90 lines
2.5 KiB
Matlab
90 lines
2.5 KiB
Matlab
function [loss,vx,info,exit_flag]=osr_obj(x,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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% 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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% vx vector variances of the endogenous variables
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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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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2005-2013 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 <http://www.gnu.org/licenses/>.
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global M_ oo_ options_ optimal_Q_ it_
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% global ys_ Sigma_e_ endo_nbr exo_nbr optimal_Q_ it_ ykmin_ options_
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junk = [];
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exit_flag = 1;
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vx = [];
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info=0;
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loss=[];
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% set parameters of the policiy rule
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M_.params(i_params) = x;
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% don't change below until the part where the loss function is computed
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it_ = M_.maximum_lag+1;
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[dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_);
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switch info(1)
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case 1
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loss = 1e8;
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return
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case 2
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loss = 1e8*min(1e3,info(2));
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return
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case 3
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loss = 1e8*min(1e3,info(2));
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return
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case 4
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loss = 1e8*min(1e3,info(2));
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return
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case 5
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loss = 1e8;
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return
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case 6
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loss = 1e8*min(1e3,info(2));
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return
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case 7
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loss = 1e8*min(1e3);
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return
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case 8
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loss = 1e8*min(1e3,info(2));
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return
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case 9
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loss = 1e8*min(1e3,info(2));
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return
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case 20
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loss = 1e8*min(1e3,info(2));
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return
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otherwise
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if info(1)~=0
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loss = 1e8;
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return;
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end
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end
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vx = get_variance_of_endogenous_variables(dr,i_var);
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loss = full(weights(:)'*vx(:));
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