Do not assign variables to base workspace by default

Related to https://git.dynare.org/Dynare/preprocessor/-/issues/95

doc/manual/source/dynare-misc-commands.rst

@@ -8,6 +8,20 @@
 Dynare misc commands
 ####################
 
+.. matcomm:: send_endogenous_variables_to_workspace
+
+    Puts the simulation results for the endogenous variables stored in ``oo_.endo_simul`` 
+    into vectors with the same name as the respective variables into the base workspace.
+
+.. matcomm:: send_exogenous_variables_to_workspace
+
+    Puts the simulation results for the exogenous variables stored in ``oo_.exo_simul`` 
+    into vectors with the same name as the respective variables into the base workspace.
+
+.. matcomm:: send_irfs_to_workspace
+
+    Puts the IRFs stored in ``oo_.irfs`` into vectors with the same name into the base workspace.
+
 .. command:: prior_function(OPTIONS);
 
     Executes a user-defined function on parameter draws from the prior

doc/manual/source/the-model-file.rst

@@ -4880,14 +4880,10 @@ Computing the stochastic solution
 
     If the ``periods`` option is present, sets ``oo_.skewness``,
     ``oo_.kurtosis``, and ``oo_.endo_simul`` (see
-    :mvar:`oo_.endo_simul`), and also saves the simulated variables in
+    :mvar:`oo_.endo_simul`).
-    MATLAB/Octave vectors of the global workspace with the same name
-    as the endogenous variables.
 
     If option ``irf`` is different from zero, sets ``oo_.irfs`` (see
-    below) and also saves the IRFs in MATLAB/Octave vectors of the
+    below).
-    global workspace (this latter way of accessing the IRFs is
-    deprecated and will disappear in a future version).
 
     If the option ``contemporaneous_correlation`` is different from
     ``0``, sets ``oo_.contemporaneous_correlation``, which is
@@ -5096,10 +5092,13 @@ Computing the stochastic solution
     For example, ``oo_.irfs.gnp_ea`` contains the effect on ``gnp`` of
     a one-standard deviation shock on ``ea``.
 
-.. matcomm:: get_irf ('EXOGENOUS_NAME' [, 'ENDOGENOUS_NAME']... );
+.. matcomm:: IRF_MATRIX=get_irf ('EXOGENOUS_NAME' [, 'ENDOGENOUS_NAME']... );
 
-   |br| Given the name of an exogenous variables, returns the IRFs for the
+   |br| Given the name of an exogenous variable, returns the IRFs for the
-   requested endogenous variable(s), as they are stored in ``oo_.irfs``.
+   requested endogenous variable(s) (as they are stored in ``oo_.irfs``) in the output
+   ``IRF_MATRIX``. The periods are stored along the first dimension, with the steady
+   state in the first row. The variables are stored along the second dimension. If no
+   endogenous variables were specified, the matrix contains all variables stored in ``oo_.irfs``.
 
 The approximated solution of a model takes the form of a set of
 decision rules or transition equations expressing the current value of

matlab/accessors/get_irf.m

@@ -1,20 +1,19 @@
-
+function y0 = get_irf(exoname,varargin)
-function y0 = get_irf(exo,varargin)
+% function x = get_irf(exoname, varargin)
-% function x = get_irf(exoname, vname1, vname2, ...)
 % returns IRF to individual exogenous for a list of variables and adds the
 % steady state
 %
 % INPUTS:
-%   exo: exo variable name
+%   exoname: exo variable name
 %   vname1, vname2, ... :  list of variable names
 %
 % OUTPUTS
-%   x:      irf matrix [time x number of variables]
+%   y0:      irf matrix [time x number of variables]
 %
 % SPECIAL REQUIREMENTS
 %   none
 
