180 lines
5.6 KiB
Matlab
180 lines
5.6 KiB
Matlab
function [dr,ys,info]=discretionary_policy_1(oo_,Instruments)
|
|
|
|
% Copyright (C) 2007-2018 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 <http://www.gnu.org/licenses/>.
|
|
|
|
global M_ options_
|
|
persistent Hold
|
|
|
|
dr = [];
|
|
ys = [];
|
|
info = 0;
|
|
|
|
if isempty(options_.qz_criterium)
|
|
options_.qz_criterium = 1+1e-6;
|
|
end
|
|
|
|
% safeguard against issues like running ramsey policy first and then running discretion
|
|
if isfield(M_,'orig_model')
|
|
orig_model = M_.orig_model;
|
|
M_.endo_nbr = orig_model.endo_nbr;
|
|
M_.endo_names = orig_model.endo_names;
|
|
M_.lead_lag_incidence = orig_model.lead_lag_incidence;
|
|
M_.maximum_lead = orig_model.maximum_lead;
|
|
M_.maximum_endo_lead = orig_model.maximum_endo_lead;
|
|
M_.maximum_lag = orig_model.maximum_lag;
|
|
M_.maximum_endo_lag = orig_model.maximum_endo_lag;
|
|
else
|
|
M_.orig_model = M_;
|
|
end
|
|
|
|
beta = get_optimal_policy_discount_factor(M_.params, M_.param_names);
|
|
|
|
exo_nbr = M_.exo_nbr;
|
|
if isfield(M_,'orig_model')
|
|
orig_model = M_.orig_model;
|
|
endo_nbr = orig_model.endo_nbr;
|
|
endo_names = orig_model.endo_names;
|
|
lead_lag_incidence = orig_model.lead_lag_incidence;
|
|
MaxLead = orig_model.maximum_lead;
|
|
MaxLag = orig_model.maximum_lag;
|
|
else
|
|
endo_names = M_.endo_names;
|
|
endo_nbr = M_.endo_nbr;
|
|
MaxLag=M_.maximum_lag;
|
|
MaxLead=M_.maximum_lead;
|
|
lead_lag_incidence = M_.lead_lag_incidence;
|
|
end
|
|
|
|
%call steady_state_file if present to update parameters
|
|
if options_.steadystate_flag
|
|
% explicit steady state file
|
|
[~,M_.params,info] = evaluate_steady_state_file(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_, ...
|
|
options_,0);
|
|
end
|
|
[U,Uy,W] = feval([M_.fname,'.objective.static'],zeros(endo_nbr,1),[], M_.params);
|
|
if any(any(isnan(Uy)))
|
|
error(['discretionary_policy: the derivatives of the objective function contain NaN'])
|
|
end
|
|
if any(any(Uy~=0))
|
|
non_zero_derivs=find(any(Uy~=0));
|
|
for ii=1:length(non_zero_derivs)
|
|
non_zero_deriv_names{ii,1} = M_.endo_names{non_zero_derivs(ii)};
|
|
end
|
|
disp_string=[non_zero_deriv_names{1,:}];
|
|
for ii=2:size(non_zero_deriv_names,1)
|
|
disp_string=[disp_string,', ',non_zero_deriv_names{ii,:}];
|
|
end
|
|
fprintf('\nThe derivative of the objective function w.r.t. to variable(s) %s is not 0\n',disp_string)
|
|
error(['discretionary_policy: the objective function must have zero ' ...
|
|
'first order derivatives'])
|
|
end
|
|
|
|
W=reshape(W,endo_nbr,endo_nbr);
|
|
|
|
klen = MaxLag + MaxLead + 1;
|
|
iyv=lead_lag_incidence';
|
|
% Find the jacobian
|
|
z = repmat(zeros(endo_nbr,1),1,klen);
|
|
z = z(nonzeros(iyv)) ;
|
|
it_ = MaxLag + 1 ;
|
|
|
|
if exo_nbr == 0
|
|
oo_.exo_steady_state = [] ;
|
|
end
|
|
|
|
[junk,jacobia_] = feval([M_.fname '.dynamic'],z, [zeros(size(oo_.exo_simul)) ...
