Wrong with estimation in dynare 4.5(unstable)

Hi, everyone, I am try to use dynare 4.5 to estimation a model, but it comes out like:

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):

Error using initial_estimation_checks (line 143)
initial_estimation_checks:: The forecast error variance in the
multivariate Kalman filter became singular.
Error in initial_estimation_checks (line 143)
error(‘initial_estimation_checks:: The forecast error variance
in the multivariate Kalman filter became singular.’)
Error in dynare_estimation_1 (line 149)
oo_ =
initial_estimation_checks(objective_function,xparam1,dataset_,dataset_info,M_,estim_params_,options_,bayestopt_,bounds,oo_);
Error in dynare_estimation (line 105)
dynare_estimation_1(var_list,dname);
Error in shadow1bey (line 600)
oo_recursive_=dynare_estimation(var_list_);
Error in dynare (line 223)
evalin(‘base’,fname) ;

Even I do as add a estimated_params_init-block with use_calibration option, the problem still exists.
So, could anyone help me with that problem?

Stochastic singularity is typically not a matter of wrong parameter values, but a fundamental issue with the model. You need to fix that.