Storage of results: use subfolder

Ref. #1758

doc/manual/source/running-dynare.rst

@@ -548,7 +548,8 @@ by the ``dynare`` command.
     results in the workspace available for further processing. More
     details are given under the relevant computing tasks. The
     ``M_``,``oo_``, and ``options_`` structures are saved in a file
-    called ``FILENAME_results.mat``. If they exist, ``estim_params_``,
+    called ``FILENAME_results.mat`` located in the ``MODFILENAME/Output`` folder.
+    If they exist, ``estim_params_``,
     ``bayestopt_``, ``dataset_``, ``oo_recursive_`` and
     ``estimation_info`` are saved in the same file. Note that Matlab 
     by default only allows ``.mat``-files up to 2GB. You can lift this 

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

@@ -3890,7 +3890,8 @@ Computing the stochastic solution
        requested (i.e. ``periods`` :math:`>` 0). Note that if this
        option is greater than 1, the additional series will not be
        used for computing the empirical moments but will simply be
-       saved in binary form to the file ``FILENAME_simul``. Default:
+       saved in binary form to the file ``FILENAME_simul`` in the 
+       ``FILENAME/Output``-folder. Default:
        ``1``.
 
     .. option:: solve_algo = INTEGER
@@ -5297,7 +5298,8 @@ block decomposition of the model (see :opt:`block`).
        achieve an acceptance rate of
        :ref:`AcceptanceRateTarget<art>`. The resulting scale parameter
        will be saved into a file named
-       ``MODEL_FILENAME_mh_scale.mat.`` This file can be loaded in
+       ``MODEL_FILENAME_mh_scale.mat`` in the ``FILENAME/Output``-folder.
+       This file can be loaded in
        subsequent runs via the ``posterior_sampler_options`` option
        :ref:`scale_file <scale-file>`. Both ``mode_compute=6`` and
        ``scale_file`` will overwrite any value specified in
@@ -5378,7 +5380,8 @@ block decomposition of the model (see :opt:`block`).
        Name of the file containing previous value for the mode. When
        computing the mode, Dynare stores the mode (``xparam1``) and
        the hessian (``hh``, only if ``cova_compute=1``) in a file
-       called ``MODEL_FILENAME_mode.mat``. After a successful run of
+       called ``MODEL_FILENAME_mode.mat`` in the ``FILENAME/Output``-folder. 
+       After a successful run of
        the estimation command, the ``mode_file`` will be disabled to
        prevent other function calls from implicitly using an updated
        ``mode-file``. Thus, if the mod-file contains subsequent

matlab/compute_mh_covariance_matrix.m

@@ -78,4 +78,4 @@ hh = inv(posterior_covariance);
 fval = posterior_kernel_at_the_mode;
 parameter_names = bayestopt_.name;
 
-save([M_.fname '_mh_mode.mat'],'xparam1','hh','fval','parameter_names');
\ No newline at end of file
+save([M_.dname filesep 'Output' filesep M_.fname '_mh_mode.mat'],'xparam1','hh','fval','parameter_names');
\ No newline at end of file

matlab/dynare_estimation.m

@@ -101,7 +101,7 @@ if nnobs>1 || nfirstobs > 1
         end
         dynare_estimation_1(var_list,M_.dname);
         if isequal(i,1) && options_.mode_compute ~= 0
-            options_.mode_file = [M_.fname '_mode'];
+            options_.mode_file = [M_.dname filesep 'Output' filesep M_.fname '_mode'];
         end
         if options_.recursive_estimation_restart
             for j=1:options_.recursive_estimation_restart

matlab/dynare_estimation_1.m

@@ -31,6 +31,10 @@ function dynare_estimation_1(var_list_,dname)
 
