKS: There was an error in computing the likelihood for initial parameter values.
ESTIMATION_CHECKS: If this is not a problem with the setting of options (check the error message below),
ESTIMATION_CHECKS: you should try using the calibrated version of the model as starting values. To do
ESTIMATION_CHECKS: this, add an empty estimated_params_init-block with use_calibration option immediately before the estimation
ESTIMATION_CHECKS: command (and after the estimated_params-block so that it does not get overwritten):

I also try to change the initial value as well as the data but the same thing happened and I do not know how to solve this.
I greatly appreciate who can help me overvome this issue.
Thanks
data.m (4.7 KB)
NK_SOE_Bayes_Nash3.mod (16.4 KB)

This means that your model implies an exact linear combination between the observables that is not satisfied by the data. That is for example also the case in the ALLV (2008) model where they used measurement error to get around this problem.