function [info, oo_, options_, M_] = run(M_, options_, oo_, var_list, params, i_var,W) % [info, oo_, options_, M_] = osr(M_, options_, oo_, var_list, params, i_var,W) % Function computing the solution to the optimal simple rule-problem % % INPUTS % M_ [structure] Dynare's model structure % oo_ [structure] Dynare's results structure % options_ [structure] Dynare's options structure % var_list [character array] list of endogenous variables specified % params [character array] list of parameter to be chosen in % optimal simple rule % i_var [n_osr_vars by 1 double] indices of osr-variable in % specified in optim_weights in declaration order % W [M_.endo_nbr by M_.endo_nbr sparse matrix] Weighting matrix for variance of endogenous variables % % OUTPUTS % info [integer] scalar or vector, error code. % oo_ [structure] Dynare's results structure, containing subfield % osr_res: results structure containing: % - objective_function [scalar double] value of the objective % function at the optimum % - optim_params [structure] parameter values at the optimum % options_ [structure] Dynare's options structure % M_ [structure] Dynare's model structure % % SPECIAL REQUIREMENTS % none. % Copyright © 2001-2023 Dynare Team % % This file is part of Dynare. % % Dynare is free software: you can redistribute it and/or modify % it under the terms of the GNU General Public License as published by % the Free Software Foundation, either version 3 of the License, or % (at your option) any later version. % % Dynare is distributed in the hope that it will be useful, % but WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the % GNU General Public License for more details. % % You should have received a copy of the GNU General Public License % along with Dynare. If not, see . options_.order = 1; if isempty(options_.qz_criterium) options_.qz_criterium = 1+1e-6; end oo_=make_ex_(M_,options_,oo_); np = size(params,1); i_params = zeros(np,1); for i=1:np str = deblank(params(i,:)); i_params(i) = strmatch(str{:}, M_.param_names, 'exact'); end if ~options_.noprint fprintf('\nOPTIMAL SIMPLE RULE\n') end osr_res.error_indicator = 1; %initialize indicator if M_.exo_nbr == 0 oo_.exo_steady_state = [] ; end if ~M_.lead_lag_incidence(M_.maximum_lag+1,:) > 0 error ('OSR: Error in model specification: some variables don''t appear as current') ; end if M_.maximum_lead == 0 error ('OSR: Backward or static model: no point in using OSR') ; end if any(any(isinf(W))) error ('OSR: At least one of the optim_weights is infinite.') ; end if any(isnan(M_.params(i_params))) error ('OSR: At least one of the initial parameter values for osr_params is NaN') ; end %restore backward compatibility with maxit and tolf if isfield(options_.osr,'maxit') || isfield(options_.osr,'tolf') warning('OSR: The use of maxit and tolf is now deprecated. Please use the optim-option instead.') if options_.osr.opt_algo~=4 error ('OSR: The maxit and tolf options are not supported when not using the default opt_algo=4. Use the optim-option instead.') ; else if isfield(options_.osr,'maxit') if ~isempty(options_.optim_opt) options_.optim_opt=[options_.optim_opt,',']; end options_.optim_opt=[options_.optim_opt,'''MaxIter'',',num2str(options_.osr.maxit),'']; end if isfield(options_.osr,'tolf') if ~isempty(options_.optim_opt) options_.optim_opt=[options_.optim_opt,',']; end options_.optim_opt=[options_.optim_opt,'''TolFun'',',num2str(options_.osr.tolf),'']; end end end oo_.dr = set_state_space(oo_.dr,M_); par_0 = M_.params(i_params); inv_order_var = oo_.dr.inv_order_var; %extract unique entries of covariance i_var=unique(i_var); %% do initial checks [loss,info]=osr.objective(par_0,M_,oo_,options_,i_params,inv_order_var(i_var),W(i_var,i_var)); if info~=0 print_info(info, options_.noprint, options_); else if ~options_.noprint fprintf('\nOSR: Initial value of the objective function: %g \n\n',loss); end end if ~options_.noprint && isinf(loss) fprintf('\nOSR: The initial value of the objective function is infinite.\n'); fprintf('\nOSR: Check whether the unconditional variance of a target variable is infinite\n'); fprintf('\nOSR: due to the presence of a unit root.\n'); error('OSR: Initial likelihood is infinite') end if isequal(options_.osr.opt_algo,5) error('OSR: OSR does not support opt_algo=5.') elseif isequal(options_.osr.opt_algo,6) error('OSR: OSR does not support opt_algo=6.') elseif isequal(options_.osr.opt_algo,10) error('OSR: OSR does not support opt_algo=10.') elseif isequal(options_.osr.opt_algo,11) error('OSR: OSR does not support opt_algo=11.') else if ~isempty(M_.osr.param_bounds) && ~(ismember(options_.osr.opt_algo,[1,2,5,9]) || ischar(options_.osr.opt_algo)) error('OSR: OSR with bounds on parameters requires a constrained optimizer, i.e. opt_algo= 1,2 or 9.') end %%do actual optimization [p, f] = dynare_minimize_objective(str2func('osr.objective'),par_0,options_.osr.opt_algo,options_,M_.osr.param_bounds,M_.param_names(i_params),[],[], M_,oo_,options_,i_params,... inv_order_var(i_var),W(i_var,i_var)); end osr_res.objective_function = f; M_.params(i_params)=p; %make sure optimal parameters are set (and not the last draw used in csminwel) for i=1:length(i_params) osr_res.optim_params.(M_.param_names{i_params(i)}) = p(i); end if ~options_.noprint my_title='OPTIMAL VALUE OF THE PARAMETERS'; labels = M_.param_names(i_params); headers = {'Variables'; 'Value'}; lh = cellofchararraymaxlength(labels)+2; dyntable(options_, my_title, headers, labels, p, lh, 10, 6); if options_.TeX labels = M_.param_names_tex(i_params); lh = cellofchararraymaxlength(labels)+2; dyn_latex_table(M_, options_, my_title, 'osr', headers, labels, p, lh, 10, 6); end fprintf('\nObjective function : %16.6g\n\n',f); end [info, oo_, options_, M_] = stoch_simul(M_, options_, oo_, var_list); if ~info(1) osr_res.error_indicator=0; end oo_.osr = osr_res;