 global M_ options_ oo_ estim_params_ bayestopt_ dataset_ dataset_info
 
+if ~exist([M_.dname filesep 'Output'],'dir')
+    mkdir(M_.dname,'Output');
+end
+
 if isempty(estim_params_)
     mode_compute_o = options_.mode_compute;
     mh_replic_o = options_.mh_replic;
@@ -206,7 +210,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         newratflag = new_rat_hess_info.newratflag;
         new_rat_hess_info = new_rat_hess_info.new_rat_hess_info;
     elseif isnumeric(options_.mode_compute) && options_.mode_compute==6 %save scaling factor
-        save([M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
+        save([M_.dname filesep 'Output' filesep M_.fname '_optimal_mh_scale_parameter.mat'],'Scale');
         options_.mh_jscale = Scale;
         bayestopt_.jscale(:) = options_.mh_jscale;
     end
@@ -260,9 +264,9 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
     end
     parameter_names = bayestopt_.name;
     if options_.cova_compute || options_.mode_compute==5 || options_.mode_compute==6
-        save([M_.fname '_mode.mat'],'xparam1','hh','parameter_names','fval');
+        save([M_.dname filesep 'Output' filesep M_.fname '_mode.mat'],'xparam1','hh','parameter_names','fval');
     else
-        save([M_.fname '_mode.mat'],'xparam1','parameter_names','fval');
+        save([M_.dname filesep 'Output' filesep M_.fname '_mode.mat'],'xparam1','parameter_names','fval');
     end
 end
 
@@ -375,7 +379,7 @@ end
 
 if np > 0
     pindx = estim_params_.param_vals(:,1);
-    save([M_.fname '_params.mat'],'pindx');
+    save([M_.dname filesep 'Output' filesep M_.fname '_params.mat'],'pindx');
 end
 
 switch options_.MCMC_jumping_covariance
@@ -472,7 +476,7 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
     else
         %get stored results if required
         if options_.load_mh_file && options_.load_results_after_load_mh
-            oo_load_mh=load([M_.fname '_results'],'oo_');
+            oo_load_mh=load([M_.dname filesep 'Output' filesep M_.fname '_results'],'oo_');
         end
         if ~options_.nodiagnostic
             if (options_.mh_replic>0 || (options_.load_mh_file && ~options_.load_results_after_load_mh))
@@ -778,7 +782,7 @@ end
 
 if np > 0
     pindx = estim_params_.param_vals(:,1);
-    save([M_.fname '_pindx.mat'] ,'pindx');
+    save([M_.dname filesep 'Output' filesep M_.fname '_pindx.mat'] ,'pindx');
 end
 
 %reset qz_criterium

matlab/gsa/filt_mc_.m

@@ -92,11 +92,11 @@ if ~isempty(options_.mode_file)
     load(options_.mode_file,'xparam1')
 end
 if options_.opt_gsa.ppost
-    c=load([fname_,'_mean.mat'],'xparam1');
+    c=load([M_.dname filesep 'Output' filesep fname_,'_mean.mat'],'xparam1');
     xparam1_mean=c.xparam1;
     clear c
-elseif ~isempty(options_.mode_file) && exist([fname_,'_mean.mat'])==2
-    c=load([fname_,'_mean.mat'],'xparam1');
+elseif ~isempty(options_.mode_file) && exist([M_.dname filesep 'Output' filesep fname_,'_mean.mat'])==2
+    c=load([M_.dname filesep 'Output' filesep fname_,'_mean.mat'],'xparam1');
     xparam1_mean=c.xparam1;
     clear c
 end

matlab/marginal_density.m

@@ -66,7 +66,7 @@ end
 % (usefull if the user wants to perform some computations using
 % the posterior mean instead of the posterior mode ==> ).
 parameter_names = bayestopt_.name;
-save([M_.fname '_mean.mat'],'xparam1','hh','parameter_names','SIGMA');
+save([M_.dname filesep 'Output' filesep M_.fname '_mean.mat'],'xparam1','hh','parameter_names','SIGMA');
 
 fprintf('Estimation::marginal density: I''m computing the posterior log marginal density (modified harmonic mean)... ');
 logdetSIGMA = log(det(SIGMA));

