Unverified Commit 440a0e46 authored by Johannes Pfeifer 's avatar Johannes Pfeifer Committed by Sébastien Villemot
Browse files

Storage of results: use subfolder

Ref. #1758
parent 263e97da
......@@ -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
......
......@@ -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
......
......@@ -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
......@@ -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
......
......@@ -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
......
......@@ -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
......
......@@ -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));
......
......@@ -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
......
......@@ -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_);
......
......@@ -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
......
......@@ -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
......
Subproject commit 5cfe6303e26fcaeff204e6d2ca3988b169621f46
Subproject commit bb19d98712f2599380dfc704f98b33531d7414de
......@@ -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));
......
......@@ -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));
......
......@@ -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));
......
......@@ -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));
......
......@@ -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));
......
......@@ -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))
......
......@@ -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
......
......@@ -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')
......
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