-% Copyright © 2019 Dynare Team
+% Copyright © 2019-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -33,14 +32,44 @@ function y0 = get_irf(exo,varargin)
 
 global M_ oo_
 
-ys_ = [oo_.steady_state];
+if isfield(oo_,'irfs')
-y0=zeros(length(oo_.irfs.([varargin{1} '_' exo]))+1,length(varargin));
+    irf_fields=fieldnames(oo_.irfs);
-
+else
-
+    error('get_irf: No IRFs detected in oo_')
-[i_var,nvar] = varlist_indices(varargin,M_.endo_names);
-
-
-for j=1:nvar
-    %     mfys = strmatch(varargin{j},lgy_,'exact');
-    y0(:,j)=[0; oo_.irfs.([ varargin{j} '_' exo ])']+ys_(i_var(j));
 end
+
+exo_matches=find(endsWith(irf_fields,['_' exoname]));
+if isempty(exo_matches)
+    error('get_irf: No IRFs for shock %s detected in oo_',exoname)
+else
+    if nargin>1
+        endo_cell={};
+        i_var=[];
+        for var_iter=1:length(varargin)
+            temp=startsWith(irf_fields(exo_matches),varargin{var_iter});
+            if isempty(temp)
+                fprintf('get_irf: No IRF for variable %s detected in oo_',varargin{var_iter})
+            else
+                endo_cell=[endo_cell,varargin{var_iter}];
+                i_var=[i_var,strmatch(varargin(var_iter),M_.endo_names,'exact')];
+            end
+        end
+    else
+        endo_cell={};
+        i_var=[];
+        for var_iter=1:length(exo_matches)
+            endo_cell=[endo_cell,irf_fields{var_iter}(1:end-length(exoname)-1)];
+            i_var=[i_var,strmatch(endo_cell(end),M_.endo_names,'exact')];
+        end
+    end
+end
+
+ys_ = [oo_.steady_state];
+nvars=length(endo_cell);
+y0=zeros(length(oo_.irfs.([ endo_cell{1} '_' exoname ]))+1,nvars);
+
+for j=1:nvars
+    y0(:,j)=[0; oo_.irfs.([ endo_cell{j} '_' exoname ])']+ys_(i_var(j));
+end
+
+

matlab/ep/extended_path.m

@@ -1,5 +1,5 @@
-function [ts, oo_] = extended_path(initialconditions, samplesize, exogenousvariables, options_, M_, oo_)
+function [ts,oo_] = extended_path(initialconditions, samplesize, exogenousvariables, options_, M_, oo_)
-% [ts, oo_] = extended_path(initialconditions, samplesize, exogenousvariables, options_, M_, oo_)
+% [ts,oo_] = extended_path(initialconditions, samplesize, exogenousvariables, options_, M_, oo_)
 % Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
 % series of size T  is obtained by solving T perfect foresight models.
 %
@@ -13,7 +13,7 @@ function [ts, oo_] = extended_path(initialconditions, samplesize, exogenousvaria
 %
 % OUTPUTS
 %  o ts                     [dseries]   m*samplesize array, the simulations.
-%  o results                [cell]
+%  o results                [struct]    results structure
 %
 % ALGORITHM
 %
@@ -106,9 +106,4 @@ if any(isnan(endogenous_variables_paths(:)))
 end
 ts = dseries(transpose(endogenous_variables_paths), initial_period, M_.endo_names);
 
-oo_.endo_simul = transpose(ts.data);
+oo_.endo_simul = transpose(ts.data);
-assignin('base', 'Simulated_time_series', ts);
-
-if ~nargout || nargout<2
-    assignin('base', 'oo_', oo_);
-end

matlab/perfect-foresight-models/perfect_foresight_solver.m

@@ -1,4 +1,4 @@
-function oo_=perfect_foresight_solver(M_, options_, oo_, no_error_if_learnt_in_is_present, marginal_linearization_previous_raw_sims)
+function [oo_, ts]=perfect_foresight_solver(M_, options_, oo_, no_error_if_learnt_in_is_present, marginal_linearization_previous_raw_sims)
 % Computes deterministic simulations
 %
 % INPUTS
@@ -16,6 +16,7 @@ function oo_=perfect_foresight_solver(M_, options_, oo_, no_error_if_learnt_in_i
 %
 % OUTPUTS
 %   oo_                 [structure] storing the results
+%   ts                  [dseries]   final simulation paths
 %
 % ALGORITHM
 %
@@ -273,8 +274,6 @@ if ~isempty(per_block_status)
     oo_.deterministic_simulation.block = per_block_status;
 end
 
