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testsuite: use silent_optimizer option to not clutter meson log-file

kalman-mex
Johannes Pfeifer 2023-09-28 11:25:48 +02:00
parent f2abdb6ec8
commit 776c247b9b
148 changed files with 219 additions and 216 deletions
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4
tests/TeX/fs2000_corr_ME.mod
View File

@ -154,7 +154,7 @@ stderr gy_obs, 1;
corr gp_obs, gy_obs,0;
end;
estimation(order=1,mode_compute=4,datafile='../fs2000/fsdat_simul',brooks_gelman_plotrows=4, mode_check,smoother,filter_covariance,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20,contemporaneous_correlation) m P c e W R k d y gy_obs;
estimation(order=1,mode_compute=4,silent_optimizer,datafile='../fs2000/fsdat_simul',brooks_gelman_plotrows=4, mode_check,smoother,filter_covariance,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20,contemporaneous_correlation) m P c e W R k d y gy_obs;
@ -172,7 +172,7 @@ end;
write_latex_prior_table;
estimation(mode_compute=8,order=1,datafile='../fs2000/fsdat_simul',mode_check,smoother,filter_decomposition,mh_replic=4000, mh_nblocks=1, mh_jscale=0.8,forecast = 8,bayesian_irf,filtered_vars,filter_step_ahead=[1,3],irf=20,
estimation(mode_compute=8,silent_optimizer,order=1,datafile='../fs2000/fsdat_simul',mode_check,smoother,filter_decomposition,mh_replic=4000, mh_nblocks=1, mh_jscale=0.8,forecast = 8,bayesian_irf,filtered_vars,filter_step_ahead=[1,3],irf=20,
moments_varendo,contemporaneous_correlation,conditional_variance_decomposition=[1 2 4],smoothed_state_uncertainty,raftery_lewis_diagnostics) m P c e W R k d y gy_obs;
trace_plot(options_,M_,estim_params_,'PosteriorDensity',1);

20
tests/analytic_derivatives/fs2000_analytic_derivation.mod
View File

@ -76,7 +76,7 @@ varobs gp_obs gy_obs;
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);
estimation(order=1,mode_compute=9,silent_optimizer,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/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')
@ -85,10 +85,10 @@ if (isoctave && user_has_octave_forge_package('optim', '1.6')) || (~isoctave &&
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
if ~isoctave % This estimation randomly fails on Octave
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=5,silent_optimizer,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);
end
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);
estimation(order=1,mode_compute=4,silent_optimizer,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,silent_optimizer,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/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;
@ -111,19 +111,19 @@ stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
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);
estimation(order=1,mode_compute=9,silent_optimizer,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/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/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);
estimation(order=1,mode_compute=3,silent_optimizer,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,silent_optimizer,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
if ~isoctave % This estimation randomly fails on Octave
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=5,silent_optimizer,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);
end
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);
estimation(order=1,mode_compute=4,silent_optimizer,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,silent_optimizer,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/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;

2
tests/arima/mod1a.mod
View File

@ -25,4 +25,4 @@ end;
varobs dx dy;
check;
estimation(datafile=data1,nobs=1000,mh_replic=2000,mh_jscale=1.2);
estimation(datafile=data1,silent_optimizer,nobs=1000,mh_replic=2000,mh_jscale=1.2);

2
tests/arima/mod1b.mod
View File

@ -28,4 +28,4 @@ stderr e_y,INV_GAMMA_PDF,0.01,inf;
end;
varobs x y;
estimation(datafile=data1,nobs=1000,mh_replic=0,mh_jscale=0.8,diffuse_filter);
estimation(datafile=data1,silent_optimizer,nobs=1000,mh_replic=0,mh_jscale=0.8,diffuse_filter);

2
tests/arima/mod1c.mod
View File

@ -30,4 +30,4 @@ stderr y,INV_GAMMA_PDF,0.01,inf;
end;
varobs x y;
estimation(datafile=data1,nobs=1000,mh_replic=2000,lik_init=2,mh_jscale=1.2);
estimation(datafile=data1,silent_optimizer,nobs=1000,mh_replic=2000,lik_init=2,mh_jscale=1.2);

2
tests/arima/mod2a.mod
View File

@ -36,4 +36,4 @@ end;
varobs dx dy;
estimation(datafile=data2,nobs=100,mh_replic=0,diffuse_filter);
estimation(datafile=data2,silent_optimizer,nobs=100,mh_replic=0,diffuse_filter);

2
tests/arima/mod2b.mod
View File

@ -36,4 +36,4 @@ end;
varobs x y;
estimation(datafile=data2,nobs=100,mh_replic=0,diffuse_filter);
estimation(datafile=data2,silent_optimizer,nobs=100,mh_replic=0,diffuse_filter);

2
tests/arima/mod2c.mod
View File

@ -34,4 +34,4 @@ end;
varobs dx dy;
estimation(datafile=data2,nobs=100,mh_replic=0);
estimation(datafile=data2,silent_optimizer,nobs=100,mh_replic=0);

12
tests/bgp/solow-1/solow.mod
View File

@ -66,11 +66,13 @@ verbatim;
GK(i) = y(12);
EG(i) = y(2);
end
% Display the progress
percentDone = 100 * i / MC;
msg = sprintf('Percent done: %3.1f', percentDone);
fprintf([reverseStr, msg]);
reverseStr = repmat(sprintf('\b'), 1, length(msg));
if mod(i,100)==0
% Display the progress
percentDone = 100 * i / MC;
msg = sprintf('Percent done: %3.1f', percentDone);
fprintf([reverseStr, msg]);
reverseStr = repmat(sprintf('\b'), 1, length(msg));
end
end
fprintf('\n');
% Compute the physical capital stock over output ratio along the BGP as

2
tests/conditional_forecasts/2/fs2000_est.mod
View File

@ -107,7 +107,7 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
// Metropolis replications are too few, this is only for testing purpose
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=1,mh_jscale=0.8);
estimation(order=1,datafile=fsdat_simul,silent_optimizer,nobs=192,loglinear,mh_replic=2000,mh_nblocks=1,mh_jscale=0.8);
conditional_forecast_paths;
var gy_obs;

2
tests/data/mod1a.mod
View File

@ -20,4 +20,4 @@ end;
varobs dx dy;
check;
estimation(datafile='test.xlsx',nobs=1000,mh_replic=2000,mh_jscale=1.3);
estimation(datafile='test.xlsx',nobs=1000,mh_replic=2000,mh_jscale=1.3,silent_optimizer);

4
tests/dates/dseries_interact.mod
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@ -41,9 +41,9 @@ varobs log_nn;
%reading Excel sheet from column A on creates quarterly dseries starting in
%1950
estimation(first_obs=2,datafile='data_uav.xlsx', xls_sheet=Tabelle1, xls_range=a1:b54, mh_replic=2, mh_nblocks=1, mh_jscale=1.1, mh_drop=0.8, plot_priors=0, smoother) log_nn nn hh ;
estimation(first_obs=2,datafile='data_uav.xlsx', xls_sheet=Tabelle1, xls_range=a1:b54, silent_optimizer,mh_replic=2, mh_nblocks=1, mh_jscale=1.1, mh_drop=0.8, plot_priors=0, smoother) log_nn nn hh ;
shock_decomposition( parameter_set=posterior_median ) nn hh;
%reading Excel sheet from column B on creates annual dseries starting with 1
estimation(first_obs=2,datafile='data_uav.xlsx', xls_sheet=Tabelle1, xls_range=b1:b54, mh_replic=2, mh_nblocks=1, mh_jscale=1.1, mh_drop=0.8, plot_priors=0, smoother) log_nn nn hh ;
estimation(first_obs=2,datafile='data_uav.xlsx', xls_sheet=Tabelle1, xls_range=b1:b54, silent_optimizer, mh_replic=2, mh_nblocks=1, mh_jscale=1.1, mh_drop=0.8, plot_priors=0, smoother) log_nn nn hh ;
shock_decomposition( parameter_set=posterior_median ) nn hh;

