106 lines
4.2 KiB
Matlab
106 lines
4.2 KiB
Matlab
function simulation = simul_static_model(samplesize, innovations)
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% Simulates a stochastic static model (with arbitrary precision).
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%
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% INPUTS
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% - samplesize [integer] scalar, number of periods for the simulation.
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% - innovations [dseries] innovations to be used for the simulation.
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%
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% OUTPUTS
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% - simulation [dseries] Simulated endogenous and exogenous variables.
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%
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% REMARKS
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% [1] The innovations used for the simulation are saved in oo_.exo_simul, and the resulting paths for the endogenous
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% variables are saved in oo_.endo_simul.
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% [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
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% through the shocks block.
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% Copyright © 2019-2022 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
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global M_ options_ oo_
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if M_.maximum_lag
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error('%s.mod has lagged variables, but it should be a static model.', M_.fname)
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end
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if M_.maximum_lead
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error('%s.mod has leaded variables, but it should be a static model.', M_.fname)
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end
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% Set innovations.
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if nargin<2 || isempty(innovations)
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% Set the covariance matrix of the structural innovations.
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variances = diag(M_.Sigma_e);
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number_of_shocks = length(M_.Sigma_e);
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positive_var_indx = find(variances>0);
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effective_number_of_shocks = length(positive_var_indx);
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covariance_matrix = M_.Sigma_e(positive_var_indx,positive_var_indx);
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covariance_matrix_upper_cholesky = chol(covariance_matrix);
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% Set seed to its default state.
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if options_.bnlms.set_dynare_seed_to_default
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options_=set_dynare_seed_local_options(options_,'default');
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end
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% Simulate structural innovations.
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switch options_.bnlms.innovation_distribution
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case 'gaussian'
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oo_.bnlms.shocks = randn(samplesize, effective_number_of_shocks)*covariance_matrix_upper_cholesky;
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otherwise
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error('%s distribution for the structural innovations is not (yet) implemented!', options_.bnlms.innovation_distribution)
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end
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% Put the simulated innovations in oo_.exo_simul.
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oo_.exo_simul = zeros(samplesize, number_of_shocks);
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oo_.exo_simul(:,positive_var_indx) = oo_.bnlms.shocks;
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innovations = [];
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else
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if innovations.nobs<samplesize
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error('Time span in third argument is too short (should not be less than %s, the value of the second argument)', num2str(samplesize))
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end
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% Set array holding innovations values.
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Innovations = zeros(samplesize, M_.exo_nbr);
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exonames = M_.exo_names;
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for i=1:M_.exo_nbr
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if ismember(exonames{i}, innovations.name)
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Innovations(:,i) = innovations{exonames{i}}.data(1:samplesize);
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else
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dprintf('Exogenous variable %s is not available in third argument, default value is zero.', exonames{i});
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end
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end
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oo_.exo_simul = Innovations;
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end
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staticmodel = str2func(sprintf('%s.static', M_.fname));
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% Simulations (call a Newton-like algorithm for each period).
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for t=1:samplesize
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y = zeros(M_.endo_nbr, 1);
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[oo_.endo_simul(:,t), errorflag, ~, ~, errorcode] = dynare_solve(staticmodel, y, options_.simul.maxit, options_.dynatol.f, options_.dynatol.x, options_, oo_.exo_simul(t,:), M_.params);
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if errorflag
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dprintf('simul_static_mode: Nonlinear solver failed with errorcode=%i in period %i.', errorcode, t)
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oo_.endo_simul(:,t) = nan;
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end
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end
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ysim = oo_.endo_simul(1:M_.orig_endo_nbr,:);
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xsim = oo_.exo_simul;
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initperiod = dates('1Y');
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if isdseries(innovations)
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initperiod = innovations.dates(1);
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end
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simulation = [dseries(ysim', initperiod, M_.endo_names(1:M_.orig_endo_nbr)), dseries(xsim, initperiod, M_.exo_names)]; |