125 lines
4.5 KiB
Matlab
125 lines
4.5 KiB
Matlab
function [dr, info, params]=discretionary_policy_1(M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
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% Higher-level function for solving discretionary optimal policy
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% INPUTS
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% - M_ [structure] Matlab's structure describing the model (M_).
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% - options_ [structure] Matlab's structure describing the current options (options_).
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% - dr [struct] Decision rules for stochastic simulations.
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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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%
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% OUTPUTS
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% - dr [structure] Reduced form model.
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% - info [integer] scalar or vector, error code.
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% - params [double] vector of potentially updated parameters
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% Copyright © 2007-2020 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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persistent Hold
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info = 0;
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beta = get_optimal_policy_discount_factor(M_.params, M_.param_names);
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%call steady_state_file if present to update parameters
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if options_.steadystate_flag
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% explicit steady state file
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[ys,M_.params,info] = evaluate_steady_state_file(endo_steady_state,[exo_steady_state; exo_det_steady_state],M_, ...
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options_,~options_.steadystate.nocheck);
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if info(1)
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return;
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end
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else
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ys=zeros(M_.endo_nbr,1);
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end
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params=M_.params;
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[U,Uy,W] = feval([M_.fname,'.objective.static'],zeros(M_.endo_nbr,1),[], M_.params);
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if any(any(isnan(Uy)))
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info = 64 ; %the derivatives of the objective function contain NaN
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return;
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end
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if any(any(Uy~=0))
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if options_.debug
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non_zero_derivs=find(any(Uy~=0));
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for ii=1:length(non_zero_derivs)
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non_zero_deriv_names{ii,1} = M_.endo_names{non_zero_derivs(ii)};
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end
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disp_string=[non_zero_deriv_names{1,:}];
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for ii=2:size(non_zero_deriv_names,1)
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disp_string=[disp_string,', ',non_zero_deriv_names{ii,:}];
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end
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fprintf('\nThe derivative of the objective function w.r.t. to variable(s) %s is not 0\n',disp_string);
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end
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info = 66;
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return;
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end
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W=reshape(W,M_.endo_nbr,M_.endo_nbr);
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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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% Find the jacobian
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z = repmat(ys,1,klen);
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iyr0 = find(iyv(:)) ;
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z = z(iyr0);
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it_ = M_.maximum_lag + 1 ;
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[junk,jacobia_] = feval([M_.fname '.dynamic'],z,zeros(M_.exo_nbr+M_.exo_det_nbr,klen), M_.params, ys, it_);
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if max(abs(junk))>options_.solve_tolf
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info = 65; %the model must be written in deviation form and not have constant terms or have a steady state provided
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return;
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end
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Indices={'lag','contemp','lead'};
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iter=1;
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for j=1:numel(Indices)
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A.(Indices{j})=zeros(M_.eq_nbr,M_.endo_nbr);
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if strcmp(Indices{j},'contemp')||(strcmp(Indices{j},'lag') && M_.maximum_lag)||(strcmp(Indices{j},'lead') && M_.maximum_lead)
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[~,row,col]=find(M_.lead_lag_incidence(iter,:));
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A.(Indices{j})(:,row)=jacobia_(:,col);
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iter=iter+1;
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end
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end
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B=jacobia_(:,nnz(iyv)+1:end);
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%%% MAIN ENGINE %%%
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if ~isempty(Hold)
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[H,G,info]=discretionary_policy_engine(A.lag,A.contemp,A.lead,B,W,M_.instr_id,beta,options_.dp.maxit,options_.discretionary_tol,options_.qz_criterium,Hold);
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else
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[H,G,info]=discretionary_policy_engine(A.lag,A.contemp,A.lead,B,W,M_.instr_id,beta,options_.dp.maxit,options_.discretionary_tol,options_.qz_criterium);
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end
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if info
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return
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else
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Hold=H; %save previous solution
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% Hold=[]; use this line if persistent command is not used.
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end
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%write back solution to dr
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dr.ys =ys;
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dr=set_state_space(dr,M_);
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T=H(dr.order_var,dr.order_var);
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dr.ghu=G(dr.order_var,:);
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if M_.maximum_endo_lag
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Selection=M_.lead_lag_incidence(1,dr.order_var)>0;%select state variables
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end
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dr.ghx=T(:,Selection); |