2
tests/dates/fs2000.mod
View File

@ -88,5 +88,5 @@ data(series=ts, first_obs=1950Q3, last_obs=2000Q3);
disp('First date is $1950Q3') // disp('First date is 1950Q3'), without the $ symbol, would trigger an error because of the substitution of 1950Q3 by dates('1950Q3')
// Run the estimation. Note that we do not have a datafile option, because of the data command used above.
estimation(order=1, loglinear, mh_replic=0);
estimation(order=1, loglinear, mh_replic=0, silent_optimizer);

2
tests/discretionary_policy/dennis_1_estim.mod
View File

@ -36,7 +36,7 @@ estimated_params;
end;
options_.plot_priors=0;
estimation(order = 1, datafile = dennis_simul, mh_replic = 2000, mh_nblocks=1,smoother,bayesian_irf,moments_varendo, conditional_variance_decomposition=[1,2]) y i pi pi_c q;
estimation(order = 1, datafile = dennis_simul, mh_replic = 2000, silent_optimizer,mh_nblocks=1,smoother,bayesian_irf,moments_varendo, conditional_variance_decomposition=[1,2]) y i pi pi_c q;
if max(abs(oo_.posterior.optimization.mode - [1; 0.3433])) > 0.025
error('Posterior mode too far from true parameter values');

2
tests/dsge-var/dsgevar_forward_calibrated_lambda.mod
View File

@ -80,4 +80,4 @@ varobs pie r rw y;
** The Dashed lines are the first, fifth (ie the median) and ninth posterior deciles of the DSGE-VAR's IRFs, the bold dark curve is the
** posterior median of the DSGE's IRfs and the shaded surface covers the space between the first and ninth posterior deciles of the DSGE's IRFs.
*/
estimation(datafile=datarabanal_hybrid,first_obs=50,mh_nblocks = 1,nobs=90,dsge_var=.8,optim=('NumgradAlgorithm',3),mode_compute=4,mh_replic=2000,bayesian_irf);
estimation(datafile=datarabanal_hybrid,silent_optimizer,first_obs=50,mh_nblocks = 1,nobs=90,dsge_var=.8,optim=('NumgradAlgorithm',3),mode_compute=4,mh_replic=2000,bayesian_irf);

2
tests/dsge-var/dsgevar_forward_estimated_lambda.mod
View File

@ -85,4 +85,4 @@ varobs pie r rw y;
** posterior median of the DSGE's IRfs and the shaded surface covers the space between the first and ninth posterior deciles of the DSGE's IRFs.
*/
estimation(datafile=datarabanal_hybrid,first_obs=50,mh_nblocks = 1,nobs=90,dsge_var,mode_compute=4,optim=('NumgradAlgorithm',3),mh_replic=2000,bayesian_irf);
estimation(datafile=datarabanal_hybrid,silent_optimizer,first_obs=50,mh_nblocks = 1,nobs=90,dsge_var,mode_compute=4,optim=('NumgradAlgorithm',3),mh_replic=2000,bayesian_irf);

2
tests/estimation/MH_recover/fs2000_recover.mod
View File

@ -3,7 +3,7 @@
@#include "fs2000.common.inc"
options_.MaxNumberOfBytes=1000*11*8/2;
estimation(order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=1000, mh_nblocks=2, mh_jscale=0.8);
estimation(order=1, datafile='../fsdat_simul',nobs=192, silent_optimizer,loglinear, mh_replic=1000, mh_nblocks=2, mh_jscale=0.8);
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh1_blck1.mat'],[M_.dname '_mh1_blck1.mat'])
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh2_blck2.mat'],[M_.dname '_mh2_blck2.mat'])
delete([M_.dname filesep 'metropolis' filesep M_.dname '_mh2_blck2.mat'])

2
tests/estimation/MH_recover/fs2000_recover_2.mod
View File

@ -4,7 +4,7 @@
@#include "fs2000.common.inc"
options_.MaxNumberOfBytes=2000*11*8/4;
estimation(order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=999, mh_nblocks=2, mh_jscale=0.8);
estimation(order=1, datafile='../fsdat_simul',nobs=192, silent_optimizer,loglinear, mh_replic=999, mh_nblocks=2, mh_jscale=0.8);
estimation(order=1,mode_compute=0,mode_file='fs2000_recover_2/Output/fs2000_recover_2_mode', datafile='../fsdat_simul',nobs=192, loglinear, load_mh_file,mh_replic=1002, mh_nblocks=2, mh_jscale=0.8);
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh1_blck1.mat'],[M_.dname '_mh1_blck1.mat'])
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh3_blck2.mat'],[M_.dname '_mh3_blck2.mat'])

2
tests/estimation/MH_recover/fs2000_recover_3.mod
View File

@ -4,7 +4,7 @@
@#include "fs2000.common.inc"
options_.MaxNumberOfBytes=2000*11*8/4;
estimation(order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=1000, mh_nblocks=2, mh_jscale=0.8);
estimation(order=1, datafile='../fsdat_simul',nobs=192, silent_optimizer,loglinear, mh_replic=1000, mh_nblocks=2, mh_jscale=0.8);
estimation(order=1,mode_compute=0,mode_file='fs2000_recover_3/Output/fs2000_recover_3_mode', datafile='../fsdat_simul',nobs=192, loglinear, load_mh_file,mh_replic=1000, mh_nblocks=2, mh_jscale=0.8);
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh1_blck1.mat'],[M_.dname '_mh1_blck1.mat'])
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh3_blck2.mat'],[M_.dname '_mh3_blck2.mat'])

2
tests/estimation/MH_recover/fs2000_recover_tarb.mod
View File

@ -3,7 +3,7 @@
@#include "fs2000.common.inc"
options_.MaxNumberOfBytes=10*11*8/2;
estimation(posterior_sampling_method='tailored_random_block_metropolis_hastings',order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=10, mh_nblocks=2, mh_jscale=0.8);
estimation(posterior_sampling_method='tailored_random_block_metropolis_hastings',silent_optimizer,order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=10, mh_nblocks=2, mh_jscale=0.8);
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh1_blck1.mat'],[M_.dname '_mh1_blck1.mat'])
copyfile([M_.dname filesep 'metropolis' filesep M_.dname '_mh2_blck2.mat'],[M_.dname '_mh2_blck2.mat'])
delete([M_.dname filesep 'metropolis' filesep M_.dname '_mh2_blck2.mat'])

2
tests/estimation/conditional-likelihood/1/fs2000_estimation_conditional.mod
View File

@ -81,7 +81,7 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
tic
estimation(conditional_likelihood,order=1,datafile='../../fsdat_simul',nobs=192,mode_compute=4,loglinear,mh_replic=5000,mh_nblocks=2,mh_jscale=0.8);
estimation(conditional_likelihood,silent_optimizer,order=1,datafile='../../fsdat_simul',nobs=192,mode_compute=4,loglinear,mh_replic=5000,mh_nblocks=2,mh_jscale=0.8);
toc
exact_likelihood = load('fs2000_estimation_exact/Output/fs2000_estimation_exact_results.mat');

2
tests/estimation/conditional-likelihood/1/fs2000_estimation_exact.mod
View File

@ -81,5 +81,5 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
tic
estimation(order=1,datafile='../../fsdat_simul',nobs=192,mode_compute=4,loglinear,mh_replic=5000,mh_nblocks=2,mh_jscale=0.8);
estimation(order=1,datafile='../../fsdat_simul',nobs=192,silent_optimizer,mode_compute=4,loglinear,mh_replic=5000,mh_nblocks=2,mh_jscale=0.8);
toc

