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41 results

ax_lapack.m4

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  • Forked from Dynare / dynare
    Source project has a limited visibility.
    get_variance_of_endogenous_variables.m 1.73 KiB
    function vx1 = get_variance_of_endogenous_variables(dr,i_var)
    
    % function vx1 = get_variance_of_endogenous_variables(dr,i_var)
    % Gets the variance of a variables subset
    %
    % INPUTS
    %    dr:        structure of decisions rules for stochastic simulations
    %    i_var:     indices of a variables list
    %        
    % OUTPUTS
    %    vx1:       variance-covariance matrix
    %        
    % SPECIAL REQUIREMENTS
    %    none
    
    % Copyright (C) 2003-2012 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_
    
    endo_nbr = M_.endo_nbr;
    
    Sigma_e = M_.Sigma_e;
    
    nstatic = M_.nstatic;
    nspred = M_.nspred;
    ghx = dr.ghx(i_var,:);
    ghu = dr.ghu(i_var,:);
    nc = size(ghx,2);
    n = length(i_var);
    
    [A,B] = kalman_transition_matrix(dr,nstatic+(1:nspred),1:nc,M_.exo_nbr);
    
    [vx,u] = lyapunov_symm(A,B*Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold);
    
    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*Sigma_e*ghu';