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function simulation = simul_static_model(samplesize, innovations)
% Simulates a stochastic static model (with arbitrary precision).
%
% INPUTS
% - samplesize [integer] scalar, number of periods for the simulation.
% - innovations [dseries] innovations to be used for the simulation.
%
% OUTPUTS
% - simulation [dseries] Simulated endogenous and exogenous variables.
%
% REMARKS
% [1] The innovations used for the simulation are saved in DynareOutput.exo_simul, and the resulting paths for the endogenous
% variables are saved in DynareOutput.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.
% Copyright (C) 2019 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_ options_ oo_
if M_.maximum_lag
error('%s.mod has lagged variables, but it should be a static model.', M_.fname)
end
if M_.maximum_lead
error('%s.mod has leaded variables, but it should be a static model.', M_.fname)
end
% Set innovations.
if nargin<2 || isempty(innovations)
% Set the covariance matrix of the structural innovations.
variances = diag(M_.Sigma_e);
number_of_shocks = length(M_.Sigma_e);
positive_var_indx = find(variances>0);
effective_number_of_shocks = length(positive_var_indx);
covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx);
covariance_matrix_upper_cholesky = chol(covariance_matrix);
% Set seed to its default state.
if options_.bnlms.set_dynare_seed_to_default
set_dynare_seed('default');
end
% Simulate structural innovations.
switch options_.bnlms.innovation_distribution
case 'gaussian'
oo_.bnlms.shocks = randn(samplesize, effective_number_of_shocks)*covariance_matrix_upper_cholesky;
otherwise
error('%s distribution for the structural innovations is not (yet) implemented!', options_.bnlms.innovation_distribution)
end
% Put the simulated innovations in DynareOutput.exo_simul.
oo_.exo_simul = zeros(samplesize, number_of_shocks);
oo_.exo_simul(:,positive_var_indx) = oo_.bnlms.shocks;
innovations = [];
else
if innovations.nobs<samplesize
error('Time span in third argument is too short (should not be less than %s, the value of the second argument)', num2str(samplesize))
end
% Set array holding innovations values.
Innovations = zeros(samplesize, M_.exo_nbr);
exonames = M_.exo_names;
for i=1:M_.exo_nbr
if ismember(exonames{i}, innovations.name)
Innovations(:,i) = innovations{exonames{i}}.data(1:samplesize);
else
dprintf('Exogenous variable %s is not available in third argument, default value is zero.', exonames{i});
end
end
oo_.exo_simul = Innovations;
end
staticmodel = sprintf('%s.static', M_.fname);
% Simulations (call a Newton-like algorithm for each period).
for t=1:samplesize
y = zeros(M_.endo_nbr, 1);
[oo_.endo_simul(:,t), info] = dynare_solve(staticmodel, y, options_, oo_.exo_simul(t,:), M_.params);
if info
error('Newton failed!')
end
end
ysim = oo_.endo_simul(1:M_.orig_endo_nbr,:);
xsim = oo_.exo_simul;
initperiod = dates('1Y');
if isdseries(innovations)
initperiod = innovations.dates(1);
end
simulation = [dseries(ysim', initperiod, M_.endo_names(1:M_.orig_endo_nbr)), dseries(xsim, initperiod, M_.exo_names)];
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