2
tests/estimation/fs2000.mod
View File

@ -82,7 +82,7 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=3000,mh_nblocks=1,mh_jscale=0.8,moments_varendo,selected_variables_only,contemporaneous_correlation,smoother,forecast=8,
estimation(order=1,silent_optimizer,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=3000,mh_nblocks=1,mh_jscale=0.8,moments_varendo,selected_variables_only,contemporaneous_correlation,smoother,forecast=8,
geweke_interval = [0.19 0.49],
taper_steps = [4 7 15],
raftery_lewis_diagnostics,

8
tests/estimation/fs2000_MCMC_jumping_covariance.mod
View File

@ -82,13 +82,13 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
options_.mode_compute=4;
options_.plot_priors=0;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.8,mcmc_jumping_covariance=hessian);
estimation(order=1,silent_optimizer,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.8,mcmc_jumping_covariance=hessian);
load('fs2000_MCMC_jumping_covariance/Output/fs2000_MCMC_jumping_covariance_mode','hh');
jumping_covariance=diag(diag(hh));
save('test_matrix.mat','jumping_covariance');
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.01,mcmc_jumping_covariance=prior_variance);
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.0001,mcmc_jumping_covariance=identity_matrix);
estimation(order=1,silent_optimizer,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.01,mcmc_jumping_covariance=prior_variance);
estimation(order=1,silent_optimizer,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.0001,mcmc_jumping_covariance=identity_matrix);
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.8,mcmc_jumping_covariance='test_matrix');
estimation(order=1,silent_optimizer,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=1000,mh_nblocks=1,mh_jscale=0.8,mcmc_jumping_covariance='test_matrix');

2
tests/estimation/fs2000_calibrated_covariance.mod
View File

@ -80,7 +80,7 @@ corr e_a, e_m, 0.5;
stderr gp_obs, 0.5;
end;
estimation(order=1,datafile=fsdat_simul,nobs=192, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=0.8,moments_varendo,consider_all_endogenous);
estimation(order=1,datafile=fsdat_simul,nobs=192,silent_optimizer, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=0.8,moments_varendo,consider_all_endogenous);
if isequal(M_.Sigma_e(2,1),5e-5) || isequal(M_.Sigma_e(1,2),5e-5)
error('Problem in overriding calibrated covariance of structural shocks by estimated correlation')

2
tests/estimation/fs2000_estimated_params_remove.mod
View File

@ -94,7 +94,7 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0) y m;
estimation(order=1,datafile=fsdat_simul,nobs=192,silent_optimizer,loglinear,mh_replic=0) y m;
if size(estim_params_.var_exo, 1) ~= 2 || size(estim_params_.param_vals, 1) ~= 7 ...
|| size(estim_params_.var_endo, 1) ~= 0 || size(estim_params_.corrn, 1) ~= 0 ...

2
tests/estimation/fs2000_fast.mod
View File

@ -85,4 +85,4 @@ options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=3000,
fast_kalman_filter,mh_nblocks=2,mh_jscale=0.8,moments_varendo,
selected_variables_only,contemporaneous_correlation,
smoother,forecast=8) y m;
smoother,forecast=8,silent_optimizer) y m;

2
tests/estimation/fs2000_initialize_from_calib.mod
View File

@ -100,4 +100,4 @@ del, 0.020000;
end;
options_.plot_priors=0;
estimation(order=1, datafile=fsdat_simul, nobs=192, loglinear, mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,prior_trunc=0);
estimation(order=1, datafile=fsdat_simul, silent_optimizer, nobs=192, loglinear, mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,prior_trunc=0);

2
tests/estimation/fs2000_model_comparison.mod
View File

@ -81,7 +81,7 @@ end;
varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=1,mh_jscale=0.8,tex);
estimation(order=1,datafile=fsdat_simul,silent_optimizer,nobs=192,loglinear,mh_replic=2000,mh_nblocks=1,mh_jscale=0.8,tex);
model_comparison fs2000(0.5) fs2000_calibrated_covariance(0.5);

2
tests/estimation/fs2000_with_weibull_prior.mod
View File

@ -81,4 +81,4 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,moments_varendo,consider_only_observed);
estimation(order=1,datafile=fsdat_simul,nobs=192,silent_optimizer,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,moments_varendo,consider_only_observed);

2
tests/estimation/heteroskedastic_shocks/fs2000_het.mod
View File

@ -27,7 +27,7 @@ heteroskedastic_shocks;
scales 0;
end;
estimation(order=1,datafile='../fsdat_simul',nobs=192,mode_compute=5,loglinear,mh_replic=0,smoother,filtered_vars,forecast=8,filter_step_ahead=[1:8],consider_all_endogenous,heteroskedastic_filter);
estimation(order=1,datafile='../fsdat_simul',nobs=192,silent_optimizer,mode_compute=5,loglinear,mh_replic=0,smoother,filtered_vars,forecast=8,filter_step_ahead=[1:8],consider_all_endogenous,heteroskedastic_filter);
@#define mode_file_name="'fs2000_het/Output/fs2000_het_mode'"
@#include "fs2000_het_check.inc"

2
tests/estimation/heteroskedastic_shocks/fs2000_het_corr.mod
View File

@ -31,7 +31,7 @@ heteroskedastic_shocks;
scales 0;
end;
estimation(order=1,datafile='../fsdat_simul',nobs=192,mode_compute=5,loglinear,mh_replic=0,smoother,filtered_vars,forecast=8,filter_step_ahead=[1:8],consider_all_endogenous,heteroskedastic_filter);
estimation(order=1,datafile='../fsdat_simul',nobs=192,mode_compute=5,silent_optimizer,loglinear,mh_replic=0,smoother,filtered_vars,forecast=8,filter_step_ahead=[1:8],consider_all_endogenous,heteroskedastic_filter);
@#define mode_file_name="'fs2000_het_corr/Output/fs2000_het_corr_mode'"
@#include "fs2000_het_check.inc"

2
tests/estimation/heteroskedastic_shocks/fs2000_het_sample_restriction.mod
View File

@ -27,7 +27,7 @@ heteroskedastic_shocks;
scales 0;
end;
estimation(order=1,datafile='../fsdat_simul',first_obs=10,nobs=182,mode_compute=5,loglinear,mh_replic=0,smoother,filtered_vars,forecast=8,filter_step_ahead=[1:8],consider_all_endogenous,heteroskedastic_filter);
estimation(order=1,datafile='../fsdat_simul',first_obs=10,nobs=182,silent_optimizer,mode_compute=5,loglinear,mh_replic=0,smoother,filtered_vars,forecast=8,filter_step_ahead=[1:8],consider_all_endogenous,heteroskedastic_filter);
if M_.heteroskedastic_shocks.Qscale(strmatch('e_a',M_.exo_names,'exact'),91)~=0 && M_.heteroskedastic_shocks.Qscale(strmatch('e_b',M_.exo_names,'exact'),91)~=0.01
error('first_obs is incorrectly handled.')

