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function model_info(varargin)
%function model_info;
% Copyright (C) 2008-2017 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 <http://www.gnu.org/licenses/>.
global M_;
if sum(strcmp(varargin,'static')) > 0
static_ = 1;
else
static_ = 0;
end
if sum(strcmp(varargin,'incidence')) > 0
incidence = 1;
else
incidence = 0;
end
if static_
fprintf(' Informations about %s (static model)\n',M_.fname);
block_structre_str = 'block_structure_stat';
nb_leadlag = 1;
else
fprintf(' Informations about %s (dynamic model)\n',M_.fname);
block_structre_str = 'block_structure';
nb_leadlag = 3;
end
if(isfield(M_,block_structre_str))
if static_
block_structure = M_.block_structure_stat;
else
block_structure = M_.block_structure;
end
fprintf(strcat(' ===================',char(ones(1,length(M_.fname))*'='),'\n\n'));
nb_blocks=length(block_structure.block);
fprintf('The model has %d equations and is decomposed in %d blocks as follow:\n',M_.endo_nbr,nb_blocks);
fprintf('===============================================================================================================\n');
fprintf('| %10s | %10s | %30s | %14s | %31s |\n','Block no','Size','Block Type',' Equation','Dependent variable');
fprintf('|============|============|================================|================|=================================|\n');
for i=1:nb_blocks
size_block=length(block_structure.block(i).equation);
if(i>1)
fprintf('|------------|------------|--------------------------------|----------------|---------------------------------|\n');
end
for j=1:size_block
if(j==1)
fprintf('| %10d | %10d | %30s | %14d | %-6d %24s |\n',i,size_block,Sym_type(block_structure.block(i).Simulation_Type),block_structure.block(i).equation(j),block_structure.block(i).variable(j),M_.endo_names(block_structure.block(i).variable(j),:));
else
fprintf('| %10s | %10s | %30s | %14d | %-6d %24s |\n','','','',block_structure.block(i).equation(j),block_structure.block(i).variable(j),M_.endo_names(block_structure.block(i).variable(j),:));
end
end
end
fprintf('===============================================================================================================\n');
fprintf('\n');
if static_
fprintf('%-30s %s','the variable','is used in equations Contemporaneously');
if(size(block_structure.incidence.sparse_IM,1)>0)
IM=sortrows(block_structure.incidence.sparse_IM,2);
else
IM=[];
end
size_IM=size(IM,1);
last=99999999;
for i=1:size_IM
if(last~=IM(i,2))
fprintf('\n%-30s',M_.endo_names(IM(i,2),:));
end
fprintf(' %5d',IM(i,1));
last=IM(i,2);
end
fprintf('\n\n');
else
for k=1:M_.maximum_endo_lag+M_.maximum_endo_lead+1
if(k==M_.maximum_endo_lag+1)
fprintf('%-30s %s','the variable','is used in equations Contemporaneously');
elseif(k<M_.maximum_endo_lag+1)
fprintf('%-30s %s %d','the variable','is used in equations with lag ',M_.maximum_endo_lag+1-k);
else
fprintf('%-30s %s %d','the variable','is used in equations with lead ',k-(M_.maximum_endo_lag+1));
end
if(size(block_structure.incidence(k).sparse_IM,1)>0)
IM=sortrows(block_structure.incidence(k).sparse_IM,2);
else
IM=[];
end
size_IM=size(IM,1);
last=99999999;
for i=1:size_IM
if(last~=IM(i,2))
fprintf('\n%-30s',M_.endo_names(IM(i,2),:));
end
fprintf(' %5d',IM(i,1));
last=IM(i,2);
end
fprintf('\n\n');
end
end
%printing the gross incidence matrix
IM_star = char([kron(ones(M_.endo_nbr, M_.endo_nbr-1), double(blanks(3))) double(blanks(M_.endo_nbr)')]);
for i = 1:nb_leadlag
n = size(block_structure.incidence(i).sparse_IM,1);
for j = 1:n
if ismember(block_structure.incidence(i).sparse_IM(j,2), M_.state_var)
IM_star(block_structure.incidence(i).sparse_IM(j,1), 3 * (block_structure.incidence(i).sparse_IM(j,2) - 1) + 1) = 'X';
else
IM_star(block_structure.incidence(i).sparse_IM(j,1), 3 * (block_structure.incidence(i).sparse_IM(j,2) - 1) + 1) = '1';
end
end
end
seq = 1: M_.endo_nbr;
blank = [ blanks(size(M_.endo_names,2)); blanks(size(M_.endo_names,2))];
for i = 1:M_.endo_nbr
if i == 1
var_names = [blank; M_.endo_names(i,:)];
else
var_names = [var_names; blank; M_.endo_names(i,:)];
end
end
if incidence
