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method_of_moments: improved header

kalman-mex
Willi Mutschler 2023-09-04 15:29:58 +02:00
parent 3c0f3c1c24
commit f25188cf2f
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1 changed files with 98 additions and 70 deletions
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matlab/+mom/run.m
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@ -1,68 +1,84 @@
function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_) function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
%function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_) % function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_, M_, options_mom_)
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% This function performs a method of moments estimation with the following steps: % This function performs a method of moments estimation with the following steps:
% Step 0: Check if required structures and options exist % o Checking if required structures and options exist
% Step 1: - Prepare options_mom_ structure % o Preparing local options_mom_ structure
% - Carry over options from the preprocessor % o Checking the options and the compatibility of the settings
% - Initialize other options % o Initializations of variables, orderings and state space representation
% - Get variable orderings and state space representation % o Checks and transformations for matched moments structure
% Step 2: Checks and transformations for matched moments structure % o Checks and transformations for estimated parameters, priors, and bounds
% Step 3: Checks and transformations for estimated parameters, priors, and bounds % o Checks and transformations for data
% Step 4: Checks and transformations for data % o Checks for objective function at initial parameters
% Step 5: Checks for steady state at initial parameters % o GMM/SMM: iterated method of moments estimation
% Step 6: Checks for objective function at initial parameters % o GMM/SMM: J-Test and fit of moments%
% Step 7: Iterated method of moments estimation % o Display of results
% Step 8: J-Test % o Clean up
% Step 9: Clean up
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% This function is inspired by replication codes accompanied to the following papers: % This function is inspired by replication codes accompanied to the following papers:
% GMM/SMM:
% o Andreasen, Fernández-Villaverde, Rubio-Ramírez (2018): "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications", Review of Economic Studies, 85(1):1-49. % o Andreasen, Fernández-Villaverde, Rubio-Ramírez (2018): "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications", Review of Economic Studies, 85(1):1-49.
% o Born, Pfeifer (2014): "Risk Matters: Comment", American Economic Review, 104(12):4231-4239. % o Born, Pfeifer (2014): "Risk Matters: Comment", American Economic Review, 104(12):4231-4239.
% o Mutschler (2018): "Higher-order statistics for DSGE models", Econometrics and Statistics, 6:44-56. % o Mutschler (2018): "Higher-order statistics for DSGE models", Econometrics and Statistics, 6:44-56.
% ========================================================================= % =========================================================================
% INPUTS % INPUTS
% o bayestopt_: [structure] information about priors % o bayestopt_: [structure] information about priors
% o options_: [structure] information about global options % o options_: [structure] information about global options
% o oo_: [structure] storage for results % o oo_: [structure] storage for results
% o estim_params_: [structure] information about estimated parameters % o estim_params_: [structure] information about estimated parameters
% o M_: [structure] information about model with % o M_: [structure] information about model with
% o matched_moments: [cell] information about selected moments to match in estimation % o matched_moments: [cell] information about selected moments to match in GMM/SMM estimation
% vars: matched_moments{:,1}); % vars: matched_moments{:,1});
% lead/lags: matched_moments{:,2}; % lead/lags: matched_moments{:,2};
% powers: matched_moments{:,3}; % powers: matched_moments{:,3};
% o options_mom_: [structure] information about settings specified by the user % o options_mom_: [structure] information about settings specified by the user
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% OUTPUTS % OUTPUTS
% o oo_: [structure] storage for results (oo_) % o oo_: [structure] storage for results (oo_)
% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_) % o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
% o M_: [structure] updated information about model
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% This function is called by % This function is called by
% o driver.m % o driver.m
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% This function calls % This function calls
% o check_for_calibrated_covariances.m % o cellofchararraymaxlength
% o check_prior_bounds.m % o check_for_calibrated_covariances
% o do_parameter_initialization.m % o check_prior_bounds
% o dynare_minimize_objective.m % o check_prior_stderr_corr
% o evaluate_steady_state % o check_steady_state_changes_parameters
% o get_all_parameters.m % o check_varobs_are_endo_and_declared_once
% o get_matrix_entries_for_psd_check.m % o display_estimation_results_table
% o makedataset.m % o do_parameter_initialization
% o mom.check_plot.m % o dyn_latex_table
% o mom.data_moments.m % o dynare_minimize_objective
% o mom.objective_function.m % o dyntable
% o get_all_parameters
% o get_dynare_random_generator_state
% o get_matrix_entries_for_psd_check
% o M_.fname '_prior_restrictions'
% o makedataset
% o mom.check_plot
% o mom.default_option_mom_values
% o mom.get_data_moments
% o mom.matched_moments_block
% o mom.objective_function
% o mom.optimal_weighting_matrix % o mom.optimal_weighting_matrix
% o mom-standard_errors % o mom.print_info_on_estimation_settings
% o plot_priors.m % o mom.set_correct_bounds_for_stderr_corr
% o print_info.m % o mom.standard_errors
% o prior_bounds.m % o plot_priors
% o set_default_option.m % o prior_bounds
% o set_prior.m % o priordens
% o set_state_space.m % o print_info
% o set_all_parameters.m % o set_all_parameters
% o test_for_deep_parameters_calibration.m % o set_dynare_random_generator_state
% o set_prior
% o set_state_space
% o skipline
% o test_for_deep_parameters_calibration
% o warning_config
% =========================================================================
% Copyright © 2020-2023 Dynare Team % Copyright © 2020-2023 Dynare Team
% %
% This file is part of Dynare. % This file is part of Dynare.
