215 lines
8.5 KiB
Matlab
215 lines
8.5 KiB
Matlab
function [info, M_, options_, oo_, ReducedForm] = ...
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solve_model_for_online_filter(setinitialcondition, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_)
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% Solves the dsge model for an particular parameters set.
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%
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% INPUTS
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% - setinitialcondition [logical] return initial condition if true.
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% - xparam1 [double] n×1 vector, parameter values.
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% - dataset_ [struct] Dataset for estimation.
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% - options_ [struct] Dynare options.
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% - M_ [struct] Model description.
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% - estim_params_ [struct] Estimated parameters.
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% - bayestopt_ [struct] Prior definition.
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% - oo_ [struct] Dynare results.
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%
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% OUTPUTS
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% - info [integer] scalar, nonzero if any problem occur when computing the reduced form.
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% - M_ [struct] M_ description.
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% - options_ [struct] Dynare options.
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% - oo_ [struct] Dynare results.
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% - ReducedForm [struct] Reduced form model.
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% Copyright © 2013-2023 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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persistent init_flag restrict_variables_idx state_variables_idx mf0 mf1 number_of_state_variables
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info = 0;
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%----------------------------------------------------
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% 1. Get the structural parameters & define penalties
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%----------------------------------------------------
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% Test if some parameters are smaller than the lower bound of the prior domain.
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if any(xparam1<bounds.lb)
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info = 41;
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return
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end
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% Test if some parameters are greater than the upper bound of the prior domain.
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if any(xparam1>bounds.ub)
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info = 42;
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return
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end
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% Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
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Q = M_.Sigma_e;
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H = M_.H;
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for i=1:estim_params_.nvx
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k =estim_params_.var_exo(i,1);
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Q(k,k) = xparam1(i)*xparam1(i);
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end
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offset = estim_params_.nvx;
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if estim_params_.nvn
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for i=1:estim_params_.nvn
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H(i,i) = xparam1(i+offset)*xparam1(i+offset);
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end
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offset = offset+estim_params_.nvn;
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else
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H = zeros(size(dataset_.data, 2));
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end
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% Get the off-diagonal elements of the covariance matrix for the structural innovations. Test if Q is positive definite.
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if estim_params_.ncx
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for i=1:estim_params_.ncx
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k1 =estim_params_.corrx(i,1);
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k2 =estim_params_.corrx(i,2);
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Q(k1,k2) = xparam1(i+offset)*sqrt(Q(k1,k1)*Q(k2,k2));
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Q(k2,k1) = Q(k1,k2);
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end
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% Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
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[~, testQ] = chol(Q);
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if testQ
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% The variance-covariance matrix of the structural innovations is not definite positive.
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info = 43;
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return
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end
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offset = offset+estim_params_.ncx;
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end
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% Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite.
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if estim_params_.ncn
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corrn_observable_correspondence = estim_params_.corrn_observable_correspondence;
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for i=1:estim_params_.ncn
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k1 = corrn_observable_correspondence(i,1);
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k2 = corrn_observable_correspondence(i,2);
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H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
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H(k2,k1) = H(k1,k2);
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end
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% Try to compute the cholesky decomposition of H (possible iff H is positive definite)
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[~, testH] = chol(H);
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if testH
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% The variance-covariance matrix of the measurement errors is not definite positive.
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info = 44;
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return
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end
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offset = offset+estim_params_.ncn;
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end
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% Update estimated structural parameters in Mode.params.
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if estim_params_.np > 0
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M_.params(estim_params_.param_vals(:,1)) = xparam1(offset+1:end);
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end
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% Update M_.Sigma_e and M_.H.
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M_.Sigma_e = Q;
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M_.H = H;
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%------------------------------------------------------------------------------
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% 2. call model setup & reduction program
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%------------------------------------------------------------------------------
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warning('off', 'MATLAB:nearlySingularMatrix')
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[~, ~, ~, info, oo_.dr, M_.params] = ...