-dyn2vec(M_, oo_, options_);
-
 if isfield(oo_, 'initval_series') && ~isempty(oo_.initval_series)
     initial_period = oo_.initval_series.dates(1)+(M_.orig_maximum_lag-1);
 elseif ~isdates(options_.initial_period) && isnan(options_.initial_period)
@@ -290,8 +289,6 @@ if isfield(oo_, 'initval_series') && ~isempty(oo_.initval_series)
     ts = merge(oo_.initval_series{names{:}}, ts);
 end
 
-assignin('base', 'Simulated_time_series', ts);
-
 oo_.gui.ran_perfect_foresight = oo_.deterministic_simulation.status;
 
 

matlab/send_exogenous_variables_to_workspace.m

@@ -0,0 +1,25 @@
+function send_exogenous_variables_to_workspace()
+% send_exogenous_variables_to_workspace()
+% Saves all the endogenous variables in matlab's workspace.
+
+% Copyright © 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/>.
+global M_ oo_
+
+for idx = 1:M_.exo_nbr
+    assignin('base', M_.exo_names{idx}, oo_.exo_simul(:,idx))
+end

matlab/send_irfs_to_workspace.m

@@ -0,0 +1,27 @@
+function send_irfs_to_workspace()
+% Saves all the IRFs in MATLAB's workspace.
+
+% Copyright © 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/>.
+global oo_
+
+if isfield(oo_,'irfs')
+    irf_fields=fieldnames(oo_.irfs);
+    for irf_iter = 1:size(irf_fields,1)
+    assignin('base',irf_fields{irf_iter},oo_.irfs.(irf_fields{irf_iter})');
+    end
+end

matlab/stochastic_solver/stoch_simul.m

@@ -197,9 +197,6 @@ if options_.periods > 0 && ~PI_PCL_solver
         end
     end
     [oo_.endo_simul, oo_.exo_simul] = simult(y0,oo_.dr,M_,options_);
-    if ~options_.minimal_workspace
-        dyn2vec(M_, oo_, options_);
-    end
 end
 
 if ~options_.nomoments
@@ -267,8 +264,6 @@ if options_.irf
                 mylistTeX = [];
             end
             for j = 1:nvar
-                assignin('base',[M_.endo_names{i_var(j)} '_' M_.exo_names{i}],...
-                         y(i_var(j),:)');
                 oo_.irfs.([M_.endo_names{i_var(j)} '_' M_.exo_names{i}]) = y(i_var(j),:);
                 if max(abs(y(i_var(j),:))) >= options_.impulse_responses.plot_threshold
                     irfs  = cat(1,irfs,y(i_var(j),:));

preprocessor

@@ -1 +1 @@
-Subproject commit 0f397f40afd096b062010a374464aae1567ef623
+Subproject commit 638c49d96ee491a66ba775ffc79a1d6afbe8acfc

tests/analytic_derivatives/fs2000_analytic_derivation.mod

@@ -62,6 +62,7 @@ var e_m; stderr 0.005;
 end;
 
 stoch_simul(order=1,periods=200, irf=0,nomoments,noprint);
+send_endogenous_variables_to_workspace;
 save('my_data.mat','gp_obs','gy_obs');
 
 estimated_params;

tests/deterministic_simulations/pfwee.mod

@@ -46,7 +46,7 @@ verbatim;
 
 % Information arriving in period 1 (temp shock now)
 oo_.exo_simul(2,1) = 1.2;
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 
 % Information arriving in period 2 (temp shock now + permanent shock in future)
 oo_.exo_simul(3,1) = 1.3;
@@ -59,7 +59,7 @@ saved_endo = oo_.endo_simul(:, 1);
 saved_exo = oo_.exo_simul(1, :);
 oo_.endo_simul = oo_.endo_simul(:, 2:end);
 oo_.exo_simul = oo_.exo_simul(2:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -74,7 +74,7 @@ saved_endo = oo_.endo_simul(:, 1:2);
 saved_exo = oo_.exo_simul(1:2, :);
 oo_.endo_simul = oo_.endo_simul(:, 3:end);
 oo_.exo_simul = oo_.exo_simul(3:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -90,7 +90,7 @@ saved_endo = oo_.endo_simul(:, 1:5);
 saved_exo = oo_.exo_simul(1:5, :);
 oo_.endo_simul = oo_.endo_simul(:, 6:end);
 oo_.exo_simul = oo_.exo_simul(6:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_,true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 

tests/deterministic_simulations/pfwee_constant_sim_length.mod

@@ -45,7 +45,7 @@ verbatim;
 
 % Information arriving in period 1 (temp shock now)
 oo_.exo_simul(2,1) = 1.2;
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 
 % Information arriving in period 2 (temp shock now + permanent shock in future)
 oo_.exo_simul(3,1) = 1.3;
@@ -57,7 +57,7 @@ saved_endo = oo_.endo_simul(:, 1);
 saved_exo = oo_.exo_simul(1, :);
 oo_.endo_simul = oo_.endo_simul(:, 2:end);
 oo_.exo_simul = oo_.exo_simul(2:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -71,7 +71,7 @@ saved_endo = oo_.endo_simul(:, 1:2);
 saved_exo = oo_.exo_simul(1:2, :);
 oo_.endo_simul = oo_.endo_simul(:, 3:end);
 oo_.exo_simul = oo_.exo_simul(3:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -87,7 +87,7 @@ saved_endo = oo_.endo_simul(:, 1:5);
 saved_exo = oo_.exo_simul(1:5, :);
 oo_.endo_simul = oo_.endo_simul(:, 6:end);
 oo_.exo_simul = oo_.exo_simul(6:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 

tests/deterministic_simulations/pfwee_learnt_in.mod

@@ -95,7 +95,7 @@ verbatim;
 
 % Information arriving in period 1 (temp shock now and tomorrow)
 oo_.exo_simul(2:3,1) = 1.2;
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 
 % Information arriving in period 2 (temp shock now + permanent shock in future)
 oo_.exo_simul(3,1) = 1.3;
@@ -108,7 +108,7 @@ saved_endo = oo_.endo_simul(:, 1);
 saved_exo = oo_.exo_simul(1, :);
 oo_.endo_simul = oo_.endo_simul(:, 2:end);
 oo_.exo_simul = oo_.exo_simul(2:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -125,7 +125,7 @@ saved_endo = oo_.endo_simul(:, 1:2);
 saved_exo = oo_.exo_simul(1:2, :);
 oo_.endo_simul = oo_.endo_simul(:, 3:end);
 oo_.exo_simul = oo_.exo_simul(3:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 
@@ -141,7 +141,7 @@ saved_endo = oo_.endo_simul(:, 1:5);
 saved_exo = oo_.exo_simul(1:5, :);
 oo_.endo_simul = oo_.endo_simul(:, 6:end);
 oo_.exo_simul = oo_.exo_simul(6:end, :);
-perfect_foresight_solver(true);
+oo_=perfect_foresight_solver(M_, options_, oo_, true);
 oo_.endo_simul = [ saved_endo oo_.endo_simul ];
 oo_.exo_simul = [ saved_exo; oo_.exo_simul ];
 

tests/deterministic_simulations/purely_backward/ar1.mod

@@ -31,7 +31,8 @@ perfect_foresight_solver;
 if ~oo_.deterministic_simulation.status
    error('Perfect foresight simulation failed')
 end
+send_endogenous_variables_to_workspace;
 