matlab/model_comparison.m

@@ -81,9 +81,9 @@ for i=1:NumberOfModels
         mstruct.oo_ = oo;
     else
         if strcmpi(ModelNames{i}(end-3:end),'.mod') || strcmpi(ModelNames{i}(end-3:end),'.dyn')
-            mstruct = load([ModelNames{i}(1:end-4) '_results.mat' ],'oo_');
+            mstruct = load([ModelNames{i}(1:end-4) filesep 'Output' ModelNames{i}(1:end-4) '_results.mat' ],'oo_');
         else
-            mstruct = load([ModelNames{i} '_results.mat' ],'oo_');
+            mstruct = load([ModelNames{i} filesep 'Output' filesep ModelNames{i} '_results.mat' ],'oo_');
         end
     end
     try

matlab/partial_information/dr1_PI.m

@@ -154,7 +154,7 @@ else
 end
 
 if options_.debug
-    save([M_.fname '_debug.mat'],'jacobia_')
+    save([M_.dname filesep 'Output' filesep M_.fname '_debug.mat'],'jacobia_')
 end
 
 dr=set_state_space(dr,M_,options_);

matlab/simult.m

@@ -66,7 +66,7 @@ order = DynareOptions.order;
 replic = DynareOptions.simul_replic;
 
 if replic > 1
-    fname = [DynareModel.fname,'_simul'];
+    fname = [DynareModel.dname filesep 'Output' DynareModel.fname,'_simul'];
     fh = fopen(fname,'w+');
 end
 

matlab/stochastic_solvers.m

@@ -128,7 +128,7 @@ elseif local_order == 2
         if ~isempty(infrow)
             fprintf('\nSTOCHASTIC_SOLVER: The Hessian of the dynamic model contains Inf.\n')
             fprintf('STOCHASTIC_SOLVER: Try running model_diagnostics to find the source of the problem.\n')
-            save([M_.fname '_debug.mat'],'hessian1')
+            save([M_.dname filesep 'Output' filesep M_.fname '_debug.mat'],'hessian1')
         end
     end
     if ~isempty(infrow)
@@ -140,7 +140,7 @@ elseif local_order == 2
         if ~isempty(nanrow)
             fprintf('\nSTOCHASTIC_SOLVER: The Hessian of the dynamic model contains NaN.\n')
             fprintf('STOCHASTIC_SOLVER: Try running model_diagnostics to find the source of the problem.\n')
-            save([M_.fname '_debug.mat'],'hessian1')
+            save([M_.dname filesep 'Output' filesep M_.fname '_debug.mat'],'hessian1')
         end
     end
     if ~isempty(nanrow)
@@ -155,7 +155,7 @@ if options_.debug
     if ~isempty(infrow)
         fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains Inf. The problem is associated with:\n\n')
         display_problematic_vars_Jacobian(infrow,infcol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
-        save([M_.fname '_debug.mat'],'jacobia_')
+        save([M_.dname filesep 'Output' filesep M_.fname '_debug.mat'],'jacobia_')
     end
 end
 
@@ -184,7 +184,7 @@ if options_.debug
     if ~isempty(nanrow)
         fprintf('\nSTOCHASTIC_SOLVER: The Jacobian of the dynamic model contains NaN. The problem is associated with:\n\n')
         display_problematic_vars_Jacobian(nanrow,nancol,M_,dr.ys,'dynamic','STOCHASTIC_SOLVER: ')
-        save([M_.fname '_debug.mat'],'jacobia_')
+        save([M_.dname filesep 'Output' filesep M_.fname '_debug.mat'],'jacobia_')
     end
 end
 

preprocessor @ bb19d987

-Subproject commit 5cfe6303e26fcaeff204e6d2ca3988b169621f46
+Subproject commit bb19d98712f2599380dfc704f98b33531d7414de

tests/AIM/fs2000_b1L1L_AIM.mod

@@ -92,7 +92,7 @@ steady(nocheck);
  
 stoch_simul(aim_solver, order=1, irf=0);
  