2
tests/estimation/independent_mh/fs2000_independent_mh.mod
View File

@ -82,5 +82,5 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile='../fsdat_simul',nobs=192,loglinear,mh_replic=3000,
estimation(order=1,datafile='../fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=3000,
mh_nblocks=1,posterior_sampling_method='independent_metropolis_hastings',mh_jscale=0.8) y m;

2
tests/estimation/ls2003_endog_prior_restrict_estimation.mod
View File

@ -81,5 +81,5 @@ y_obs,pie_obs(@{ilag}), -; //[ccf]
@#endfor
end;
estimation(datafile='../gsa/data_ca1.m',mode_check,first_obs=8,nobs=79,mh_nblocks=1,
estimation(datafile='../gsa/data_ca1.m',silent_optimizer,mode_check,first_obs=8,nobs=79,mh_nblocks=1,
prefilter=1,mh_jscale=0.0005,mh_replic=5000, mode_compute=4, mh_drop=0.6, bayesian_irf,mcmc_jumping_covariance='identity_matrix') R_obs y;

2
tests/estimation/method_of_moments/AFVRR/AFVRR_M0.mod
View File

@ -295,7 +295,7 @@ method_of_moments(
,'UseParallel' , 1
%,'Jacobian' , 'on'
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
%, analytic_standard_errors
, se_tolx=1e-10
);

2
tests/estimation/method_of_moments/AFVRR/AFVRR_MFB.mod
View File

@ -296,7 +296,7 @@ method_of_moments(
,'UseParallel' , 1
%,'Jacobian' , 'on'
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
%, analytic_standard_errors
, se_tolx=1e-10
);

2
tests/estimation/method_of_moments/AFVRR/AFVRR_MFB_RRA.mod
View File

@ -295,7 +295,7 @@ method_of_moments(
,'UseParallel' , 1
%,'Jacobian' , 'on'
) % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
%, analytic_standard_errors
, se_tolx=1e-10
);

2
tests/estimation/method_of_moments/AnScho/AnScho_MoM_common.inc
View File

@ -256,7 +256,7 @@ method_of_moments(
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
)
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation

2
tests/estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod
View File

@ -205,7 +205,7 @@ method_of_moments(
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
)
% , silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation

2
tests/estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod
View File

@ -192,7 +192,7 @@ end
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
% )
% , silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation

2
tests/estimation/method_of_moments/RBC/RBC_MoM_optimizer.mod
View File

@ -142,7 +142,7 @@ options_.solveopt.TolXConstraint=1e-3;
,'MaxFunEvals' , 1D3 % maximum number of function evaluations allowed, a positive integer
)
@#endif
%, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
);
@#if estimParams == 2 && optimizer == 13

2
tests/estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod
View File

@ -164,7 +164,7 @@ save('test_matrix.mat','weighting_matrix')
% ,'UseParallel' , 1 % when true (and supported by optimizer) solver estimates gradients in parallel (using Matlab/Octave's parallel toolbox)
% ,'Jacobian' , 'off' % when 'off' gradient-based solvers approximate Jacobian using finite differences; for GMM we can also pass the analytical Jacobian to gradient-based solvers by setting this 'on'
% )
% , silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
, silent_optimizer % run minimization of moments distance silently without displaying results or saving files in between
% Numerical algorithms options
% , aim_solver % Use AIM algorithm to compute perturbation approximation

2
tests/estimation/no_init_estimation_check_first_obs/fs2000_init_check.mod
View File

@ -71,4 +71,4 @@ end;
varobs gp_obs gy_obs k;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_mat,nobs=192,loglinear,mh_replic=0,use_univariate_filters_if_singularity_is_detected=0, smoother, consider_all_endogenous, no_init_estimation_check_first_obs);
estimation(order=1,datafile=fsdat_mat,nobs=192,silent_optimizer,loglinear,mh_replic=0,use_univariate_filters_if_singularity_is_detected=0, smoother, consider_all_endogenous, no_init_estimation_check_first_obs);

4
tests/estimation/slice/fs2000_slice.mod
View File

@ -80,11 +80,11 @@ end;
varobs gp_obs gy_obs;
//options_.posterior_sampling_method = 'slice';
estimation(order=1,datafile='../fsdat_simul',nobs=192,loglinear,mh_replic=50,mh_nblocks=2,mh_drop=0.2, //mode_compute=0,cova_compute=0,
estimation(order=1,datafile='../fsdat_simul',nobs=192,silent_optimizer,loglinear,mh_replic=50,mh_nblocks=2,mh_drop=0.2, //mode_compute=0,cova_compute=0,
posterior_sampling_method='slice'
);
// continue with rotated slice
estimation(order=1,datafile='../fsdat_simul',nobs=192,loglinear,mh_replic=100,mh_nblocks=2,mh_drop=0.5,load_mh_file,//mode_compute=0,
estimation(order=1,datafile='../fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=100,mh_nblocks=2,mh_drop=0.5,load_mh_file,//mode_compute=0,
posterior_sampling_method='slice',
posterior_sampler_options=('rotated',1,'use_mh_covariance_matrix',1)
);

4
tests/estimation/system_prior_restriction/Gali_2015.mod
View File

@ -160,7 +160,7 @@ title('Prior')
% Run estimation with 1 observation to show effect of _prior_restriction .m
% on independent prior
estimation(datafile='sim_data',mode_compute=5,mh_replic=2001,mh_nblocks=1,diffuse_filter,nobs=1,mh_jscale=0.8);
estimation(datafile='sim_data',silent_optimizer,mode_compute=5,mh_replic=2001,mh_nblocks=1,diffuse_filter,nobs=1,mh_jscale=0.8);
posterior_function(function='Gali_2015_PC_slope');
PC_slope_vec=cell2mat(oo_.posterior_function_results(:,1));
optimal_bandwidth = mh_optimal_bandwidth(PC_slope_vec,length(PC_slope_vec),0,'gaussian');
@ -172,7 +172,7 @@ title('Updated Prior')
% Run estimation with full observations
estimation(datafile='sim_data',mode_compute=5,mh_replic=2001,mh_nblocks=1,diffuse_filter,nobs=100,mh_jscale=0.8);
estimation(datafile='sim_data',silent_optimizer,mode_compute=5,mh_replic=2001,mh_nblocks=1,diffuse_filter,nobs=100,mh_jscale=0.8);
posterior_function(function='Gali_2015_PC_slope');
PC_slope_vec=cell2mat(oo_.posterior_function_results(:,1));

4
tests/estimation/t_proposal/fs2000_student.mod
View File

@ -114,9 +114,9 @@ end;
varobs gp_obs gy_obs;
estimation(order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=2002, mh_nblocks=2, mh_jscale=0.8,mode_compute=4,
estimation(order=1, datafile='../fsdat_simul',nobs=192, silent_optimizer,loglinear, mh_replic=2002, mh_nblocks=2, mh_jscale=0.8,mode_compute=4,
posterior_sampler_options=('proposal_distribution','rand_multivariate_student','student_degrees_of_freedom',5,'save_tmp_file',0));
estimation(order=1, datafile='../fsdat_simul',nobs=192, loglinear, mh_replic=30, mh_nblocks=1, mh_jscale=0.8,mode_compute=4,
estimation(order=1, datafile='../fsdat_simul',nobs=192, silent_optimizer,loglinear, mh_replic=30, mh_nblocks=1, mh_jscale=0.8,mode_compute=4,
posterior_sampling_method='tailored_random_block_metropolis_hastings',
posterior_sampler_options=('proposal_distribution','rand_multivariate_student','student_degrees_of_freedom',5,'save_tmp_file',0));

2
tests/estimation/tune_mh_jscale/fs2000.mod
View File

@ -19,7 +19,7 @@
@#include "fs2000.inc"
estimation(order=1, datafile='../fsdat_simul', nobs=192, loglinear, mh_replic=10000, mh_nblocks=1, mh_tune_jscale=0.33,mh_tune_guess=0.7,plot_priors=0);
estimation(order=1, datafile='../fsdat_simul', nobs=192,silent_optimizer, loglinear, mh_replic=10000, mh_nblocks=1, mh_tune_jscale=0.33,mh_tune_guess=0.7,plot_priors=0);
mhdata = load('fs2000/metropolis/fs2000_mh_history_0.mat');