-if max(abs(y-[1; exp(cumprod([1; rho*ones(9, 1)]))]))>options_.dynatol.x
+if max(abs(y'-[1; exp(cumprod([1; rho*ones(9, 1)]))]))>options_.dynatol.x
     error('Wrong solution!')
 end

tests/estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod

@@ -79,6 +79,7 @@ end;
 
 % Simulate data
 stoch_simul(order=@{orderApp},pruning,nodisplay,nomoments,periods=250);
+send_endogenous_variables_to_workspace;
 save('RBC_MoM_data_@{orderApp}.mat', options_.varobs{:} );
 pause(1);
 

tests/estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod

@@ -35,6 +35,7 @@ varobs n c iv;
 
 % Simulate data
 stoch_simul(order=@{orderApp},pruning,nodisplay,nomoments,periods=250,TeX);
+send_endogenous_variables_to_workspace;
 save('RBC_MoM_data_@{orderApp}.mat', options_.varobs{:} );
 pause(1);
 

tests/kalman_filter_smoother/gen_data.mod

@@ -33,6 +33,16 @@ end;
 
 stoch_simul(periods=2000,irf=0);
 
+verbatim;
+w=oo_.endo_simul(strmatch('w',M_.endo_names,'exact'),:)';
+x=oo_.endo_simul(strmatch('x',M_.endo_names,'exact'),:)';
+y=oo_.endo_simul(strmatch('y',M_.endo_names,'exact'),:)';
+z=oo_.endo_simul(strmatch('z',M_.endo_names,'exact'),:)';
+dw=oo_.endo_simul(strmatch('dw',M_.endo_names,'exact'),:)';
+dx=oo_.endo_simul(strmatch('dx',M_.endo_names,'exact'),:)';
+dy=oo_.endo_simul(strmatch('dy',M_.endo_names,'exact'),:)';
+end;
+
 plot([w x y z]);
 
 save data_algo.mat w x y z dw dx dy;

tests/kalman_filter_smoother/test_compute_Pinf_Pstar_data.mod

@@ -85,5 +85,6 @@ shocks;
 end;
 
 stoch_simul(order=1,irf=20,periods=500);
+send_endogenous_variables_to_workspace;
 
 save data_Pinf_Pstar.mat v1 v2 v3;

tests/lmmcp/rbcii.mod

@@ -58,7 +58,7 @@ perfect_foresight_solver(lmmcp, maxit=200, no_homotopy);
 if ~oo_.deterministic_simulation.status
     error('Convergence not obtained')
 end
-
+send_endogenous_variables_to_workspace;
 n = 40;
 
 figure(2);

tests/loglinear/example4_loglinear.mod

@@ -130,6 +130,7 @@ if max(max(abs(struct2array(forecasts.cond.Mean)-struct2array(conditional_foreca
 end
 
 stoch_simul(loglinear,order=1,periods=100000);
+send_endogenous_variables_to_workspace;
 if abs(mean(y)-0.0776)>0.02
     error('Simulations are wrong')
 end

tests/moments/example1_hp_test.mod

@@ -73,15 +73,16 @@ total_var_filtered=diag(oo_.var);
 oo_filtered_all_shocks=oo_;
 
 stoch_simul(order=1,nofunctions,hp_filter=0,periods=2500000,nomoments);
+send_endogenous_variables_to_workspace;
 options_.nomoments=0;
 oo_unfiltered_all_shocks=oo_;
 
