59 lines
2.9 KiB
Matlab
59 lines
2.9 KiB
Matlab
function [resids, grad, state_out, E, M_, out] = match_function(err_0, obs_list,current_obs, opts_simul,...
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M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_)
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% function [resids, grad, stateout, E, M_, out] = match_function(err_0, obs_list,current_obs, opts_simul,...
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% M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_)
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% Outputs:
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% - resids [double] [n_exo by 1] vector of residuals
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% - grad [double] [n by n_exo] gradient (response of observables to shocks)
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% - state_out [double] [ny by 1] value of endogenous variables
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% - E [double] response of endogenous variables to shocks
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% - M_ [structure] Matlab's structure describing the model (M_).
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% - out [structure] Occbin's results structure
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%
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% Inputs
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% - err_ [double] value of shocks
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% - obs_list [cell] names of observables
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% - current_obs [double] [1 by n_obs] current value of observables
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% - opts_simul [structure] Structure with simulation options
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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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% 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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opts_simul.SHOCKS = err_0';
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options_.occbin.simul=opts_simul;
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options_.occbin.simul.full_output=1;
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options_.noprint = 1;
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[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
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nobs = size(obs_list,1);
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resids = zeros(nobs,1);
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if ~out.error_flag
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state_out= out.piecewise(1,:)' - out.ys;
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E = ss.R(:,opts_simul.exo_pos);
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grad = ss.R(opts_simul.varobs_id,opts_simul.exo_pos);
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resids = (out.piecewise(1,opts_simul.varobs_id)-current_obs)'; %-out.endo_ss.(obs_list{this_obs});
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else
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grad = NaN(length(opts_simul.varobs_id),length(opts_simul.exo_pos));
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resids = resids+100;
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end
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end
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