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!