From 5d43070041a42def472e688c8b6c192de4e79de4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?St=C3=A9phane=20Adjemian=20=28Argos=29?=
<stepan@adjemian.eu>
Date: Fri, 14 Mar 2025 11:45:54 +0100
Subject: [PATCH] Remove unused routine.
---
matlab/ep/extended_path_homotopy.m | 4 +-
...solve_stochastic_perfect_foresight_model.m | 254 ------------------
2 files changed, 2 insertions(+), 256 deletions(-)
delete mode 100644 matlab/ep/solve_stochastic_perfect_foresight_model.m
diff --git a/matlab/ep/extended_path_homotopy.m b/matlab/ep/extended_path_homotopy.m
index ce5c207a47..c4df1c1592 100644
--- a/matlab/ep/extended_path_homotopy.m
+++ b/matlab/ep/extended_path_homotopy.m
@@ -40,7 +40,7 @@ if ismember(method, [1, 2])
switch(algo)
case 0
[flag, endo_simul_new] = ...
- solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
+ solve_stochastic_perfect_foresight_model_0(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
case 1
[flag, endo_simul_new] = ...
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
@@ -107,7 +107,7 @@ if isequal(method, 3) || (isequal(method, 2) && noconvergence)
switch(algo)
case 0
[flag, endo_simul_new] = ...
- solve_stochastic_perfect_foresight_model(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
+ solve_stochastic_perfect_foresight_model_0(endo_simul, exo_simul, pfm, ep.stochastic.quadrature.nodes, ep.stochastic.order);
case 1
[flag, endo_simul_new] = ...
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
diff --git a/matlab/ep/solve_stochastic_perfect_foresight_model.m b/matlab/ep/solve_stochastic_perfect_foresight_model.m
deleted file mode 100644
index 8263f5e706..0000000000
--- a/matlab/ep/solve_stochastic_perfect_foresight_model.m
+++ /dev/null
@@ -1,254 +0,0 @@
-function [flag,endo_simul,err] = solve_stochastic_perfect_foresight_model(endo_simul,exo_simul,pfm,nnodes,order)
-
-% Copyright © 2012-2017 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 <https://www.gnu.org/licenses/>.
-
-flag = 0;
-err = 0;
-stop = 0;
-
-params = pfm.params;
-steady_state = pfm.steady_state;
-ny = pfm.ny;
-periods = pfm.periods;
-dynamic_model = pfm.dynamic_model;
-lead_lag_incidence = pfm.lead_lag_incidence;
-lead_lag_incidence_t = transpose(lead_lag_incidence);
-nyp = pfm.nyp;
-nyf = pfm.nyf;
-i_cols_1 = pfm.i_cols_1;
-i_cols_j = pfm.i_cols_j;
-i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
-hybrid_order = pfm.hybrid_order;
-if hybrid_order > 0
- if hybrid_order == 2
- h_correction = 0.5*pfm.dr.ghs2(pfm.dr.inv_order_var);
- end
-else
- h_correction = 0;
-end
-%h_correction = pfm.h_correction;
-
-
-maxit = pfm.maxit_;
-tolerance = pfm.tolerance;
-verbose = pfm.verbose;
-
-number_of_shocks = size(exo_simul,2);
-
-[nodes,weights] = gauss_hermite_weights_and_nodes(nnodes);
-
-if number_of_shocks>1
- nodes = repmat(nodes,1,number_of_shocks)*chol(pfm.Sigma);
- % to be fixed for Sigma ~= I
- for i=number_of_shocks:-1:1
- rr(i) = {nodes(:,i)};
- ww(i) = {weights};
- end
- nodes = cartesian_product_of_sets(rr{:});
- weights = prod(cartesian_product_of_sets(ww{:}),2);
- nnodes = nnodes^number_of_shocks;
-else
- nodes = nodes*sqrt(pfm.Sigma);
-end
-
-if verbose
- disp (' -----------------------------------------------------');
- disp ('MODEL SIMULATION :');
- fprintf('\n');
-end
-
-z = endo_simul(lead_lag_incidence_t(:)>0);
-[~, jacobian] = dynamic_model(z, exo_simul, params,steady_state, 2);
-
-% Each column of Y represents a different world
-% The upper right cells are unused
-% The first row block is ny x 1
-% The second row block is ny x nnodes
-% The third row block is ny x nnodes^2
-% and so on until size ny x nnodes^order
-world_nbr = nnodes^order;
-Y = repmat(endo_simul(:),1,world_nbr);
-
-% The columns of A map the elements of Y such that
-% each block of Y with ny rows are unfolded column wise
-dimension = ny*(sum(nnodes.^(0:order-1),2)+(periods-order)*world_nbr);
-if order == 0
- i_upd_r = (1:ny*periods);
- i_upd_y = i_upd_r + ny;
-else
- i_upd_r = zeros(dimension,1);
- i_upd_y = i_upd_r;
- i_upd_r(1:ny) = (1:ny);
- i_upd_y(1:ny) = ny+(1:ny);
- i1 = ny+1;
- i2 = 2*ny;
- n1 = ny+1;
- n2 = 2*ny;
- for i=2:periods
- for j=1:nnodes^min(i-1,order)
- i_upd_r(i1:i2) = (n1:n2)+(j-1)*ny*periods;
- i_upd_y(i1:i2) = (n1:n2)+ny+(j-1)*ny*(periods+2);
- i1 = i2+1;
- i2 = i2+ny;
- end
- n1 = n2+1;
- n2 = n2+ny;
- end
-end
-if rows(lead_lag_incidence)>2
- icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ...