-benchmark = load('fs2000_b1L1L_results');
+benchmark = load(['fs2000_b1L1L' filesep 'Output' filesep 'fs2000_b1L1L_results']);
 threshold = 1e-8;
  
 if max(max(abs(benchmark.oo_.dr.ghx-oo_.dr.ghx) > threshold));

tests/AIM/fs2000x10L9_L_AIM.mod

@@ -76,7 +76,7 @@ check;
 
 stoch_simul(aim_solver, order=1,irf=0);
  
-benchmark = load('fs2000x10L9_L_results');
+benchmark = load(['fs2000x10L9_L' filesep 'Output' filesep 'fs2000x10L9_L_results']);
 threshold = 1e-8;
  
 if max(max(abs(benchmark.oo_.dr.ghx-oo_.dr.ghx) > threshold));

tests/AIM/fs2000x10_L9_L_AIM.mod

@@ -57,7 +57,7 @@ steady;
 
 stoch_simul(aim_solver, order=1,irf=0);
 
-benchmark = load('fs2000x10_L9_L_results');
+benchmark = load(['fs2000x10_L9_L' filesep 'Output' filesep 'fs2000x10_L9_L_results']);
 threshold = 1e-8;
  
 if max(max(abs(benchmark.oo_.dr.ghx-oo_.dr.ghx) > threshold));

tests/AIM/ls2003_2L0L_AIM.mod

@@ -45,7 +45,7 @@ end;
 
 stoch_simul(aim_solver, order=1,irf=0);
  
-benchmark = load('ls2003_2L0L_results');
+benchmark = load(['ls2003_2L0L' filesep 'Output' filesep 'ls2003_2L0L_results']);
 threshold = 1e-8;
  
 if max(max(abs(benchmark.oo_.dr.ghx-oo_.dr.ghx) > threshold));

tests/AIM/ls2003_2L2L_AIM.mod

@@ -43,7 +43,7 @@ end;
 
 stoch_simul(aim_solver, order=1,irf=0);
  
-benchmark = load('ls2003_2L2L_results');
+benchmark = load(['ls2003_2L2L' filesep 'Output' filesep 'ls2003_2L2L_results']);
 threshold = 1e-8;
  
 if max(max(abs(benchmark.oo_.dr.ghx-oo_.dr.ghx) > threshold));

tests/Makefile.am

@@ -1412,13 +1412,6 @@ clean-local:
 	rm -f $(patsubst %.trs, %.json, $(O_TRS_FILES))
 	rm -f $(patsubst %.trs, %.json, $(O_XFAIL_TRS_FILES))
 
-	rm -f $(patsubst %.mod, %_results.mat, $(MODFILES))
-	rm -f $(patsubst %.mod, %_mode.mat, $(MODFILES))
-	rm -f $(patsubst %.mod, %_mh_mode.mat, $(MODFILES))
-	rm -f $(patsubst %.mod, %_mean.mat, $(MODFILES))
-	rm -f $(patsubst %.mod, %_pindx.mat, $(MODFILES))
-	rm -f $(patsubst %.mod, %_params.mat, $(MODFILES))
-	rm -f $(patsubst %.mod, %_simul, $(MODFILES))
 	rm -f $(patsubst %.mod, %.log, $(MODFILES))
 
 	rm -rf $(patsubst %.mod, %, $(MODFILES))

tests/analytic_derivatives/fs2000_analytic_derivation.mod

@@ -78,22 +78,22 @@ options_.solve_tolf = 1e-12;
 