@ -80,19 +96,31 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
% You should have received a copy of the GNU General Public License % You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>. % along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% ------------------------------------------------------------------------- % -------------------------------------------------------------------------
% Author(s): % Maintaining Author(s):
% o Willi Mutschler (willi@mutschler.eu) % o Willi Mutschler (willi@mutschler.eu)
% o Johannes Pfeifer (jpfeifer@uni-koeln.de) % o Johannes Pfeifer (johannes.pfeifer@unibw.de)
% ========================================================================= % =========================================================================
%% TO DO LIST % -------------------------------------------------------------------------
% - [ ] add IRF matching % TO DO LISTS
% - [ ] speed up pruned_state_space_system (by using doubling with old initial values, hardcoding zeros, other "tricks" used in e.g. nlma) % -------------------------------------------------------------------------
% - [ ] add option to use autocorrelations (we have useautocorr in identification toolbox already) % GENERAL
% - [ ] SMM with extended path % - document all options in manual
% - [ ] deal with measurement errors (once @wmutschl has implemented this in identification toolbox) % - document analytic_jacobian better
% - [ ] dirname option to save output to different directory not yet implemented % - make endogenous_prior_restrictions work
% - [ ] display scaled moments % - dirname option to save output to different directory not yet implemented
% - create test for prior restrictions file
% - add mode_file option
% - implement penalty objective
% - test optimizers
% GMM/SMM
% - speed up pruned_state_space_system (by using doubling with old initial values, hardcoding zeros, other "tricks" used in e.g. nlma)
% - add option to use autocorrelations (we have useautocorr in identification toolbox already)
% - SMM with extended path
% - deal with measurement errors (once @wmutschl has implemented this in identification toolbox)
% - display scaled moments
% - enable first moments despite prefilter
% - do "true" Bayesian GMM and SMM not only penalized
% The TeX option crashes MATLAB R2014a run with "-nodisplay" option % The TeX option crashes MATLAB R2014a run with "-nodisplay" option
% (as is done from the testsuite). % (as is done from the testsuite).
@ -823,8 +851,8 @@ for i = 1:length(optimizer_vec)
case 13 case 13
fprintf('\n %s=13): lsqnonlin',str); fprintf('\n %s=13): lsqnonlin',str);
otherwise otherwise
if ischar(optimizer_vec{i}) if ischar(options_mom_.optimizer_vec{i})
fprintf('\n %s=%s): user-defined',str,optimizer_vec{i}); fprintf('\n %s=%s): user-defined',str,options_mom_.optimizer_vec{i});
else else
error('method_of_moments: Unknown optimizer, please contact the developers ') error('method_of_moments: Unknown optimizer, please contact the developers ')
end end
@ -832,7 +860,7 @@ for i = 1:length(optimizer_vec)
if options_mom_.silent_optimizer if options_mom_.silent_optimizer
fprintf(' (silent)'); fprintf(' (silent)');
end end
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(optimizer_vec{i},analytic_jacobian_optimizers) if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(options_mom_.optimizer_vec{i},options_mom_.mom.analytic_jacobian_optimizers)
fprintf(' (using analytical Jacobian)'); fprintf(' (using analytical Jacobian)');
end end
end end
@ -900,26 +928,26 @@ for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
error('method_of_moments: Specified weighting_matrix is not positive definite. Check whether your model implies stochastic singularity.') error('method_of_moments: Specified weighting_matrix is not positive definite. Check whether your model implies stochastic singularity.')
end end
for optim_iter= 1:length(optimizer_vec) for optim_iter= 1:length(options_mom_.optimizer_vec)
options_mom_.current_optimizer = optimizer_vec{optim_iter}; options_mom_.current_optimizer = options_mom_.optimizer_vec{optim_iter};
if optimizer_vec{optim_iter}==0 if options_mom_.optimizer_vec{optim_iter}==0
xparam1=xparam0; %no minimization, evaluate objective at current values xparam1=xparam0; %no minimization, evaluate objective at current values
fval = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_); fval = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
else else
if optimizer_vec{optim_iter}==13 if options_mom_.optimizer_vec{optim_iter}==13
options_mom_.vector_output = true; options_mom_.mom.vector_output = true;
else else
options_mom_.vector_output = false; options_mom_.mom.vector_output = false;
end end
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(optimizer_vec{optim_iter},analytic_jacobian_optimizers) %do this only for gradient-based optimizers if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_jacobian && ismember(options_mom_.optimizer_vec{optim_iter},options_mom_.mom.analytic_jacobian_optimizers) %do this only for gradient-based optimizers
options_mom_.mom.compute_derivs = true; options_mom_.mom.compute_derivs = true;
else else
options_mom_.mom.compute_derivs = false; options_mom_.mom.compute_derivs = false;
end end
[xparam1, fval, exitflag] = dynare_minimize_objective(objective_function, xparam0, optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_laplace.name, bayestopt_laplace, [],... [xparam1, fval, exitflag] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_laplace.name, bayestopt_laplace, [],...
Bounds, oo_, estim_params_, M_, options_mom_); Bounds, oo_, estim_params_, M_, options_mom_);
if options_mom_.vector_output if options_mom_.mom.vector_output
fval = fval'*fval; fval = fval'*fval;
end end
end end
@ -929,7 +957,7 @@ for stage_iter=1:size(options_mom_.mom.weighting_matrix,1)
end end
xparam0=xparam1; xparam0=xparam1;
end end
options_mom_.vector_output = false; options_mom_.mom.vector_output = false;
% Update M_ and DynareResults (in particular to get oo_.mom.model_moments) % Update M_ and DynareResults (in particular to get oo_.mom.model_moments)
M_ = set_all_parameters(xparam1,estim_params_,M_); M_ = set_all_parameters(xparam1,estim_params_,M_);
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors