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
140
141
142
143
144
145
|
function [initial_conditions, pfm, options_, oo_] = extended_path_initialization(initial_conditions, options_, M_, oo_)
% Initialization of the extended path routines.
%
% INPUTS
% - initial_conditions [double] m×1 array, where m is the number of endogenous variables in the model.
% - options_ [struct] Dynare's options structure
% - M_ [struct] Dynare's model structure
% - oo_ [struct] Dynare's result structure
%
% OUTPUTS
% - initial_conditions [double] m*1 array, initial conditions (if empty on input, set to steady state or histval).
% - pfm [struct] Structure for the perfect foresight
% model solver, containing:
% * positive_var_indx: indices of shocks with positive variance
% * effective_number_of_shocks: number of shocks with positive variance
% * covariance_matrix: covariance matrix of effective shocks
% * covariance_matrix_upper_cholesky: upper Cholesky factor of covariance matrix
% - options_ [struct] Modified Dynare's options structure.
% - oo_ [struct] Modified Dynare's result structure.
%
% ALGORITHM
% None.
%
% SPECIAL REQUIREMENTS
% None.
% Copyright © 2016-2026 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/>.
ep = options_.ep;
% Set verbosity levels.
options_.verbosity = ep.verbosity+ep.debug;
% Set maximum number of iterations for the deterministic solver.
options_.simul.maxit = ep.maxit;
% Prepare a structure needed by the MATLAB implementation of the perfect foresight model solver
pfm = setup_stochastic_perfect_foresight_model_solver(M_, options_, oo_);
% Check that the user did not use varexo_det
if M_.exo_det_nbr~=0
error('Extended path does not support varexo_det.')
end
% Set default initial conditions.
if isempty(initial_conditions)
if M_.maximum_lag==0
error('extended_path_initialization: you cannot use an initial condition in a model without lags.')
end
if isempty(M_.endo_histval)
initial_conditions = oo_.steady_state;
else
initial_conditions = M_.endo_histval;
end
end
% Set the number of periods for the (stochastic) perfect foresight model
pfm.periods = ep.periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
pfm.block = options_.block;
% Set the algorithm for the perfect foresight solver
options_.stack_solve_algo = ep.stack_solve_algo;
% Compute the first order reduced form if needed.
dr = struct();
if ep.use_first_order_solution_as_initial_guess
options_.order = 1;
oo_.dr=set_state_space(dr,M_);
[oo_.dr,info,M_.params] = resol(0,M_,options_,oo_.dr,oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if info(1)
print_info(info,options_.noprint,options_);
end
end
% Do not use a minimal number of periods for the perfect foresight solver (with bytecode and blocks)
options_.minimal_solving_period = options_.ep.periods;
% Set seed.
if ep.set_dynare_seed_to_default
set_dynare_seed_local_options([],false,'default');
end
% hybrid correction
pfm.hybrid_order = ep.stochastic.hybrid_order;
if pfm.hybrid_order==1
warning('extended_path:: hybrid=1 is equivalent to hybrid=0 (option value must be an integer greater than 1 to be effective).')
end
if pfm.hybrid_order>1
oo_.dr = set_state_space(oo_.dr, M_);
options = options_;
options.order = pfm.hybrid_order;
[pfm.dr, M_.params] = resol(0, M_, options, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
else
pfm.dr = [];
end
% Deactivate homotopy with SEP
if ep.stochastic.order>0
options_.no_homotopy = true;
end
% setting up integration nodes if order > 0
if ep.stochastic.order>0
[nodes,weights,nnodes] = setup_integration_nodes(options_.ep,pfm);
pfm.nodes = nodes;
pfm.weights = weights;
pfm.nnodes = nnodes;
% compute number of blocks
[pfm.block_nbr, pfm.world_nbr] = get_block_world_nbr(ep.stochastic.algo,nnodes,ep.stochastic.order,ep.periods);
end
% set boundaries if mcp
if ~ep.stochastic.order
[lb, ub] = feval(sprintf('%s.dynamic_complementarity_conditions', M_.fname), M_.params);
pfm.eq_index = M_.dynamic_mcp_equations_reordering;
if options_.ep.solve_algo == 10
options_.lmmcp.lb = repmat(lb, ep.periods, 1);
options_.lmmcp.ub = repmat(ub, ep.periods, 1);
elseif options_.ep.solve_algo == 11
options_.mcppath.lb = repmat(lb, ep.periods, 1);
options_.mcppath.ub = repmat(ub, ep.periods, 1);
end
else
% For SEP, boundaries are set in solve_stochastic_perfect_foresight_model_{0,1}.m
end
|