topp = [char(kron(double(blanks(ceil(log10(M_.endo_nbr)))),ones(size(M_.endo_names,2),1))) var_names' ];
bott = [int2str(seq') blanks(M_.endo_nbr)' blanks(M_.endo_nbr)' IM_star];
fprintf('\n Gross incidence matrix\n');
fprintf(' =======================\n');
disp([topp; bott]);
%printing the reordered incidence matrix
IM_star_reordered = char([kron(ones(M_.endo_nbr, M_.endo_nbr-1), double(blanks(3))) double(blanks(M_.endo_nbr)')]);
eq(block_structure.equation_reordered) = seq;
va(block_structure.variable_reordered) = seq;
barre_blank = [ barre(size(M_.endo_names,2)); blanks(size(M_.endo_names,2))];
cur_block = 1;
for i = 1:M_.endo_nbr
past_block = cur_block;
while ismember(block_structure.variable_reordered(i), block_structure.block(cur_block).variable) == 0
cur_block = cur_block + 1;
end
if i == 1
var_names = [blank; M_.endo_names(block_structure.variable_reordered(i),:)];
else
if past_block ~= cur_block
var_names = [var_names; barre_blank; M_.endo_names(block_structure.variable_reordered(i),:)];
else
var_names = [var_names; blank; M_.endo_names(block_structure.variable_reordered(i),:)];
end
end
end
topp = [char(kron(double(blanks(ceil(log10(M_.endo_nbr)))),ones(size(M_.endo_names,2),1))) var_names' ];
n_state_var = length(M_.state_var);
IM_state_var = zeros(n_state_var, n_state_var);
inv_variable_reordered(block_structure.variable_reordered) = 1:M_.endo_nbr;
state_equation = block_structure.equation_reordered(inv_variable_reordered(M_.state_var));
for i = 1:nb_leadlag
n = size(block_structure.incidence(i).sparse_IM,1);
for j = 1:n
[tf, loc] = ismember(block_structure.incidence(i).sparse_IM(j,2), M_.state_var);
if tf
IM_star_reordered(eq(block_structure.incidence(i).sparse_IM(j,1)), 3 * (va(block_structure.incidence(i).sparse_IM(j,2)) - 1) + 1) = 'X';
[tfi, loci] = ismember(block_structure.incidence(i).sparse_IM(j,1), state_equation);
if tfi
IM_state_var(loci, loc) = 1;
end
else
IM_star_reordered(eq(block_structure.incidence(i).sparse_IM(j,1)), 3 * (va(block_structure.incidence(i).sparse_IM(j,2)) - 1) + 1) = '1';
end
end
end
fprintf('1: non nul element, X: non nul element related to a state variable\n');
cur_block = 1;
i_last = 0;
block = {};
for i = 1:n_state_var
past_block = cur_block;
while ismember(M_.state_var(i), block_structure.block(cur_block).variable) == 0
cur_block = cur_block + 1;
end
if (past_block ~= cur_block) || (past_block == cur_block && i == n_state_var)
block(past_block).IM_state_var(1:(i - 1 - i_last), 1:i - 1) = IM_state_var(i_last+1:i - 1, 1:i - 1);
i_last = i - 1;
end
end
cur_block = 1;
for i = 1:M_.endo_nbr
past_block = cur_block;
while ismember(block_structure.variable_reordered(i), block_structure.block(cur_block).variable) == 0
cur_block = cur_block + 1;
end
if past_block ~= cur_block
for j = 1:i-1
IM_star_reordered(j, 3 * (i - 1) - 1) = '|';
end
end
end
bott = [int2str(block_structure.equation_reordered') blanks(M_.endo_nbr)' blanks(M_.endo_nbr)' IM_star_reordered];
fprintf('\n Reordered incidence matrix\n');
fprintf(' ==========================\n');
disp([topp; bott]);
fprintf('1: non nul element, X: non nul element related to a state variable\n');
end
else
fprintf('There is no block decomposition of the model.\nUse ''block'' model''s option.\n');
end
function ret=Sym_type(type)
UNKNOWN=0;
EVALUATE_FORWARD=1;
EVALUATE_BACKWARD=2;
SOLVE_FORWARD_SIMPLE=3;
SOLVE_BACKWARD_SIMPLE=4;
SOLVE_TWO_BOUNDARIES_SIMPLE=5;
SOLVE_FORWARD_COMPLETE=6;
SOLVE_BACKWARD_COMPLETE=7;
SOLVE_TWO_BOUNDARIES_COMPLETE=8;
EVALUATE_FORWARD_R=9;
EVALUATE_BACKWARD_R=10;
switch (type)
case (UNKNOWN)
ret='UNKNOWN ';
case {EVALUATE_FORWARD,EVALUATE_FORWARD_R}
ret='EVALUATE FORWARD ';
case {EVALUATE_BACKWARD,EVALUATE_BACKWARD_R}
ret='EVALUATE BACKWARD ';
case SOLVE_FORWARD_SIMPLE
ret='SOLVE FORWARD SIMPLE ';
case SOLVE_BACKWARD_SIMPLE
ret='SOLVE BACKWARD SIMPLE ';
case SOLVE_TWO_BOUNDARIES_SIMPLE
ret='SOLVE TWO BOUNDARIES SIMPLE ';
case SOLVE_FORWARD_COMPLETE
ret='SOLVE FORWARD COMPLETE ';
case SOLVE_BACKWARD_COMPLETE
ret='SOLVE BACKWARD COMPLETE ';
case SOLVE_TWO_BOUNDARIES_COMPLETE
ret='SOLVE TWO BOUNDARIES COMPLETE';
end
function ret = barre(n)
s = [];
for i=1:n
s = [s '|'];
end
ret = s;
|
