Bayesian estimation : Error in computing likelihood for initial parameter values

Hello:
I need help
I don’t kow how to solve this problem of initial parameter, my model is valide, but when i estimat it with data i got this error.

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 32)
Error using print_info (line 32)
Likelihood is a complex number
Error in initial_estimation_checks (line 305)
        print_info(info, DynareOptions.noprint, DynareOptions)
Error in dynare_estimation_1 (line 159)
    oo_ =
    initial_estimation_checks(objective_function,xparam1,dataset_,dataset_info,M_,estim_params_,options_,bayestopt_,bounds,oo_);
    Error in dynare_estimation (line 118)
    dynare_estimation_1(var_list,dname);
Error in codecorr.driver (line 997)
oo_recursive_=dynare_estimation(var_list_);
Error in dynare (line 278)
    evalin('base',[fname '.driver']);

This are my parameters using in initial parameter

muo=0.125;             
phi=1.45;         
phi_px=0.90;      
phi_y=0.10; 
phi_b=0.2;
rho=0.95;
zeta_GC=0;  // zeta_GC=0.4;   zeta_GC= 0.2;
estimated_params;
 muo,       beta_pdf,   0.1, 0.05;
phi_y,     gamma_pdf, 0.15, 0.05;
phi_b,     gamma_pdf, 0.2, 0.05;
rho,       beta_pdf,   0.95,  0.02;
zeta_GC,   normal_pdf, 0,     0.05;

noted that som varialbes of my data are stationary at the first difference, and somme of that are with frequance value:

Y_obs        = Y - Y(-1) + eps_Yobs;
C_obs        = C - C(-1) + eps_Cobs;
I_obs        = I + eps_Iobs;              
PX_d_obs     = PX_d + eps_pxdobs;  
U_obs        = U -U(-1) + eps_Uobs;     
b_obs        = b - b(-1) + eps_bobs;     
bstar_obs    = bstar  + eps_bsatrobs;
NX_obs = NX_ratio - NX_ratio(-1) + eps_NXobs;
Pstar_o_obs  = Pstar_o - Pstar_o(-1) + eps_Pstaroobs;

my model est linear and I used data_log.

Without the full codes it is impossible to tell.