perfect_foresight_simulation + bksup0: remove unused functions
Sébastien Villemot
parent
f938014e84
commit
62805b7183
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@ -1,40 +0,0 @@
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function d = bksup0(c,ny,jcf,iyf,icf,periods)
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% Solves deterministic models recursively by backsubstitution for one lead/lag
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%
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% INPUTS
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% ny: number of endogenous variables
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% jcf: variables index forward
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%
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% OUTPUTS
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% d: vector of backsubstitution results
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2003-2017 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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ir = ((periods-2)*ny+1):(ny+(periods-2)*ny);
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irf = iyf+(periods-1)*ny ;
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for i = 2:periods
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c(ir,jcf) = c(ir,jcf)-c(ir,icf)*c(irf,jcf);
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ir = ir-ny;
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irf = irf-ny;
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end
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d = c(:,jcf);
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@ -1,2 +1,2 @@
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list_of_functions = {'discretionary_policy_1', 'dsge_var_likelihood', 'dyn_first_order_solver', 'dyn_waitbar', 'ep_residuals', 'evaluate_likelihood', 'prior_draw_gsa', 'identification_analysis', 'computeDLIK', 'univariate_computeDLIK', 'metropolis_draw', 'flag_implicit_skip_nan', 'moment_function', 'mr_hessian', 'masterParallel', 'auxiliary_initialization', 'auxiliary_particle_filter', 'conditional_filter_proposal', 'conditional_particle_filter', 'gaussian_filter', 'gaussian_filter_bank', 'gaussian_mixture_filter', 'gaussian_mixture_filter_bank', 'Kalman_filter', 'online_auxiliary_filter', 'pruned_state_space_system', 'sequential_importance_particle_filter', 'solve_model_for_online_filter', 'perfect_foresight_simulation', 'prior_draw', 'priordens',...
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'+occbin/solver.m','+occbin/mkdatap_anticipated_dyn.m','+occbin/mkdatap_anticipated_2constraints_dyn.m','+occbin/match_function.m','+occbin/solve_one_constraint.m','+occbin/solve_two_constraint.m','+occbin/plot/shock_decomposition.m'};
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list_of_functions = {'discretionary_policy_1', 'dsge_var_likelihood', 'dyn_first_order_solver', 'dyn_waitbar', 'ep_residuals', 'evaluate_likelihood', 'prior_draw_gsa', 'identification_analysis', 'computeDLIK', 'univariate_computeDLIK', 'metropolis_draw', 'flag_implicit_skip_nan', 'moment_function', 'mr_hessian', 'masterParallel', 'auxiliary_initialization', 'auxiliary_particle_filter', 'conditional_filter_proposal', 'conditional_particle_filter', 'gaussian_filter', 'gaussian_filter_bank', 'gaussian_mixture_filter', 'gaussian_mixture_filter_bank', 'Kalman_filter', 'online_auxiliary_filter', 'pruned_state_space_system', 'sequential_importance_particle_filter', 'solve_model_for_online_filter', 'prior_draw', 'priordens',...
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'+occbin/solver.m','+occbin/mkdatap_anticipated_dyn.m','+occbin/mkdatap_anticipated_2constraints_dyn.m','+occbin/match_function.m','+occbin/solve_one_constraint.m','+occbin/solve_two_constraint.m','+occbin/plot/shock_decomposition.m'};
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@ -76,9 +76,6 @@ end
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simulated_moments = zeros(size(sample_moments));
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% Just to be sure that things don't mess up with persistent variables...
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clear perfect_foresight_simulation;
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if nargin<5
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for s = 1:options.number_of_simulated_sample
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time_series = extended_path([],options.simulated_sample_size,1);
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@ -139,4 +136,4 @@ else% parallel mode.
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end
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end
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g = simulated_moments-sample_moments;
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g = simulated_moments-sample_moments;
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@ -1,189 +0,0 @@
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function info = perfect_foresight_simulation(compute_linear_solution,steady_state)
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% Performs deterministic simulations with lead or lag on one period
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%
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% INPUTS
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% endo_simul [double] n*T matrix, where n is the number of endogenous variables.
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% exo_simul [double] q*T matrix, where q is the number of shocks.
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% compute_linear_solution [integer] scalar equal to zero or one.
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%
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% OUTPUTS
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% none
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%
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% ALGORITHM
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% Laffargue, Boucekkine, Juillard (LBJ)
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% see Juillard (1996) Dynare: A program for the resolution and
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% simulation of dynamic models with forward variables through the use
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% of a relaxation algorithm. CEPREMAP. Couverture Orange. 9602.
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%
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% SPECIAL REQUIREMENTS
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% None.
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% Copyright © 2009-2022 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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global M_ options_ it_ oo_
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persistent lead_lag_incidence dynamic_model ny nyp nyf nrs nrc iyf iyp isp is isf isf1 iz icf ghx iflag
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if ~nargin && isempty(iflag)% Initialization of the persistent variables.
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lead_lag_incidence = M_.lead_lag_incidence;
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dynamic_model = [M_.fname '.dynamic'];
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ny = size(oo_.endo_simul,1);
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nyp = nnz(lead_lag_incidence(1,:));% number of lagged variables.
