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function [ysim, xsim, errorflag] = simul_backward_nonlinear_model_(initialconditions, samplesize, options_, M_, oo_, innovations, dynamic_resid, dynamic_g1)
% [ysim, xsim, errorflag] = simul_backward_nonlinear_model_(initialconditions, samplesize, options_, M_, oo_, innovations, dynamic_resid, dynamic_g1)
% Simulates a stochastic non linear backward looking model with arbitrary precision (a deterministic solver is used).
%
% INPUTS
% - initial_conditions [dseries] initial conditions for the endogenous variables.
% - sample_size [integer] scalar, number of periods for the simulation.
% - options_ [struct] Dynare's options_ global structure.
% - M_ [struct] Dynare's M_ global structure.
% - oo_ [struct] Dynare's oo_ global structure.
% - innovations [double] T*q matrix, innovations to be used for the simulation.
% - dynamic_resid
% - dynamic_g1
%
% OUTPUTS
% - oo_ [struct] Dynare's oo_ global structure.
% - errorflag [logical] scalar, equal to false iff the simulation did not fail.
%
% REMARKS
% [1] The innovations used for the simulation are saved in oo_.exo_simul, and the resulting paths for the endogenous
% variables are saved in oo_.endo_simul.
% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
% through the shocks block.
% [3] If the first input argument is empty, the endogenous variables are initialized with 0, or if available with the informations
% provided thrtough the histval block.
% Copyright © 2017-2023 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/>.
debug = false;
errorflag = false;
if ~isempty(innovations)
oo_.exo_simul(initialconditions.nobs+(1:samplesize),:) = innovations;
end
if ismember(options_.solve_algo, [12,14])
[funcs, feedback_vars_idxs] = setup_time_recursive_block_simul(M_);
end
function [r, J] = block_wrapper(z, feedback_vars_idx, func, y_dynamic, x, sparse_rowval, sparse_colval, sparse_colptr, T)
% NB: do as few computations as possible inside this function, since it is
% called a very large number of times
y_dynamic(feedback_vars_idx) = z;
[~, ~, r, J] = feval(func, y_dynamic, x, M_.params, oo_.steady_state, ...
sparse_rowval, sparse_colval, sparse_colptr, T);
end
% Simulations (call a Newton-like algorithm for each period).
for it = initialconditions.nobs+(1:samplesize)
if debug
dprintf('Period t = %s.', num2str(it-initialconditions.nobs));
end
y_ = oo_.endo_simul(:,it-1);
y = y_; % A good guess for the initial conditions is the previous values for the endogenous variables.
x = oo_.exo_simul(it,:);
try
if ismember(options_.solve_algo, [12,14])
T = NaN(M_.block_structure.dyn_tmp_nbr);
y_dynamic = [y_; y; NaN(M_.endo_nbr, 1)];
for blk = 1:length(M_.block_structure.block)
sparse_rowval = M_.block_structure.block(blk).g1_sparse_rowval;
sparse_colval = M_.block_structure.block(blk).g1_sparse_colval;
sparse_colptr = M_.block_structure.block(blk).g1_sparse_colptr;
if M_.block_structure.block(blk).Simulation_Type ~= 1 % Not an evaluate forward block
[z, errorflag, ~, ~, errorcode] = dynare_solve(@block_wrapper, y_dynamic(feedback_vars_idxs{blk}), ...
options_.simul.maxit, options_.dynatol.f, ...
options_.dynatol.x, options_, ...
feedback_vars_idxs{blk}, funcs{blk}, y_dynamic, x, sparse_rowval, sparse_colval, sparse_colptr, T);
if errorflag
error('Nonlinear solver routine failed with errorcode=%i in block %i and period %i.', errorcode, blk, it)
end
y_dynamic(feedback_vars_idxs{blk}) = z;
end
%% Compute endogenous if the block is of type evaluate or if there are recursive variables in a solve block.
%% Also update the temporary terms vector.
