84 lines
3.6 KiB
Matlab
84 lines
3.6 KiB
Matlab
function [loss,info,exit_flag,df,vx]=objective(x,M_, oo_, options_,i_params,i_var,weights)
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% [loss,info,exit_flag,df,vx]=objective(x,M_, oo_, options_,i_params,i_var,weights)
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% Objective function for optimal simple rules (OSR)
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% INPUTS
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% x vector values of the parameters
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% over which to optimize
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% M_ [structure] Dynare's model structure
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% oo_ [structure] Dynare's results structure
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% options_ [structure] Dynare's options structure
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% i_params vector index of optimizing parameters in M_.params
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% i_var vector variables indices
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% weights vector weights in the OSRs
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%
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% OUTPUTS
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% loss scalar loss function returned to solver
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% info vector info vector returned by resol
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% exit_flag scalar exit flag returned to solver
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% df vectcor Analytic Jacobian
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% vx vector variances of the endogenous variables
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2005-2023 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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exit_flag = 1;
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vx = [];
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df=NaN(length(i_params),1);
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% set parameters of the policy rule
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M_.params(i_params) = x;
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[oo_.dr,info,M_.params] = resol(0,M_,options_,oo_.dr ,oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
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if info(1)
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if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
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info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
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info(1) == 81 || info(1) == 84 || info(1) == 85
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loss = 1e8;
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info(4)=info(2);
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return
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else
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loss = 1e8;
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info(4)=0.1;
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return
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end
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end
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if ~options_.analytic_derivation
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vx = osr.get_variance_of_endogenous_variables(M_,options_,oo_.dr,i_var);
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loss = full(weights(:)'*vx(:));
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else
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totparam_nbr=length(i_params);
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oo_.dr.derivs = identification.get_perturbation_params_derivs(M_, options_, [], oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, i_params, [], [], 0); %analytic derivatives of perturbation matrices
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pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_, oo_.dr, i_var, 0, 0, 1);
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vx = pruned_state_space.Var_y + pruned_state_space.E_y*pruned_state_space.E_y';
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dE_yy = pruned_state_space.dVar_y;
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for jp=1:length(i_params)
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dE_yy(:,:,jp) = dE_yy(:,:,jp) + pruned_state_space.dE_y(:,jp)*pruned_state_space.E_y' + pruned_state_space.E_y*pruned_state_space.dE_y(:,jp)';
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end
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model_moments_params_derivs = reshape(dE_yy,length(i_var)^2,totparam_nbr);
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df = NaN(totparam_nbr,1);
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loss = full(weights(:)'*vx(:));
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for jp=1:length(i_params)
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df(jp,1) = sum(weights(:).*model_moments_params_derivs(:,jp));
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end
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end |