# Endogenous priors error

Dear all,
I am trying to use endogenous priors in my estimation of a large dsge model (70-80 parameters). I have many moments I want to match and I have specified them in this way:

moment_calibration;
variable1, variable1 , [0.003 0.008];
variable2, variable2 , [0.006 0.009];

end;

The numbers in brackets are variances (not standard errors). Correct?

However, I get this error Message:

Error in computing likelihood for initial parameter values

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 print_info (line 118)
The model violates one (many) endogenous prior restriction(s)

If I only include one or two moments, then it works. When I include more than 3 I get the message above. What does it actually mean?

Here is my estimation commando:
estimation(datafile=data,first_obs=29,lik_init=1,mode_compute=6,prefilter=1,smoother,mh_replic=40000,plot_priors=0,mh_nblocks=1,mh_drop=0.5, mode_file=MODEL_FILENAME_mode);

Thank you!

Let me add that I am starting from a mode that actually fulfills all moments in the moment_calibration block. thanks!

I would need to see the files.