1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
|
function [info_convergence, endo_simul] = extended_path_homotopy(endo_simul, exo_simul, M_, options_, oo_, pfm, ep, order, algo, method, debug)
% Copyright © 2016-2023 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 <https://www.gnu.org/licenses/>.
endo_simul0 = endo_simul;
if ismember(method, [1, 2])
noconvergence = true;
iteration = 0;
weight = .1;
maxiter = 100;
increase_flag = false;
increase_factor = 1.2;
decrease_factor = 1.1;
state = false(5,1);
oldweight = weight;
while noconvergence
iteration = iteration + 1;
oo_.endo_simul = endo_simul;
oo_.endo_simul(:,1) = oo_.steady_state + weight*(endo_simul0(:,1) - oo_.steady_state);
oo_.exo_simul = bsxfun(@plus, weight*exo_simul, (1-weight)*transpose(oo_.exo_steady_state));
if order==0
[endo_simul_new, success] = perfect_foresight_solver_core(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
else
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);
case 1
[flag, endo_simul_new] = ...
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
end
end
if isequal(order, 0)
% Logical variable flag is false iff the solver fails.
flag = success;
else
% Fix convention issue on the value of flag.
flag = ~flag;
end
if debug
dprintf('%u\t %1.8f\t %u', iteration, weight, flag)
end
state(2:end) = state(1:end-1);
state(1) = flag;
if flag
if isequal(weight, 1)
noconvergence = false;
break
end
if all(state)
increase_factor = 1+(increase_factor-1)*1.1;
state = false(size(state));
end
oldweight = weight;
weight = min(weight*increase_factor, 1);
increase_flag = true;
endo_simul = endo_simul_new;
else
if increase_flag
weight = oldweight + (weight-oldweight)/100;
else
weight = min(weight/decrease_factor, 1);
end
end
if iteration>maxiter
break
end
if weight<1e-9
break
end
end
info_convergence = ~noconvergence;
end
if isequal(method, 3) || (isequal(method, 2) && noconvergence)
if isequal(method, 2)
endo_simul = endo_simul0;
end
weights = 0:(1/1000):1;
noconvergence = true;
index = 1;
jndex = 0;
nweights = length(weights);
while noconvergence
weight = weights(index);
oo_.endo_simul = endo_simul;
oo_.exo_simul = bsxfun(@plus, weight*exo_simul, (1-weight)*transpose(oo_.exo_steady_state));
if order==0
[endo_simul_new, success] = perfect_foresight_solver_core(oo_.endo_simul, oo_.exo_simul, oo_.steady_state, oo_.exo_steady_state, M_, options_);
else
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);
case 1
[flag, endo_simul_new] = ...
solve_stochastic_perfect_foresight_model_1(endo_simul, exo_simul, options_, pfm, ep.stochastic.order);
end
end
if isequal(order, 0)
% Logical variable flag is false iff the solver fails.
flag = success;
else
% Fix convention issue on the value of flag.
flag = ~flag;
end
if debug
dprintf('%u\t %1.8f\t %u', index, weight, flag)
end
if flag
jndex = index;
if isequal(weight, 1)
noconvergence = false;
continue
end
index = index+1;
endo_simul = endo_simul_new;
else
break
end
end
info_convergence = ~noconvergence;
end
|