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nyf = nnz(lead_lag_incidence(3,:));% number of leaded variables.
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nrs = ny+nyp+nyf+1;
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nrc = nyf+1;
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iyf = find(lead_lag_incidence(3,:)>0);% indices for leaded variables.
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iyp = find(lead_lag_incidence(1,:)>0);% indices for lagged variables.
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isp = 1:nyp;
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is = (nyp+1):(nyp+ny); % Indices for contemporaneaous variables.
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isf = iyf+nyp;
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isf1 = (nyp+ny+1):(nyf+nyp+ny+1);
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iz = 1:(ny+nyp+nyf);
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icf = 1:size(iyf,2);
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info = [];
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iflag = 1;
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return
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end
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initial_endo_simul = oo_.endo_simul;
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warning_old_state = warning;
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warning off all
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if nargin<1
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compute_linear_solution = 0;
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else
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if nargin<2
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error('The steady state (second input argument) is missing!');
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end
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end
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if ~isstruct(compute_linear_solution) && compute_linear_solution
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[dr,info,M_,oo_] = resol(0,M_,options_,oo_);
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elseif isstruct(compute_linear_solution)
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dr = compute_linear_solution;
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compute_linear_solution = 1;
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end
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if compute_linear_solution
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ghx(dr.order_var,:) = dr.ghx;
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ghx = ghx(iyf,:);
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end
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periods = options_.periods;
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stop = 0 ;
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it_init = M_.maximum_lag+1;
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info.convergence = 1;
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info.time = 0;
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info.error = 0;
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info.iterations.time = zeros(options_.simul.maxit,1);
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info.iterations.error = info.iterations.time;
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last_line = options_.simul.maxit;
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error_growth = 0;
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h1 = clock;
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for iter = 1:options_.simul.maxit
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h2 = clock;
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if options_.terminal_condition
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c = zeros(ny*(periods+1),nrc);
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else
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c = zeros(ny*periods,nrc);
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end
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it_ = it_init;
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z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1) ];
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[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
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jacobian = [jacobian(:,iz) , -d1];
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ic = 1:ny;
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icp = iyp;
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c(ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
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for it_ = it_init+(1:periods-1-(options_.terminal_condition==2))
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z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)];
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[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
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jacobian = [jacobian(:,iz) , -d1];
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jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:);
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ic = ic + ny;
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icp = icp + ny;
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c(ic,:) = jacobian(:,is)\jacobian(:,isf1);
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end
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if options_.terminal_condition
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if options_.terminal_condition==1% Terminal condition is Y_{T} = Y_{T+1}
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s = eye(ny);
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s(:,isf) = s(:,isf)+c(ic,1:nyf);
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ic = ic + ny;
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c(ic,nrc) = s\c(ic,nrc);
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else% Terminal condition is Y_{T+1}-Y^{\star} = TransitionMatrix*(Y_{T}-Y^{\star})
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it_ = it_+1;
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z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1) ] ;
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[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
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jacobian = [jacobian(:,iz) -d1];
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jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:) ;
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ic = ic + ny;
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icp = icp + ny;
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s = jacobian(:,is);
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s(:,iyp) = s(:,iyp)+jacobian(:,isf)*ghx;
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c (ic,:) = s\jacobian(:,isf1);
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end
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c = bksup0(c,ny,nrc,iyf,icf,periods);
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c = reshape(c,ny,periods+1);
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oo_.endo_simul(:,it_init+(0:periods)) = oo_.endo_simul(:,it_init+(0:periods))+c;
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else% Terminal condition is Y_{T}=Y^{\star}
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c = bksup0(c,ny,nrc,iyf,icf,periods);
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c = reshape(c,ny,periods);
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oo_.endo_simul(:,it_init+(0:periods-1)) = oo_.endo_simul(:,it_init+(0:periods-1))+c;
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end
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err = max(max(abs(c)));
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info.iterations.time(iter) = etime(clock,h2);
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info.iterations.error(iter) = err;
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if iter>1
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error_growth = error_growth + (info.iterations.error(iter)>info.iterations.error(iter-1));
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end
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if isnan(err) || error_growth>3
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last_line = iter;
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break
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end
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if err < options_.dynatol.f
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stop = 1;
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info.time = etime(clock,h1);
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info.error = err;
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info.iterations.time = info.iterations.time(1:iter);
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info.iterations.error = info.iterations.error(1:iter);
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break
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end
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end
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if stop && options_.terminal_condition==2
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% Compute the distance to the deterministic steady state (for the subset of endogenous variables with a non zero
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% steady state) at the last perdiod.
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idx = find(abs(oo_.steady_state)>0);
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distance_to_steady_state = abs(((oo_.endo_simul(idx,end)-oo_.steady_state(idx))./oo_.steady_state(idx)))*100;
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disp(['(max) Distance to steady state at the end is (in percentage):' num2str(max(distance_to_steady_state))])
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end
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if ~stop
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info.time = etime(clock,h1);
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info.error = err;
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info.convergence = 0;
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info.iterations.time = info.iterations.time(1:last_line);
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info.iterations.error = info.iterations.error(1:last_line);
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oo_.endo_simul = initial_endo_simul;
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end
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warning(warning_old_state);
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