Skip to content
Snippets Groups Projects
Select Git revision
  • 15ac043d1eb59a52e449f57e4ae02752de8ba98a
  • master default protected
  • julia protected
  • 6.x protected
  • python-codegen
  • llvm-15
  • 5.x protected
  • 4.6 protected
  • uop
  • rework_pac
  • aux_vars_fix
  • julia-7.0.0
  • julia-6.4.0
  • julia-6.3.0
  • julia-6.2.0
15 results

VariableDependencyGraph.cc

Blame
  • get_variance_of_endogenous_variables.m 1.97 KiB
    function vx1 = get_variance_of_endogenous_variables(M_,options_,dr,i_var)
    % vx1 = get_variance_of_endogenous_variables(dr,i_var)
    % Gets the variance of a variables subset
    %
    % INPUTS
    %   M_                        [structure]       Dynare's model structure
    %   oo_                       [structure]       Dynare's results structure
    %   options_                  [structure]       Dynare's options structure
    %   dr:                       [structure]       structure of decisions rules for stochastic simulations
    %   i_var:                    [integer]         indices of a variables list
    %
    % OUTPUTS
    %    vx1:                     [double]          variance-covariance matrix
    %
    % SPECIAL REQUIREMENTS
    %    none
    
    % Copyright © 2003-2023 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/>.
    
    ghx = dr.ghx(i_var,:);
    ghu = dr.ghu(i_var,:);
    nc = size(ghx,2);
    n = length(i_var);
    
    [A,B] = kalman_transition_matrix(dr,M_.nstatic+(1:M_.nspred),1:nc);
    
    [vx,u] = lyapunov_symm(A,B*M_.Sigma_e*B',options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, [], options_.debug);
    
    if size(u,2) > 0
        i_stat = find(any(abs(ghx*u) < options_.schur_vec_tol,2)); %only set those variances of objective function for which variance is finite
        ghx = ghx(i_stat,:);
        ghu = ghu(i_stat,:);
    else
        i_stat = (1:n)';
    end
    
    vx1 = Inf*ones(n,n);
    vx1(i_stat,i_stat) = ghx*vx*ghx'+ghu*M_.Sigma_e*ghu';