2
tests/filter_step_ahead/fs2000_filter_step_ahead_ML.mod
View File

@ -108,7 +108,7 @@ end;
varobs gp_obs gy_obs;
estimation(order=1, datafile='../fs2000/fsdat_simul', nobs=192, loglinear, filter_step_ahead = [1 4 8 12], forecast=20,smoother,filtered_vars) m P c;
estimation(order=1, datafile='../fs2000/fsdat_simul', nobs=192, silent_optimizer, loglinear, filter_step_ahead = [1 4 8 12], forecast=20,smoother,filtered_vars) m P c;
/*

2
tests/filter_step_ahead/fs2000_filter_step_ahead_bayesian.mod
View File

@ -114,7 +114,7 @@ end;
varobs gp_obs gy_obs;
estimation(order=1, datafile='../fs2000/fsdat_simul', nobs=192, loglinear, mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,filter_step_ahead = [1 4 8 12], forecast=20,smoother,filtered_vars) m P c;
estimation(order=1, datafile='../fs2000/fsdat_simul', silent_optimizer,nobs=192, loglinear, mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,filter_step_ahead = [1 4 8 12], forecast=20,smoother,filtered_vars) m P c;
/*

2
tests/filter_step_ahead/trend_cycle_decomposition.mod
View File

@ -31,4 +31,4 @@ end;
varobs y;
estimation(datafile=trend_cycle_decomposition_data,nobs=82, mh_replic=2000, mode_compute=4, mh_nblocks=1, mh_jscale=0.3, filtered_vars, smoother, diffuse_filter) yp z;
estimation(datafile=trend_cycle_decomposition_data,nobs=82, silent_optimizer,mh_replic=2000, mode_compute=4, mh_nblocks=1, mh_jscale=0.3, filtered_vars, smoother, diffuse_filter) yp z;

2
tests/fs2000/fs2000.mod
View File

@ -72,5 +72,5 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,moments_varendo,consider_only_observed);
estimation(order=1,datafile=fsdat_simul,nobs=192,silent_optimizer,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,moments_varendo,consider_only_observed);
calib_smoother(parameter_set=posterior_mean) y;

2
tests/fs2000/fs2000_data.mod
View File

@ -86,4 +86,4 @@ set_time(1970Q3); // Interpreted as the first date available in the sample loade
data(file='fsdat_simul.m',first_obs=1971Q1, nobs=40);
estimation(order=1,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);
estimation(order=1,silent_optimizer,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);

2
tests/fs2000/fs2000_dseries_a.mod
View File

@ -84,4 +84,4 @@ options_.solve_tolf = 1e-12;
data(file=fsdat_simul_dseries,first_obs=1950Q3, nobs=192);
estimation(order=1,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);
estimation(order=1,silent_optimizer,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);

2
tests/fs2000/fs2000_dseries_b.mod
View File

@ -87,4 +87,4 @@ fsdataset = fsdataset(1950Q3:1950Q3+191);
data(series=fsdataset);
estimation(order=1,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);
estimation(order=1,silent_optimizer,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);

2
tests/fs2000/fs2000_missing_data.mod
View File

@ -80,7 +80,7 @@ end;
varobs gp_obs gy_obs;
estimation(order=1, datafile=fsdat_simul_missing_obs, nobs=192, loglinear, mh_replic=2000, mh_nblocks=2, mh_jscale=0.8);
estimation(order=1, datafile=fsdat_simul_missing_obs,silent_optimizer, nobs=192, loglinear, mh_replic=2000, mh_nblocks=2, mh_jscale=0.8);
/*

2
tests/fs2000/fs2000_particle.mod
View File

@ -83,4 +83,4 @@ options_.solve_tolf = 1e-12;
/* Not computing the mode because it is very expensive, just running a small MH */
estimation(order=2,mode_compute=7,datafile=fsdat_simul,nobs=192);
estimation(order=2,mode_compute=7,silent_optimizer,datafile=fsdat_simul,nobs=192);

2
tests/fs2000/fs2000_sd.mod
View File

@ -81,6 +81,6 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0);
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0,silent_optimizer);
shock_decomposition(parameter_set=posterior_mode) gp_obs, gy_obs;

2
tests/fs2000/fs2000a.mod
View File

@ -89,7 +89,7 @@ Y_obs (gam);
end;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,
mode_compute=4,mh_nblocks=2,mh_drop=0.45,mh_jscale=0.65,diffuse_filter);
mode_compute=4,silent_optimizer,mh_nblocks=2,mh_drop=0.45,mh_jscale=0.65,diffuse_filter);
//stoch_simul(order=1, periods=200);
//datatomfile('fsdat_simul2', {'gy_obs'; 'gp_obs'; 'Y_obs'; 'P_obs'});

2
tests/gradient/fs2000_numgrad_13.mod
View File

@ -79,4 +79,4 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile='../fs2000/fsdat_simul',nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',13));
estimation(order=1,datafile='../fs2000/fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',13));

2
tests/gradient/fs2000_numgrad_15.mod
View File

@ -79,4 +79,4 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile='../fs2000/fsdat_simul',nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',15));
estimation(order=1,datafile='../fs2000/fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',15));

2
tests/gradient/fs2000_numgrad_2.mod
View File

@ -79,4 +79,4 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile='../fs2000/fsdat_simul',nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',2));
estimation(order=1,datafile='../fs2000/fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',2));

2
tests/gradient/fs2000_numgrad_3.mod
View File

@ -79,4 +79,4 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile='../fs2000/fsdat_simul',nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',3));
estimation(order=1,datafile='../fs2000/fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',3));

2
tests/gradient/fs2000_numgrad_5.mod
View File

@ -79,4 +79,4 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile='../fs2000/fsdat_simul',nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',5));
estimation(order=1,datafile='../fs2000/fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=0,optim=('NumgradAlgorithm',5));

2
tests/gsa/ls2003.mod
View File

@ -146,7 +146,7 @@ disp('Press ENTER to continue'); pause(5);
// run this to generate posterior mode and Metropolis files if not yet done
estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=1,
prefilter=1,mh_jscale=0.5,mh_replic=5000, mode_compute=4, mh_drop=0.6, nodisplay,
prefilter=1,mh_jscale=0.5,mh_replic=5000,silent_optimizer, mode_compute=4, mh_drop=0.6, nodisplay,
bayesian_irf, filtered_vars, smoother) y_obs R_obs pie_obs dq de;

2
tests/kalman/block/fs2000.mod
View File

@ -81,4 +81,4 @@ varobs gp_obs gy_obs;
options_.solve_tolf = 1e-12;
estimation(order=1,datafile='../../fs2000/fsdat_simul',nobs=192,mh_replic=0,mh_nblocks=1,mh_jscale=0.8,consider_only_observed);
estimation(order=1,datafile='../../fs2000/fsdat_simul',silent_optimizer,nobs=192,mh_replic=0,mh_nblocks=1,mh_jscale=0.8,consider_only_observed);

2
tests/kalman/block/fs2000_missing_data.mod
View File

@ -80,4 +80,4 @@ end;
varobs gp_obs gy_obs;
estimation(order=1, datafile='../../fs2000/fsdat_simul_missing_obs', nobs=192, mh_replic=0, mh_nblocks=1, mh_jscale=0.8);
estimation(order=1, datafile='../../fs2000/fsdat_simul_missing_obs',silent_optimizer, nobs=192, mh_replic=0, mh_nblocks=1, mh_jscale=0.8);

2
tests/kalman/likelihood_from_dynare/fs2000_estimation_check.inc
View File

@ -1,6 +1,6 @@
%%default
options_.lik_init=1;
estimation(kalman_algo=0,mode_compute=4,order=1,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
estimation(kalman_algo=0,silent_optimizer,mode_compute=4,order=1,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
fval_algo_0=oo_.likelihood_at_initial_parameters;
%%Multivariate Kalman Filter
options_.lik_init=1;