 estimation(order=1,mode_compute=9,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0,prior_trunc=0);
 if (isoctave && user_has_octave_forge_package('optim', '1.6')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
-    estimation(order=1,mode_compute=1,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0
+    estimation(order=1,mode_compute=1,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0
     %,optim = ('DerivativeCheck', 'on','FiniteDifferenceType','central')
     );
-    estimation(order=1,mode_compute=3,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
-    estimation(order=1,mode_compute=101,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);    
+    estimation(order=1,mode_compute=3,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
+    estimation(order=1,mode_compute=101,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);    
 end
-estimation(order=1,mode_compute=5,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
-estimation(order=1,mode_compute=4,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
-estimation(order=1,mode_compute=4,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
+estimation(order=1,mode_compute=5,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
+estimation(order=1,mode_compute=4,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
+estimation(order=1,mode_compute=4,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
 options_.debug=1;
-estimation(order=1,mode_compute=0,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
+estimation(order=1,mode_compute=0,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
 fval_ML_1=oo_.likelihood_at_initial_parameters;
-estimation(order=1,mode_compute=0,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
+estimation(order=1,mode_compute=0,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
 fval_ML_2=oo_.likelihood_at_initial_parameters;
 options_.analytic_derivation=0;
-estimation(order=1,mode_compute=0,mode_file=fs2000_analytic_derivation_mode,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
+estimation(order=1,mode_compute=0,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
 fval_ML_3=oo_.likelihood_at_initial_parameters;
 
 if abs(fval_ML_1-fval_ML_2)>1e-5 || abs(fval_ML_1-fval_ML_3)>1e-5
@@ -111,22 +111,22 @@ end;
 
 estimation(order=1,mode_compute=9,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0,prior_trunc=0);
 if (isoctave && user_has_octave_forge_package('optim', '1.6')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
-    estimation(order=1,mode_compute=1,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0
+    estimation(order=1,mode_compute=1,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0
     %,optim = ('DerivativeCheck', 'on','FiniteDifferenceType','central')
     );
-    estimation(order=1,mode_compute=3,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
-    estimation(order=1,mode_compute=101,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);    
+    estimation(order=1,mode_compute=3,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
+    estimation(order=1,mode_compute=101,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);    
 end
-estimation(order=1,mode_compute=5,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
-estimation(order=1,mode_compute=4,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
-estimation(order=1,mode_compute=4,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
+estimation(order=1,mode_compute=5,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
+estimation(order=1,mode_compute=4,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
+estimation(order=1,mode_compute=4,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,mh_nblocks=2,mh_jscale=0.8,plot_priors=0);
 options_.debug=1;
-estimation(order=1,mode_compute=0,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
+estimation(order=1,mode_compute=0,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
 fval_Bayes_1=oo_.likelihood_at_initial_parameters;
-estimation(order=1,mode_compute=0,mode_file=fs2000_analytic_derivation_mode,analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
+estimation(order=1,mode_compute=0,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',analytic_derivation,kalman_algo=2,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
 fval_Bayes_2=oo_.likelihood_at_initial_parameters;
 options_.analytic_derivation=0;
-estimation(order=1,mode_compute=0,mode_file=fs2000_analytic_derivation_mode,kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
+estimation(order=1,mode_compute=0,mode_file='fs2000_analytic_derivation/Output/fs2000_analytic_derivation_mode',kalman_algo=1,datafile=my_data,nobs=192,mh_replic=0,plot_priors=0);
 fval_Bayes_3=oo_.likelihood_at_initial_parameters;
 
 if abs(fval_Bayes_1-fval_Bayes_2)>1e-5 || abs(fval_Bayes_1-fval_Bayes_3)>1e-5

tests/block_bytecode/lola_solve_one_boundary_mfs1.mod

@@ -5,7 +5,7 @@
 @#define mfs = 1
 @#include "lola_common.inc"
 
-mfs0=load('lola_solve_one_boundary_results');
+mfs0=load(['lola_solve_one_boundary' filesep 'Output' filesep 'lola_solve_one_boundary_results']);
 
 if max(max(oo_.endo_simul-mfs0.oo_.endo_simul)) > options_.dynatol.x
    error('Inconsistency with mfs=0')