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dynare_resolve(M_, options_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, 'restrict');
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warning('on', 'MATLAB:nearlySingularMatrix')
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if info(1)~=0
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if nargout==5
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ReducedForm = 0;
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end
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return
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end
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% Get decision rules and transition equations.
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dr = oo_.dr;
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% Set persistent variables (first call).
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if isempty(init_flag)
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mf0 = bayestopt_.mf0;
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mf1 = bayestopt_.mf1;
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restrict_variables_idx = dr.restrict_var_list;
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state_variables_idx = restrict_variables_idx(mf0);
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number_of_state_variables = length(mf0);
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init_flag = true;
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end
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% Return reduced form model.
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if nargout>4
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ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
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ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
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ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
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if options_.order==2
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ReducedForm.use_k_order_solver = false;
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ReducedForm.ghxx = dr.ghxx(restrict_variables_idx,:);
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ReducedForm.ghuu = dr.ghuu(restrict_variables_idx,:);
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ReducedForm.ghxu = dr.ghxu(restrict_variables_idx,:);
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ReducedForm.constant = ReducedForm.steadystate + .5*dr.ghs2(restrict_variables_idx);
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ReducedForm.ghs2 = dr.ghs2(restrict_variables_idx,:);
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elseif options_.order>=3
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ReducedForm.use_k_order_solver = true;
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ReducedForm.dr = dr;
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ReducedForm.udr = folded_to_unfolded_dr(dr, M_, options_);
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else
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n_states=size(dr.ghx,2);
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n_shocks=size(dr.ghu,2);
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ReducedForm.use_k_order_solver = false;
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ReducedForm.ghxx = zeros(size(restrict_variables_idx,1),n_states^2);
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ReducedForm.ghuu = zeros(size(restrict_variables_idx,1),n_shocks^2);
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ReducedForm.ghxu = zeros(size(restrict_variables_idx,1),n_states*n_shocks);
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ReducedForm.constant = ReducedForm.steadystate;
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end
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ReducedForm.state_variables_steady_state = dr.ys(dr.order_var(state_variables_idx));
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ReducedForm.Q = Q;
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ReducedForm.H = H;
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ReducedForm.mf0 = mf0;
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ReducedForm.mf1 = mf1;
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end
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% Set initial condition
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if setinitialcondition
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switch options_.particle.initialization
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case 1% Initial state vector covariance is the ergodic variance associated to the first order Taylor-approximation of the model.
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StateVectorMean = ReducedForm.state_variables_steady_state;%.constant(mf0);
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[A,B] = kalman_transition_matrix(dr,dr.restrict_var_list,dr.restrict_columns);
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StateVectorVariance = lyapunov_symm(A, B*ReducedForm.Q*B', options_.lyapunov_fixed_point_tol, ...
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options_.qz_criterium, options_.lyapunov_complex_threshold, [], options_.debug);
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StateVectorVariance = StateVectorVariance(mf0,mf0);
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case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
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StateVectorMean = ReducedForm.state_variables_steady_state;%.constant(mf0);
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old_DynareOptionsperiods = options_.periods;
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options_.periods = 5000;
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old_DynareOptionspruning = options_.pruning;
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options_.pruning = options_.particle.pruning;
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y_ = simult(dr.ys, dr, M_, options_);
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y_ = y_(dr.order_var(state_variables_idx),2001:options_.periods);
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StateVectorVariance = cov(y_');
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options_.periods = old_DynareOptionsperiods;
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options_.pruning = old_DynareOptionspruning;
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clear('old_DynareOptionsperiods','y_');
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case 3% Initial state vector covariance is a diagonal matrix.
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StateVectorMean = ReducedForm.state_variables_steady_state;%.constant(mf0);
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StateVectorVariance = options_.particle.initial_state_prior_std*eye(number_of_state_variables);
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otherwise
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error('Unknown initialization option!')
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end
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ReducedForm.StateVectorMean = StateVectorMean;
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ReducedForm.StateVectorVariance = StateVectorVariance;
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end
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