2
tests/kalman/likelihood_from_dynare/fs2000ns_estimation_check.inc
View File

@ -1,5 +1,5 @@
%%get mode
estimation(diffuse_filter,kalman_algo=3,mode_compute=4,order=1,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
estimation(diffuse_filter,kalman_algo=3,silent_optimizer,mode_compute=4,order=1,datafile=@{data_file_name},smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y gy_obs;
fval_algo_0=oo_.likelihood_at_initial_parameters;
%%Diffuse Multivariate Kalman Filter

6
tests/kalman/lyapunov/fs2000_lyap.mod
View File

@ -120,17 +120,17 @@ options_.lyapunov_fp = 0;
options_.lyapunov_db = 0;
options_.lyapunov_srs = 0;
estimation(lyapunov=doubling,order=1,datafile='../../fs2000/fsdat_simul', nobs=192, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=0.8,nograph);
estimation(lyapunov=doubling,order=1,datafile='../../fs2000/fsdat_simul',silent_optimizer, nobs=192, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=0.8,nograph);
if (isoctave && user_has_octave_forge_package('control')) || (~isoctave && user_has_matlab_license('control_toolbox'))
options_.lyapunov_fp = 0;
options_.lyapunov_db = 0;
options_.lyapunov_srs = 0;
estimation(lyapunov=square_root_solver,order=1,datafile='../../fs2000/fsdat_simul', nobs=192, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=0.8,nograph);
estimation(lyapunov=square_root_solver,order=1,datafile='../../fs2000/fsdat_simul',silent_optimizer, nobs=192, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=0.8,nograph);
end
options_.lyapunov_fp = 0;
options_.lyapunov_db = 0;
options_.lyapunov_srs = 0;
estimation(lyapunov=fixed_point,order=1,datafile='../../fs2000/fsdat_simul', nobs=192, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=0.8,nograph);
estimation(lyapunov=fixed_point,order=1,datafile='../../fs2000/fsdat_simul',silent_optimizer, nobs=192, loglinear, mh_replic=0, mh_nblocks=1, mh_jscale=0.8,nograph);

2
tests/kalman_filter_smoother/algo1.mod
View File

@ -32,7 +32,7 @@ end;
varobs dw dx dy z;
estimation(datafile=data_algo,first_obs=1000,nobs=200,mh_replic=0,filtered_vars,smoothed_state_uncertainty);
estimation(datafile=data_algo,silent_optimizer,first_obs=1000,nobs=200,mh_replic=0,filtered_vars,smoothed_state_uncertainty);
//checking smoother consistency
X = oo_.SmoothedVariables;

2
tests/kalman_filter_smoother/algo3.mod
View File

@ -35,7 +35,7 @@ end;
varobs w x y;
estimation(datafile=data_algo,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter,filtered_vars,smoothed_state_uncertainty);
estimation(datafile=data_algo,first_obs=1000,silent_optimizer,nobs=200,mh_replic=0,diffuse_filter,filtered_vars,smoothed_state_uncertainty);
//checking smoother consistency
X = oo_.SmoothedVariables;

2
tests/kalman_filter_smoother/algo4a.mod
View File

@ -33,7 +33,7 @@ end;
varobs dw dx y z;
estimation(datafile=data_algo,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter);
estimation(datafile=data_algo,silent_optimizer,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter);
//estimation(datafile=data_algo,first_obs=1000,nobs=200,mh_replic=0,mode_compute=0,mode_file='algo3/Output/algo3_mode',diffuse_filter);
//checking smoother consistency

2
tests/kalman_filter_smoother/algo4b.mod
View File

@ -33,7 +33,7 @@ end;
varobs dw dx y z;
estimation(datafile=data_algo,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter,smoothed_state_uncertainty);
estimation(datafile=data_algo,first_obs=1000,silent_optimizer,nobs=200,mh_replic=0,diffuse_filter,smoothed_state_uncertainty);
//estimation(datafile=data_algo,first_obs=1000,nobs=200,mh_replic=0,mode_compute=0,mode_file='algo3/Output/algo3_mode',diffuse_filter);
//checking smoother consistency

2
tests/kalman_filter_smoother/algoH1.mod
View File

@ -34,7 +34,7 @@ end;
varobs dw dx dy z;
estimation(datafile=data_algo,first_obs=1000,nobs=200,mh_replic=0,filtered_vars,smoothed_state_uncertainty);
estimation(datafile=data_algo,first_obs=1000,silent_optimizer,nobs=200,mh_replic=0,filtered_vars,smoothed_state_uncertainty);
//checking smoother consistency
X = oo_.SmoothedVariables;

2
tests/kalman_filter_smoother/algoH3.mod
View File

@ -37,7 +37,7 @@ end;
varobs w x y;
estimation(datafile=data_algo,first_obs=1000,nobs=200,mh_replic=0,diffuse_filter,smoothed_state_uncertainty);
estimation(datafile=data_algo,first_obs=1000,silent_optimizer,nobs=200,mh_replic=0,diffuse_filter,smoothed_state_uncertainty);
stoch_simul(irf=0);

2
tests/kalman_filter_smoother/check_variable_dimensions/fs2000.mod
View File

@ -115,7 +115,7 @@ end;
varobs gp_obs gy_obs;
estimation(order=1,datafile='../fsdat_simul', nobs=192, loglinear, mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,forecast=8,smoother,filtered_vars,filter_step_ahead=[1:2],filter_decomposition) m P c e W R k d y gy_obs;
estimation(order=1,datafile='../fsdat_simul', silent_optimizer,nobs=192, loglinear, mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,forecast=8,smoother,filtered_vars,filter_step_ahead=[1:2],filter_decomposition) m P c e W R k d y gy_obs;
if size(oo_.PointForecast.deciles.gy_obs,1)~=9
error('Number of deciles must be 9')

2
tests/kalman_filter_smoother/check_variable_dimensions/fs2000_ML.mod
View File

@ -116,7 +116,7 @@ corr e_m, e_a, 0;
stderr gp_obs, 0.01;
end;
options_.prior_trunc=0;
estimation(order=1,datafile='../fsdat_simul', nobs=192, loglinear, moments_varendo,conditional_variance_decomposition=[1,3],forecast=8,smoother,filter_covariance,filtered_vars,filter_step_ahead=[1,2,4],filter_decomposition,selected_variables_only) m P c e W R k d y gy_obs gp_obs;
estimation(order=1,datafile='../fsdat_simul', nobs=192, silent_optimizer,loglinear, moments_varendo,conditional_variance_decomposition=[1,3],forecast=8,smoother,filter_covariance,filtered_vars,filter_step_ahead=[1,2,4],filter_decomposition,selected_variables_only) m P c e W R k d y gy_obs gp_obs;
if size(oo_.FilteredVariablesKStepAhead,3)~=(options_.nobs+max(options_.filter_step_ahead)) || ...

