Estimation Troubles with Gali and Monacelli (2005)

Estimation ML/Bayesian estimation
1

Hello,

I am trying to estimate the model Gali and Monacelli (2005), “Monetary and Exchange Rate Volatility in a Small Open Economy” based on the code provided by Professor Pfeifer and data from South Korea, but am running into the following error messages:

You did not declare endogenous variables after the estimation/calib_smoother command.
PARAMETER INITIALIZATION: Some standard deviations of shocks of the calibrated model are 0 and
PARAMETER INITIALIZATION: violate the inverse gamma prior. They will instead be initialized with the prior mean.
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_ =
    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 GaliMonacelliEst (line 306)
oo_recursive_=dynare_estimation(var_list_);

Error in dynare (line 235)
evalin('base',fname) ;

I have seasonally adjusted and demeaned my data according to the guide in Pfeifer (2018), and have implemented 7 observed series, and added measurement errors in order to preserve BK conditions with the number of shocks. Any guidance would be greatly appreciated!

GMPostInflationTargeting.xlsx (18.2 KB)
GaliMonacelliEst.mod (6.3 KB)

2

You do not calibrate or estimate both eps_a and eps_star. Probably you’ll want to estimate them. Run the code again please, after fixing this.

3
4

Hi Professor,

After using the diffuse_filter option and calibrating some of the shocks using test values of the original code, I received these error messages:

POSTERIOR KERNEL OPTIMIZATION PROBLEM!
(minus) the hessian matrix at the “mode” is not positive definite!
=> posterior variance of the estimated parameters are not positive.
You should try to change the initial values of the parameters using
the estimated_params_init block, or use another optimization routine.
Warning: The results below are most likely wrong!

In dynare_estimation_1 (line 316)
In dynare_estimation (line 105)
In GaliMonacelliEst (line 310)
In dynare (line 235)
Warning: Matrix is singular, close to singular or badly scaled. Results may be inaccurate.
RCOND = NaN.

Are there any further errors in the code, or should the model be adjusted? The adjusted code is attached.

GaliMonacelliEst.mod (6.6 KB)

5

You should check the identification of your parameters.