-[junk, y_filtered]=sample_hp_filter(y,1600);
+[junk, y_filtered]=sample_hp_filter(y',1600);
-[junk, c_filtered]=sample_hp_filter(c,1600);
+[junk, c_filtered]=sample_hp_filter(c',1600);
-[junk, k_filtered]=sample_hp_filter(k,1600);
+[junk, k_filtered]=sample_hp_filter(k',1600);
-[junk, a_filtered]=sample_hp_filter(a,1600);
+[junk, a_filtered]=sample_hp_filter(a',1600);
-[junk, h_filtered]=sample_hp_filter(h,1600);
+[junk, h_filtered]=sample_hp_filter(h',1600);
-[junk, b_filtered]=sample_hp_filter(b,1600);
+[junk, b_filtered]=sample_hp_filter(b',1600);
 
 verbatim;
 total_std_all_shocks_filtered_sim=std([y_filtered c_filtered k_filtered a_filtered h_filtered b_filtered]);
@@ -108,14 +109,15 @@ total_var_filtered_one_shock=diag(oo_.var);
 oo_filtered_one_shock=oo_;
 
 stoch_simul(order=1,nofunctions,hp_filter=0,periods=2500000,nomoments);
+send_endogenous_variables_to_workspace;
 oo_unfiltered_one_shock=oo_;
 
-[junk, y_filtered]=sample_hp_filter(y,1600);
+[junk, y_filtered]=sample_hp_filter(y',1600);
-[junk, c_filtered]=sample_hp_filter(c,1600);
+[junk, c_filtered]=sample_hp_filter(c',1600);
-[junk, k_filtered]=sample_hp_filter(k,1600);
+[junk, k_filtered]=sample_hp_filter(k',1600);
-[junk, a_filtered]=sample_hp_filter(a,1600);
+[junk, a_filtered]=sample_hp_filter(a',1600);
-[junk, h_filtered]=sample_hp_filter(h,1600);
+[junk, h_filtered]=sample_hp_filter(h',1600);
-[junk, b_filtered]=sample_hp_filter(b,1600);
+[junk, b_filtered]=sample_hp_filter(b',1600);
 
 verbatim;
 total_std_one_shock_filtered_sim=std([y_filtered c_filtered k_filtered a_filtered h_filtered b_filtered]);

tests/moments/test_AR1_spectral_density.mod

@@ -20,6 +20,7 @@ options_.SpectralDensity.trigger=1;
 options_.bandpass.indicator=0;
 
 stoch_simul(order=1,nofunctions,hp_filter=0,irf=0,periods=1000000,filtered_theoretical_moments_grid=2048);
+send_endogenous_variables_to_workspace;
 
 white_noise_sample=white_noise;
 

tests/particle/first_spec.mod

@@ -12,7 +12,7 @@ var ca = 0.01^2;
 end;
 
 stoch_simul(order=3,periods=200, irf=0);
-
+send_endogenous_variables_to_workspace;
 save('my_data.mat','q','ca');
 
 estimation(datafile='my_data.mat',order=2,mode_compute=0,mh_replic=0,filter_algorithm=sis,nonlinear_filter_initialization=2

tests/particle/first_spec_MCMC.mod

@@ -12,6 +12,7 @@ var ca = 0.01^2;
 end;
 
 stoch_simul(order=3,periods=200, irf=0);
+send_endogenous_variables_to_workspace;
 
 save('my_data_MCMC.mat','ca','b');
 

tests/particle/first_spec_xfail_0.mod

@@ -10,6 +10,7 @@ var nnu = 0.03^2;
 end;
 
 stoch_simul(order=3,periods=200, irf=0, nomoments, nofunctions);
+send_endogenous_variables_to_workspace;
 
 save('my_data.mat','q','ca');
 

tests/particle/first_spec_xfail_1.mod

@@ -11,6 +11,7 @@ var q = 0.01^2;
 end;
 
 stoch_simul(order=3,periods=200, irf=0);
+send_endogenous_variables_to_workspace;
 
 save('my_data.mat','q','ca');