2
tests/kalman_filter_smoother/compare_results_simulation/fs2000.mod
View File

@ -114,7 +114,7 @@ end;
varobs gp_obs gy_obs;
estimation(order=1,datafile=fsdat_simul_logged,consider_all_endogenous,nobs=192,mh_replic=2000, mh_nblocks=1,smoother, mh_jscale=0.8);
estimation(order=1,datafile=fsdat_simul_logged, silent_optimizer,consider_all_endogenous,nobs=192,mh_replic=2000, mh_nblocks=1,smoother, mh_jscale=0.8);
ex_=[];
for shock_iter=1:M_.exo_nbr

2
tests/kalman_filter_smoother/compare_results_simulation/fs2000_ML.mod
View File

@ -114,7 +114,7 @@ stderr e_a, 0.035449;
stderr e_m, 0.008862;
end;
estimation(order=1,datafile='fsdat_simul_logged', nobs=192, forecast=8,smoother,filtered_vars,filter_step_ahead=[1,2,4],filter_decomposition,selected_variables_only) m P c e W R k d y gy_obs;
estimation(order=1,datafile='fsdat_simul_logged', nobs=192, forecast=8, silent_optimizer, smoother,filtered_vars,filter_step_ahead=[1,2,4],filter_decomposition,selected_variables_only) m P c e W R k d y gy_obs;
% write shock matrix
ex_=[];

4
tests/kalman_filter_smoother/compare_results_simulation/fs2000_ML_loglinear.mod
View File

@ -113,8 +113,8 @@ del, 0.02;
stderr e_a, 0.035449;
stderr e_m, 0.008862;
end;
estimation(order=1,datafile='../fsdat_simul',loglinear, nobs=192, forecast=8,smoother,filtered_vars,filter_step_ahead=[1,2,4],filter_decomposition,selected_variables_only) m P c e W R k d y gy_obs;
warning('off','MATLAB:nearlySingularMatrix')
estimation(order=1,datafile='../fsdat_simul',silent_optimizer,loglinear, nobs=192, forecast=8,smoother,filtered_vars,filter_step_ahead=[1,2,4],filter_decomposition,selected_variables_only) m P c e W R k d y gy_obs;
% write shock matrix
ex_=[];

2
tests/kalman_filter_smoother/compare_results_simulation/fs2000_loglinear.mod
View File

@ -131,7 +131,7 @@ end;
varobs gp_obs gy_obs;
estimation(order=1, datafile='../fsdat_simul', nobs=192, loglinear, mh_replic=2000, mh_nblocks=1,smoother, mh_jscale=0.8,consider_all_endogenous);
estimation(order=1, datafile='../fsdat_simul', nobs=192, silent_optimizer, loglinear, mh_replic=2000, mh_nblocks=1,smoother, mh_jscale=0.8,consider_all_endogenous);
ex_=[];
for shock_iter=1:M_.exo_nbr

2
tests/kalman_filter_smoother/fs2000.mod
View File

@ -79,5 +79,5 @@ end;
varobs gp_obs gy_obs;
//estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,mode_check);
//estimation(order=1,datafile=fsdat_simul,nobs=192,silent_optimizer,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8,mode_check);
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0,mode_compute=0);

2
tests/kalman_filter_smoother/fs2000a.mod
View File

@ -89,6 +89,6 @@ P_obs (log(mst)-gam);
Y_obs (gam);
end;
estimation(order=1,datafile=fsdat_simul,nobs=192,loglinear,mh_replic=0,
estimation(order=1,datafile=fsdat_simul,nobs=192,silent_optimizer,loglinear,mh_replic=0,
mode_compute=4,mh_nblocks=2,mh_drop=0.45,mh_jscale=0.65,diffuse_filter,smoother,forecast=10) P_obs gp_obs gy_obs;

2
tests/kalman_initial_state/fs2000_kalman_initial.mod
View File

@ -22,4 +22,4 @@ P(0)=2.5258;
m(0) = mst;
end;
estimation(order=1,datafile='../fs2000/fsdat_simul',nobs=192,loglinear,mh_replic=2001,mh_nblocks=1,mh_jscale=0.8,moments_varendo,consider_only_observed,smoother);
estimation(order=1,datafile='../fs2000/fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=2001,mh_nblocks=1,mh_jscale=0.8,moments_varendo,consider_only_observed,smoother);

2
tests/kalman_initial_state/fs2000_kalman_initial_2_lag.mod
View File

@ -84,4 +84,4 @@ P(0)=2.5258;
m(-1) = mst;
end;
estimation(order=1,datafile='../fs2000/fsdat_simul',nobs=192,loglinear,mh_replic=2001,mh_nblocks=1,mh_jscale=0.8,moments_varendo,consider_only_observed,smoother);
estimation(order=1,datafile='../fs2000/fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=2001,mh_nblocks=1,mh_jscale=0.8,moments_varendo,consider_only_observed,smoother);

5
tests/measurement_errors/fs2000_corr_me_ml_mcmc/fs2000_corr_ME.mod
View File

@ -111,8 +111,9 @@ stderr gp_obs, 1;
stderr gy_obs, 1;
corr gp_obs, gy_obs,0;
end;
warning('off','MATLAB:nearlySingularMatrix')
estimation(order=1,datafile=fsdat_simul,mode_check,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20,tex) m P c e W R k d y gy_obs;
estimation(order=1,datafile=fsdat_simul,silent_optimizer,prior_trunc=0,mode_check,smoother,filter_decomposition,forecast = 8,filtered_vars,filter_step_ahead=[1,3],irf=20,tex) m P c e W R k d y gy_obs;
@ -128,6 +129,6 @@ stderr gp_obs, inv_gamma_pdf, 0.001, inf;
//corr gp_obs, gy_obs,normal_pdf, 0, 0.2;
end;
estimation(mode_compute=5,order=1,datafile=fsdat_simul,mode_check,smoother,filter_decomposition,mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,forecast = 8,bayesian_irf,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y;
estimation(mode_compute=5,silent_optimizer,order=1,datafile=fsdat_simul,mode_check,smoother,filter_decomposition,mh_replic=2000, mh_nblocks=1, mh_jscale=0.8,forecast = 8,bayesian_irf,filtered_vars,filter_step_ahead=[1,3],irf=20) m P c e W R k d y;
shock_decomposition y W R;
//identification(advanced=1,max_dim_cova_group=3,prior_mc=250);

4
tests/minimal_state_space_system/sw_minimal.mod
View File

@ -422,8 +422,8 @@ Sigmay_full = SS.C*Sigmax_full*SS.C' + SS.D*M_.Sigma_e*SS.D';
Sigmax_min = lyapunov_symm(minSS.A, minSS.B*M_.Sigma_e*minSS.B', options_.lyapunov_fixed_point_tol, options_.qz_criterium, options_.lyapunov_complex_threshold, 1, options_.debug);
Sigmay_min = minSS.C*Sigmax_min*minSS.C' + minSS.D*M_.Sigma_e*minSS.D';
([Sigmay_full(:) - Sigmay_min(:)]')
sqrt(([diag(Sigmay_full), diag(Sigmay_min)]'))
([Sigmay_full(:) - Sigmay_min(:)]');
sqrt(([diag(Sigmay_full), diag(Sigmay_min)]'));
dx = norm( Sigmay_full - Sigmay_min, Inf);
if dx > 3e-8
error(sprintf('something wrong with minimal state space computations, as numerical error is %d',dx))

4
tests/moments/fs2000_post_moments.mod
View File

@ -122,7 +122,7 @@ end;
varobs gp_obs gy_obs;
estimation(order=1,mode_compute=5, datafile='../fs2000/fsdat_simul.m', nobs=192, loglinear, mh_replic=20, mh_nblocks=1, mh_jscale=0.8,moments_varendo,
estimation(order=1,mode_compute=5,silent_optimizer, datafile='../fs2000/fsdat_simul.m', nobs=192, loglinear, mh_replic=20, mh_nblocks=1, mh_jscale=0.8,moments_varendo,
conditional_variance_decomposition=[2,2000],consider_all_endogenous,sub_draws=2);
stoch_simul(order=1,conditional_variance_decomposition=[2,2000],noprint,nograph);
@ -194,7 +194,7 @@ stderr e_m, inv_gamma_pdf, 0.008862, inf;
stderr gp_obs, inv_gamma_pdf, 0.003, inf;
end;
estimation(order=1,mode_compute=5, datafile='../fs2000/fsdat_simul.m', nobs=192, loglinear, mh_replic=20, mh_nblocks=1, mh_jscale=0.8,moments_varendo,
estimation(order=1,mode_compute=5,silent_optimizer, datafile='../fs2000/fsdat_simul.m', nobs=192, loglinear, mh_replic=20, mh_nblocks=1, mh_jscale=0.8,moments_varendo,
conditional_variance_decomposition=[2,2000],consider_all_endogenous,sub_draws=2);
stoch_simul(order=1,conditional_variance_decomposition=[2,2000],noprint,nograph);