- find(lead_lag_incidence(3,:))+2*world_nbr*ny]';
-else
- if nyf
- icA = [find(lead_lag_incidence(2,:))+world_nbr*ny find(lead_lag_incidence(3,:))+2*world_nbr*ny ]';
- else
- icA = [find(lead_lag_incidence(1,:)) find(lead_lag_incidence(2,:))+world_nbr*ny ]';
- end
-end
-h1 = clock;
-for iter = 1:maxit
- A1 = sparse([],[],[],ny*(sum(nnodes.^(0:order-1),2)+1),dimension,(order+1)*world_nbr*nnz(jacobian));
- res = zeros(ny,periods,world_nbr);
- i_rows = 1:ny;
- i_cols = find(lead_lag_incidence');
- i_cols_p = i_cols(1:nyp);
- i_cols_s = i_cols(nyp+(1:ny));
- i_cols_f = i_cols(nyp+ny+(1:nyf));
- i_cols_Ap = i_cols_p;
- i_cols_As = i_cols_s;
- i_cols_Af = i_cols_f - ny;
- i_hc = i_cols_f - 2*ny;
- for i = 1:order+1
- i_w_p = 1;
- for j = 1:nnodes^(i-1)
- innovation = exo_simul;
- if i > 1
- innovation(i+1,:) = nodes(mod(j-1,nnodes)+1,:);
- end
- if i <= order
- for k=1:nnodes
- y = [Y(i_cols_p,i_w_p);
- Y(i_cols_s,j);
- Y(i_cols_f,(j-1)*nnodes+k)];
- [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1);
- if i == 1
- % in first period we don't keep track of
- % predetermined variables
- i_cols_A = [i_cols_As - ny; i_cols_Af];
- A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_1);
- else
- i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
- A1(i_rows,i_cols_A) = A1(i_rows,i_cols_A) + weights(k)*jacobian(:,i_cols_j);
- end
- res(:,i,j) = res(:,i,j)+weights(k)*d1;
- i_cols_Af = i_cols_Af + ny;
- end
- else
- % i==order+1
- if hybrid_order==2
- y = [Y(i_cols_p,i_w_p);
- Y(i_cols_s,j);
- Y(i_cols_f,j)+h_correction(i_hc)];
- else
- y = [Y(i_cols_p,i_w_p);
- Y(i_cols_s,j);
- Y(i_cols_f,j)];
- end
- [d1,jacobian] = dynamic_model(y,innovation,params,steady_state,i+1);
- if i == 1
- % in first period we don't keep track of
- % predetermined variables
- i_cols_A = [i_cols_As - ny; i_cols_Af];
- A1(i_rows,i_cols_A) = jacobian(:,i_cols_1);
- else
- i_cols_A = [i_cols_Ap; i_cols_As; i_cols_Af];
- A1(i_rows,i_cols_A) = jacobian(:,i_cols_j);
- end
- res(:,i,j) = d1;
- i_cols_Af = i_cols_Af + ny;
- end
- i_rows = i_rows + ny;
- if mod(j,nnodes) == 0
- i_w_p = i_w_p + 1;
- end
- if i > 1
- if mod(j,nnodes) == 0
- i_cols_Ap = i_cols_Ap + ny;
- end
- i_cols_As = i_cols_As + ny;
- end
- end
- i_cols_p = i_cols_p + ny;
- i_cols_s = i_cols_s + ny;
- i_cols_f = i_cols_f + ny;
- end
- nzA = cell(periods,world_nbr);
- for j=1:world_nbr
- i_rows_y = find(lead_lag_incidence')+(order+1)*ny;
- offset_c = ny*(sum(nnodes.^(0:order-1),2)+j-1);
- offset_r = (j-1)*ny;
- for i=order+2:periods
- [d1,jacobian] = dynamic_model(Y(i_rows_y,j), ...
- exo_simul,params, ...
- steady_state,i+1);
- if i == periods
- [ir,ic,v] = find(jacobian(:,i_cols_T));
- else
- [ir,ic,v] = find(jacobian(:,i_cols_j));
- end
- nzA{i,j} = [offset_r+ir,offset_c+icA(ic), v]';
- res(:,i,j) = d1;
- i_rows_y = i_rows_y + ny;
- offset_c = offset_c + world_nbr*ny;
- offset_r = offset_r + world_nbr*ny;
- end
- end
- err = max(abs(res(i_upd_r)));
- if err < tolerance
- stop = 1;
- if verbose
- fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
- fprintf('\n') ;
- disp(' Convergency obtained.') ;
- fprintf('\n') ;
- end
- flag = 0;% Convergency obtained.
- endo_simul = reshape(Y(:,1),ny,periods+2);
- break
- end
- A2 = [nzA{:}]';
- A = [A1; sparse(A2(:,1),A2(:,2),A2(:,3),ny*(periods-order-1)*world_nbr,dimension)];
- dy = -A\res(i_upd_r);
- Y(i_upd_y) = Y(i_upd_y) + dy;
-end
-
-if ~stop
- if verbose
- fprintf('\n') ;
- disp([' Total time of simulation :' num2str(etime(clock,h1))]) ;
- fprintf('\n') ;
- disp('WARNING : maximum number of iterations is reached (modify options_.simul.maxit).') ;
- fprintf('\n') ;
- end
- flag = 1;% more iterations are needed.
- endo_simul = 1;
-end
-if verbose
- disp ('-----------------------------------------------------') ;
-end
--
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