Dear Professor Pfeifer,
I’ve got three questions when doing the bayesian estimation. Would you please give any advice? Thank you very much.
- The moments of main variables I got after bayesian estimation are much larger than the moments of real data. I try to use endogenous_prior option, but I get errors as bellow:
There are 27 eigenvalue(s) larger than 1 in modulus
for 27 forward-looking variable(s)
The rank condition is verified.
In endogenous_prior (line 88)
In dsge_likelihood (line 833)
In initial_estimation_checks (line 137)
In dynare_estimation_1 (line 165)
In dynare_estimation (line 105)
In czh (line 779)
In dynare (line 235)
Warning: Matrix is singular, close to singular or badly
scaled. Results may be inaccurate. RCOND = NaN.
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 114)
Prior density is not a number (NaN)
Error in print_info (line 114)
error(‘Prior density is not a number (NaN)’);
Error in initial_estimation_checks (line 175)
Error in dynare_estimation_1 (line 165)
Error in dynare_estimation (line 105)
Error in czh (line 779)
Error in dynare (line 235)
How should I deal with this?
Is there any way to make the simulated moments close to the real moments?
When running the bayesian estimation in a model with specified trend, could I use one-sided hp filter instead of one-differnece filter to deal with the observed data?
Thanks again for reading this post, and really appreciate your kindness.