126 lines
5.9 KiB
Matlab
126 lines
5.9 KiB
Matlab
function [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,err_index,filtered_errs_init,my_obs_list,obs,init_val)
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% [filtered_errs, resids, Emat, stateval, error_code] = IVF_core(M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,err_index,filtered_errs_init,my_obs_list,obs,init_val)
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% Calls the solver to get the shocks explaining the data for the inversion filter
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%
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% Outputs:
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% - filtered_errs [T by N_obs] filtered shocks
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% - resids [T by N_obs] residuals
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% - Emat [N by N_obs by T] response matrix of endogenous variables to shocks at each point in time
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% - stateval [T by N] vector of endogenous variables
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% - error_code [4 by 1] error code
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%
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% Inputs
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% - M_ [structure] Matlab's structure describing the model (M_).
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% - dr [structure] Reduced form model.
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% - endo_steady_state [vector] steady state value for endogenous variables
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% - exo_steady_state [vector] steady state value for exogenous variables
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% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
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% - options_ [structure] Matlab's structure describing the current options (options_).
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% - err_index [double] index of shocks with strictly positive variance in M_.exo_names
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% - filtered_errs_init [T by N_obs] initial values for the shocks
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% - my_obs_list [cell] names of observables
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% - obs [T by N_obs] observed data
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% - init_val [N by 1] initial value of endogenous variables
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% Original authors: Pablo Cuba-Borda, Luca Guerrieri, Matteo Iacoviello, and Molin Zhong
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% Original file downloaded from:
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% http://www.lguerrieri.com/jae-replication.zip
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% Adapted for Dynare by Dynare Team.
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%
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% This code is in the public domain and may be used freely.
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% However the authors would appreciate acknowledgement of the source by
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% citation of any of the following papers:
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%
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% Pablo Cuba-Borda, Luca Guerrieri, Matteo Iacoviello, and Molin Zhong (2019): "Likelihood evaluation of models
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% with occasionally binding constraints", Journal of Applied Econometrics,
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% 34(7), 1073-1085
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%-------------------------------------
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% Filter shocks
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%-------------------------------------
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[sample_length, n_obs]= size(obs);
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nerrs = size(err_index,1);
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if nargin<11
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init_val = zeros(M_.endo_nbr,1);
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end
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resids = zeros(sample_length,nerrs);
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stateval = zeros(sample_length,M_.endo_nbr);
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Emat = zeros(M_.endo_nbr,nerrs,sample_length);
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error_code = zeros(4,1);
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%solver options (set locally)
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options_.solve_algo = options_.occbin.solver.solve_algo;
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options_.jacobian_flag=1;
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opts_simul = options_.occbin.simul;
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opts_simul.curb_retrench = options_.occbin.likelihood.curb_retrench;
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opts_simul.maxit = options_.occbin.likelihood.maxit;
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opts_simul.waitbar = false;
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opts_simul.periods = options_.occbin.likelihood.periods;
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opts_simul.check_ahead_periods = options_.occbin.likelihood.check_ahead_periods;
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opts_simul.max_check_ahead_periods = options_.occbin.likelihood.max_check_ahead_periods;
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opts_simul.periodic_solution = options_.occbin.likelihood.periodic_solution;
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opts_simul.restrict_state_space = options_.occbin.likelihood.restrict_state_space;
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opts_simul.piecewise_only = 1;
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filtered_errs=zeros(sample_length,n_obs);
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if options_.occbin.likelihood.waitbar
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hh_fig = dyn_waitbar(0,'IVF_core: Filtering the shocks');
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set(hh_fig,'Name','IVF_core: Filtering the shocks.');
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end
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for this_period=1:sample_length
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if options_.occbin.likelihood.waitbar
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dyn_waitbar(this_period/sample_length, hh_fig, sprintf('Period %u of %u', this_period,sample_length));
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end
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current_obs = obs(this_period,:);
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init_val_old = init_val;
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inan = ~isnan(current_obs);
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current_obs = current_obs(inan);
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obs_list = my_obs_list(inan);
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opts_simul.varobs_id=options_.varobs_id(inan)';
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opts_simul.exo_pos=err_index(inan); %err_index is predefined mapping from observables to shocks
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opts_simul.endo_init = init_val_old;
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opts_simul.SHOCKS = filtered_errs_init(this_period,inan);
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[err_vals_out, exitflag] = dynare_solve(@occbin.match_function, filtered_errs_init(this_period,inan)', options_.occbin.solver.maxit, options_.occbin.solver.solve_tolf, options_.occbin.solver.solve_tolx, options_, obs_list, current_obs, opts_simul, M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_);
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if exitflag
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filtered_errs=NaN;
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error_code(1) = 304;
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error_code(4) = 1000;
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if options_.occbin.likelihood.waitbar; dyn_waitbar_close(hh_fig); end
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return
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end
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filtered_errs(this_period,inan)=err_vals_out';
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opts_simul.SHOCKS = err_vals_out;
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[ resids(this_period,inan), ~, stateval(this_period,:), Emat(:,inan,this_period), M_] = occbin.match_function(...
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err_vals_out,obs_list,current_obs,opts_simul, M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_);
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init_val = stateval(this_period,:); %update
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if max(abs(err_vals_out))>1e8
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error_code(1) = 306;
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error_code(4) = max(abs(err_vals_out))/1000;
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filtered_errs=NaN;
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if options_.occbin.likelihood.waitbar; dyn_waitbar_close(hh_fig); end
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return
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end
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if max(abs(resids(this_period,:)))>0.001
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disp_verbose('IVF_core: I am stopping because match_function could not find the shocks that',options_.verbosity)
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disp_verbose('IVF_core: solve for the model''s observed variables',options_.verbosity)
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filtered_errs=NaN;
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error_code(1) = 303;
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error_code(4) = max(abs(resids(this_period,:)))*100;
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if options_.occbin.likelihood.waitbar; dyn_waitbar_close(hh_fig); end
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return
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end
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end
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if options_.occbin.likelihood.waitbar
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dyn_waitbar_close(hh_fig);
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end
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end |