[y_dynamic, T] = feval(funcs{blk}, y_dynamic, x, M_.params, ...
oo_.steady_state, sparse_rowval, sparse_colval, ...
sparse_colptr, T);
end
oo_.endo_simul(:,it) = y_dynamic(M_.endo_nbr+(1:M_.endo_nbr));
else
[oo_.endo_simul(:,it), errorflag, ~, ~, errorcode] = ...
dynare_solve(@dynamic_backward_model_for_simulation, y, ...
options_.simul.maxit, options_.dynatol.f, options_.dynatol.x, ...
options_, dynamic_resid, dynamic_g1, y_, x, M_.params, oo_.steady_state, M_.dynamic_g1_sparse_rowval, M_.dynamic_g1_sparse_colval, M_.dynamic_g1_sparse_colptr);
if errorflag
error('Nonlinear solver routine failed with errorcode=%i in period %i.', errorcode, it)
end
end
catch Error
errorflag = true;
oo_.endo_simul = oo_.endo_simul(:, 1:it-1);
dprintf('Newton failed on iteration i = %s.', num2str(it-initialconditions.nobs));
ytm = oo_.endo_simul(:,end);
xtt = oo_.exo_simul(it,:);
skipline()
dprintf('Values of the endogenous variables before the nonlinear solver failure')
dprintf('----------------------------------------------------------------------')
skipline()
dyntable(options_, '', {'VARIABLES','VALUES'}, M_.endo_names(1:M_.orig_endo_nbr), ytm(1:M_.orig_endo_nbr), [], [], 6)
skipline()
dprintf('Values of the exogenous variables before the nonlinear solver failure')
dprintf('---------------------------------------------------------------------')
skipline()
dyntable(options_, '', {'VARIABLES','VALUES'}, M_.exo_names, transpose(oo_.exo_simul(it,:)), [], [], 6)
skipline(2)
%
% Get equation tags if any
%
if isfield(M_, 'equations_tags')
etags = cell(M_.orig_endo_nbr, 1);
for i = 1:M_.orig_endo_nbr
equations_tags = M_.equations_tags(cellfun(@(x) isequal(x, i), M_.equations_tags(:,1)), :);
name = equations_tags(strcmpi(equations_tags(:,2), 'name'),:);
if isempty(name)
eqtags{i} = int2str(i);
else
if rows(name)>1
error('Something is wrong in the equation tags.')
else
eqtags(i) = name(3);
end
end
end
else
etags = split(int2str(1:M_.orig_endo_nbr), ' ');
end
%
% Evaluate and check the residuals
%
r = dynamic_backward_model_for_simulation(ytm, dynamic_resid, dynamic_g1, ytm, x, M_.params, oo_.steady_state, M_.dynamic_g1_sparse_rowval, M_.dynamic_g1_sparse_colval, M_.dynamic_g1_sparse_colptr);
residuals_evaluating_to_nan = isnan(r);
residuals_evaluating_to_inf = isinf(r);
residuals_evaluating_to_complex = ~isreal(r);
if any(residuals_evaluating_to_nan)
dprintf('Following equations are evaluating to NaN:')
skipline()
display_names_of_problematic_equations(M_, eqtags, residuals_evaluating_to_nan);
skipline()
end
if any(residuals_evaluating_to_inf)
dprintf('Following equations are evaluating to Inf:')
skipline()
display_names_of_problematic_equations(M_, eqtags, residuals_evaluating_to_inf);
skipline()
end
if any(residuals_evaluating_to_complex)
dprintf('Following equations are evaluating to a complex number:')
skipline()
display_names_of_problematic_equations(M_, eqtags, residuals_evaluating_to_complex);
skipline()
end
dprintf('Newton failed in period %s with the following error message:', char(initialconditions.lastdate+(it-initialconditions.nobs)));
skipline()
dprintf('\t %s', Error.message);
skipline()
break
% TODO Implement same checks with the jacobian matrix.
% TODO Modify other solvers to return an exitflag.
end
end
ysim = oo_.endo_simul(1:M_.orig_endo_nbr,:);
xsim = oo_.exo_simul;
end
function display_names_of_problematic_equations(M_, eqtags, TruthTable)
for i=1:M_.orig_endo_nbr
if TruthTable(i)
dprintf(' - %s', eqtags{i})
end
end
for i=M_.orig_endo_nbr+1:M_.endo_nbr
if TruthTable(i)
dprintf(' - Auxiliary equation for %s', M_.endo_names{i})
end
end
end
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