|
|
oo_.exo_det_simul], M_.params, zeros(endo_nbr,1), it_);
|
|
if any(junk~=0)
|
|
error(['discretionary_policy: the model must be written in deviation ' ...
|
|
'form and not have constant terms'])
|
|
end
|
|
|
|
eq_nbr= size(jacobia_,1);
|
|
instr_nbr=endo_nbr-eq_nbr;
|
|
|
|
if instr_nbr==0
|
|
error('discretionary_policy:: There are no available instruments, because the model has as many equations as variables.')
|
|
end
|
|
if size(Instruments,1)< instr_nbr
|
|
error('discretionary_policy:: There are fewer declared instruments than omitted equations.')
|
|
elseif size(Instruments,1)> instr_nbr
|
|
error('discretionary_policy:: There are more declared instruments than omitted equations.')
|
|
end
|
|
|
|
instr_id=nan(instr_nbr,1);
|
|
for j=1:instr_nbr
|
|
vj=deblank(Instruments{j});
|
|
vj_id=strmatch(vj, endo_names, 'exact');
|
|
if ~isempty(vj_id)
|
|
instr_id(j)=vj_id;
|
|
else
|
|
error([mfilename,':: instrument ',vj,' not found'])
|
|
end
|
|
end
|
|
|
|
Indices={'lag','0','lead'};
|
|
iter=1;
|
|
for j=1:numel(Indices)
|
|
eval(['A',Indices{j},'=zeros(eq_nbr,endo_nbr);'])
|
|
if strcmp(Indices{j},'0')||(strcmp(Indices{j},'lag') && MaxLag)||(strcmp(Indices{j},'lead') && MaxLead)
|
|
[~,row,col]=find(lead_lag_incidence(iter,:));
|
|
eval(['A',Indices{j},'(:,row)=jacobia_(:,col);'])
|
|
iter=iter+1;
|
|
end
|
|
end
|
|
B=jacobia_(:,nnz(iyv)+1:end);
|
|
|
|
%%% MAIN ENGINE %%%
|
|
qz_criterium = options_.qz_criterium;
|
|
solve_maxit = options_.dp.maxit;
|
|
discretion_tol = options_.discretionary_tol;
|
|
|
|
if ~isempty(Hold)
|
|
[H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium,Hold);
|
|
else
|
|
[H,G,info]=discretionary_policy_engine(Alag,A0,Alead,B,W,instr_id,beta,solve_maxit,discretion_tol,qz_criterium);
|
|
end
|
|
|
|
if info
|
|
dr=[];
|
|
return
|
|
else
|
|
Hold=H; %save previous solution
|
|
% Hold=[]; use this line if persistent command is not used.
|
|
end
|
|
% set the state
|
|
dr=oo_.dr;
|
|
dr.ys =zeros(endo_nbr,1);
|
|
dr=set_state_space(dr,M_,options_);
|
|
order_var=dr.order_var;
|
|
|
|
T=H(order_var,order_var);
|
|
dr.ghu=G(order_var,:);
|
|
Selection=lead_lag_incidence(1,order_var)>0;%select state variables
|
|
dr.ghx=T(:,Selection);
|
|
|
|
ys=NondistortionarySteadyState(M_);
|
|
dr.ys=ys; % <--- dr.ys =zeros(NewEndo_nbr,1);
|
|
|
|
function ys=NondistortionarySteadyState(M_)
|
|
if exist([M_.fname,'_steadystate.m'],'file')
|
|
eval(['ys=',M_.fname,'_steadystate.m;'])
|
|
else
|
|
ys=zeros(M_.endo_nbr,1);
|
|
end
|