2
tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_no_prefilt_first_obs_MC.mod
View File

@ -4,7 +4,7 @@ addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_loglin_no_prefilt_first_obs_MC_Exp_AR1_trend_data_with_constant',mh_replic=400,
mode_compute=4,first_obs=1000,loglinear,smoother,forecast=100,prefilter=0,
mode_compute=4,silent_optimizer,first_obs=1000,loglinear,smoother,forecast=100,prefilter=0,
mcmc_jumping_covariance='Trend_loglin_no_prefilt_first_obs_MC_MCMC_jump_covar',
filtered_vars, filter_step_ahead = [1,2,4],
mh_nblocks=1,mh_jscale=0.3,no_posterior_kernel_density) P_obs Y_obs junk2;

2
tests/observation_trends_and_prefiltering/MCMC/Trend_loglin_prefilt_first_obs_MC.mod
View File

@ -4,7 +4,7 @@ addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_loglin_prefilt_first_obs_MC_Exp_AR1_trend_data_with_constant',mh_replic=400,
mode_compute=4,first_obs=1000,loglinear,smoother,forecast=100,prefilter=1,
mode_compute=4,silent_optimizer,first_obs=1000,loglinear,smoother,forecast=100,prefilter=1,
mcmc_jumping_covariance='Trend_loglin_prefilt_first_obs_MC_MCMC_jump_covar_prefilter',
filtered_vars, filter_step_ahead = [1,2,4],
mh_nblocks=1,mh_jscale=1e-4,no_posterior_kernel_density) P_obs Y_obs junk2;

2
tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_no_prefilter_MC.mod
View File

@ -4,7 +4,7 @@ addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_loglinear_no_prefilter_MC_Exp_AR1_trend_data_with_constant',mh_replic=400,
mode_compute=4,first_obs=1,loglinear,diffuse_filter,smoother,forecast=100,prefilter=0,
mode_compute=4,silent_optimizer,first_obs=1,loglinear,diffuse_filter,smoother,forecast=100,prefilter=0,
mcmc_jumping_covariance='Trend_loglinear_no_prefilter_MC_MCMC_jump_covar',
filtered_vars, filter_step_ahead = [1,2,4],
mh_nblocks=1,mh_jscale=0.3) P_obs Y_obs junk2;

2
tests/observation_trends_and_prefiltering/MCMC/Trend_loglinear_prefilter_MC.mod
View File

@ -4,7 +4,7 @@ addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_loglinear_prefilter_MC_Exp_AR1_trend_data_with_constant',mh_replic=400,
mode_compute=4,first_obs=1,loglinear,smoother,forecast=100,prefilter=1,
mode_compute=4,silent_optimizer,first_obs=1,loglinear,smoother,forecast=100,prefilter=1,
mcmc_jumping_covariance='Trend_loglinear_prefilter_MC_MCMC_jump_covar_prefilter',
filtered_vars, filter_step_ahead = [1,2,4],
mh_nblocks=1,mh_jscale=1e-4) P_obs Y_obs junk2;

2
tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_MC.mod
View File

@ -3,7 +3,7 @@
addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_no_prefilter_MC_AR1_trend_data_with_constant',mh_replic=400,
estimation(order=1,datafile='Trend_no_prefilter_MC_AR1_trend_data_with_constant',mh_replic=400,silent_optimizer,
mode_compute=4,first_obs=1,smoother,mh_nblocks=1,mh_jscale=0.3,
filtered_vars, filter_step_ahead = [1,2,4],
mcmc_jumping_covariance='Trend_no_prefilter_MC_MCMC_jump_covar',forecast=100,prefilter=0) P_obs Y_obs junk2;

2
tests/observation_trends_and_prefiltering/MCMC/Trend_no_prefilter_first_obs_MC.mod
View File

@ -4,7 +4,7 @@ addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_no_prefilter_first_obs_MC_AR1_trend_data_with_constant',
mh_replic=400,mode_compute=4,first_obs=1000,smoother,forecast=100,prefilter=0,
mh_replic=400,mode_compute=4,silent_optimizer,first_obs=1000,smoother,forecast=100,prefilter=0,
mcmc_jumping_covariance='Trend_no_prefilter_first_obs_MC_MCMC_jump_covar',
filtered_vars, filter_step_ahead = [1,2,4],
mh_nblocks=1,mh_jscale=0.3,no_posterior_kernel_density) P_obs Y_obs junk2;

2
tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_MC.mod
View File

@ -3,7 +3,7 @@
addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_prefilter_MC_AR1_trend_data_with_constant',mh_replic=400,mode_compute=4,
estimation(order=1,datafile='Trend_prefilter_MC_AR1_trend_data_with_constant',mh_replic=400,mode_compute=4,silent_optimizer,
first_obs=1,smoother,prefilter=1,
mh_nblocks=1,mh_jscale=1e-4,
filtered_vars, filter_step_ahead = [1,2,4],

2
tests/observation_trends_and_prefiltering/MCMC/Trend_prefilter_first_obs_MC.mod
View File

@ -3,7 +3,7 @@
addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_prefilter_first_obs_MC_AR1_trend_data_with_constant',mh_replic=400,mode_compute=4,
estimation(order=1,datafile='Trend_prefilter_first_obs_MC_AR1_trend_data_with_constant',mh_replic=400,mode_compute=4,silent_optimizer,
first_obs=1000,smoother,prefilter=1,
mh_nblocks=1,mh_jscale=1e-4,
filtered_vars, filter_step_ahead = [1,2,4],

2
tests/observation_trends_and_prefiltering/ML/Trend_loglinear_no_prefilter.mod
View File

@ -3,7 +3,7 @@
addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_loglinear_no_prefilter_Exp_AR1_trend_data_with_constant',mh_replic=0,
estimation(order=1,datafile='Trend_loglinear_no_prefilter_Exp_AR1_trend_data_with_constant',mh_replic=0,silent_optimizer,
mode_compute=4,first_obs=1,
filtered_vars, filter_step_ahead = [1,2,4],
loglinear,smoother,forecast=100,prefilter=0) P_obs Y_obs junk2;

2
tests/observation_trends_and_prefiltering/ML/Trend_loglinear_no_prefilter_first_obs.mod
View File

@ -3,7 +3,7 @@
addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_loglinear_no_prefilter_first_obs_Exp_AR1_trend_data_with_constant',mh_replic=0,
estimation(order=1,datafile='Trend_loglinear_no_prefilter_first_obs_Exp_AR1_trend_data_with_constant',mh_replic=0,silent_optimizer,
mode_compute=4,first_obs=1000,
filtered_vars, filter_step_ahead = [1,2,4],
loglinear,smoother,forecast=100,prefilter=0) P_obs Y_obs junk2;

2
tests/observation_trends_and_prefiltering/ML/Trend_loglinear_prefilter.mod
View File

@ -3,7 +3,7 @@
addpath('..');
generate_trend_stationary_AR1(M_.fname);
estimation(order=1,datafile='Trend_loglinear_prefilter_Exp_AR1_trend_data_with_constant',mh_replic=0,mode_compute=4,
estimation(order=1,datafile='Trend_loglinear_prefilter_Exp_AR1_trend_data_with_constant',mh_replic=0,mode_compute=4,silent_optimizer,
first_obs=1,smoother,loglinear,
filtered_vars, filter_step_ahead = [1,2,4],
forecast=100,prefilter=1) P_obs Y_obs junk2;

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