Error in computing likelihood for initial parameter values - need help :)

Hi there!

I am trying to estimate a DSGE model and struggling with a few error messages. Simulating the model (e.g. impulse response functions) under some calibration scheme works well but when it comes to estimation I get the message below. Apparently the BK conditions are ok in the simulation but they collapse in the estimation. I also set the initial parameter values to those from the calibration.
Does anyone have an idea what the error might be? Thank you very much in advance! (41.5 KB)

There are 6 eigenvalue(s) larger than 1 in modulus
for 6 forward-looking variable(s)

The rank condition is verified.

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 42)
Blanchard Kahn conditions are not satisfied: no stable equilibrium

Error in print_info (line 42)
error([‘Blanchard Kahn conditions are not satisfied: no stable’ …

Error in initial_estimation_checks (line 175)
print_info(info, DynareOptions.noprint, DynareOptions)

Error in dynare_estimation_1 (line 165)
oo_ =

Error in dynare_estimation (line 105)

Error in gali (line 600)

Error in dynare (line 223)
evalin(‘base’,fname) ;

Most probably there is a unit root in the model and you need to use diffuse_filter

1 Like

Thank you jpfeifer!! I tried the diffuse_filter option and it seems to work now. I think the problem was because apart from inflation rates I also have prices and exchange rates in the model which are most probably non-stationary.

Yes, that explains it.