120 lines
3.8 KiB
Matlab
120 lines
3.8 KiB
Matlab
function osr_res = osr1(i_params,i_var,weights)
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% Compute the Optimal Simple Rules
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% INPUTS
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% i_params vector index of optimizing parameters in M_.params
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% i_var vector variables indices in declaration order
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% weights vector weights in the OSRs
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%
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% OUTPUTS
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% osr_res: [structure] results structure containing:
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% - objective_function [scalar double] value of the objective
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% function at the optimum
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% - optim_params [structure] parameter values at the optimum
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%
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% Algorithm:
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%
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% Uses Newton-type optimizer csminwel to directly solve quadratic
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% osr-problem
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%
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% Copyright (C) 2005-2014 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_ it_
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klen = M_.maximum_lag + M_.maximum_lead + 1;
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iyv = M_.lead_lag_incidence';
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iyv = iyv(:);
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iyr0 = find(iyv) ;
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it_ = M_.maximum_lag + 1 ;
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if M_.exo_nbr == 0
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oo_.exo_steady_state = [] ;
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end
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if ~ M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0
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error ('OSR: Error in model specification: some variables don''t appear as current') ;
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end
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if M_.maximum_lead == 0
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error ('OSR: Backward or static model: no point in using OSR') ;
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end
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if any(any(isinf(weights)))
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error ('OSR: At least one of the optim_weights is infinite.') ;
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end
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if any(isnan(M_.params(i_params)))
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error ('OSR: At least one of the initial parameter values for osr_params is NaN') ;
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end
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exe =zeros(M_.exo_nbr,1);
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oo_.dr = set_state_space(oo_.dr,M_,options_);
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np = size(i_params,1);
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t0 = M_.params(i_params);
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inv_order_var = oo_.dr.inv_order_var;
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%extract unique entries of covariance
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i_var=unique(i_var);
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%% do initial checks
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[loss,info,exit_flag,vx]=osr_obj_1(t0,i_params,inv_order_var(i_var),weights(i_var,i_var));
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if info~=0
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print_info(info, options_.noprint, options_);
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else
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fprintf('\nOSR: Initial value of the objective function: %g \n\n',loss);
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end
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if isinf(loss)
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fprintf('\nOSR: The initial value of the objective function is infinite.\n');
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fprintf('\nOSR: Check whether the unconditional variance of a target variable is infinite\n');
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fprintf('\nOSR: due to the presence of a unit root.\n');
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error('OSR: Initial likelihood is infinite')
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end
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if isequal(options_.osr.opt_algo,5)
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error('OSR: OSR does not support opt_algo=5.')
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elseif isequal(options_.osr.opt_algo,6)
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error('OSR: OSR does not support opt_algo=6.')
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elseif isequal(options_.osr.opt_algo,10)
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error('OSR: OSR does not support opt_algo=10.')
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else
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%%do actual optimization
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[p, f, exitflag] = dynare_minimize_objective(str2func('osr_obj_1'),t0,options_.osr.opt_algo,options_,[],cellstr(M_.param_names(i_params,:)),[],[], i_params,...
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inv_order_var(i_var),weights(i_var,i_var));
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end
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osr_res.objective_function = f;
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M_.params(i_params)=p; %make sure optimal parameters are set (and not the last draw used in csminwel)
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for i=1:length(i_params)
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osr_res.optim_params.(deblank(M_.param_names(i_params(i),:))) = p(i);
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end
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skipline()
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disp('OPTIMAL VALUE OF THE PARAMETERS:')
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skipline()
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for i=1:np
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disp(sprintf('%16s %16.6g\n',M_.param_names(i_params(i),:),p(i)))
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end
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disp(sprintf('Objective function : %16.6g\n',f));
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skipline()
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[oo_.dr,info,M_,options_,oo_] = resol(0,M_,options_,oo_); |